• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
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    • 专利摘要:
    METHOD FOR DETECTION OF STRUCTURAL ANOMALY BASED ON A REAL-TIME MODEL. The present invention relates to a system and methods for detecting structural vehicle anomaly based on a real-time model that are revealed. A real-time measurement corresponding to a location in a vehicle structure during a vehicle operation is received, and the real-time measurement is compared to expected operating data for the location to provide a modeling error signal. A statistical significance of the modeling error signal to provide an error significance is calculated, and a persistence of the error significance is determined. A structural anomaly is indicated if the persistence exceeds a persistence threshold value.
    公开号:BR102012023565B1
    申请号:R102012023565-0
    申请日:2012-09-18
    公开日:2021-01-12
    发明作者:Timothy A. Smith；James M. Urnes, Sr.；Eric Y. Reichenbach
    申请人:The Boeing Company；
    IPC主号:
    专利说明:

    [0001] [001] Modalities of the present disclosure generally concern the detection of structural anomaly. More particularly, modalities of the present disclosure concern the detection of structural anomaly in real time. Background
    [0002] [002] Vehicle or aircraft structures are typically subjected to a variety of exogenous forces throughout their operational lives; both expected operational forces and unexpected forces. Operational health of such structures can be adversely affected by an anomalous structural response to operational forces and unexpected forces. Operational forces such as changes in aerodynamic loading and unexpected forces such as gusts of wind can result in non-ideal structural conditions. summary
    [0003] [003] A system and methods for detecting structural vehicle anomaly based on a real-time model are revealed. A real-time measurement corresponding to a location in a vehicle structure during a vehicle operation is received, and the real-time measurement is compared to expected operating data for the location to provide a modeling error signal. A statistical significance of the modeling error signal to provide an error significance is calculated, and a persistence of the error significance is determined. A structural anomaly is indicated if the persistence exceeds a persistence threshold value.
    [0004] [004] In this way, a nominal model of a structural behavior of the vehicle is compared with a detected response. A statistical analysis of modeling errors provides an indication of anomalous structural behavior; indicating the structural anomaly for the vehicle structure. A control mechanism can be activated to compensate for the structural anomaly in response to the indication of the structural anomaly. Thus, the structural life of the vehicle is extended and the time between maintenance events is extended.
    [0005] [005] In one embodiment, a method for detecting structural vehicle anomaly based on a real-time model receives a real-time measurement corresponding to a location in a vehicle structure during a vehicle operation. The method further compares the real-time measurement to expected operating data for the location to provide a modeling error signal, and calculates a statistical significance of the modeling error signal to provide an error significance. The method additionally determines a persistence of the error significance, and indicates a structural anomaly, if the persistence exceeds a persistence threshold value.
    [0006] [006] In another embodiment, a structural anomaly detection system based on a real-time model comprises a structural anomaly detection module and an anomaly mitigation module. The structural anomaly detection module is operable to receive a real-time measurement corresponding to a location on a vehicle structure during vehicle operation, and to compare the real-time measurement to expected operating data for the location to provide a signal modeling error. The structural anomaly detection module is additionally operable to calculate a statistical significance of the modeling error signal to provide an error significance, and to determine a persistence of the error significance. The structural anomaly detection module is additionally operable to indicate a structural anomaly, if the persistence exceeds a persistence threshold value. The anomaly mitigation module is operable to activate a control mechanism to compensate for the structural anomaly, if the structural anomaly is indicated.
    [0007] [007] Also in another modality, a method to alleviate a structural anomaly obtains a modeling error signal from a structure, and assesses a probability that the modeling error signal is significantly distant from zero when computing an Alarm Probability False (Pfa) to provide an error significance. The method additionally introduces a unit signal into a first order filter when the error significance falls below a Pfa threshold value, and indicates a structural anomaly condition when a first order filter output is close enough to one.
    [0008] [008] In accordance with one aspect of the present disclosure, a method for detecting structural anomaly of vehicles based on a real-time model is provided, comprising: receiving a real-time measurement corresponding to a location in a vehicle structure during an operation of the vehicle; compare real-time measurement to expected operating data for the location to provide a modeling error signal; calculate a statistical significance of the modeling error signal to provide an error significance; determining a persistence of the significance of error; and indicate a structural anomaly if the persistence exceeds a persistence threshold value.
    [0009] [009] Advantageously the step of calculating the statistical significance of the modeling error signal further comprises: repetitively estimating an estimated mean and an estimated variance of the modeling error signal to determine whether the modeling error signal confirms anomaly indication structural; and assessing a probability that the modeling error signal is significantly distant from zero when computing a False Alarm Probability (Pfa) of the modeling error signal based on the estimated mean and estimated variance to provide the error significance.
    [0010] [0010] Advantageously, the step of determining the persistence of the error significance further comprises: introducing a unit signal in a first order filter when the error significance falls below a Pfa threshold value; compare a first order filter output to a value close to one; and indicating a structural malfunction condition when the first order filter output is close enough to one, where persistence is high.
    [0011] [0011] Preferably, the Pfa threshold value and a filter time constant of the first order filter are adjustable parameters that depend on a quality of the modeling error signal and tolerance for false positive indications.
    [0012] [0012] Advantageously the method additionally comprises activating a control mechanism to compensate for the structural anomaly, if the structural anomaly is indicated.
    [0013] [0013] Preferably, the step of activating the control mechanism comprises at least one element selected from the group consisting of: a control surface drive, a support surface drive, a flow control drive, memory alloy drive shape, activation by means of active structural materials and a change in propulsive power.
    [0014] [0014] Advantageously the method further comprises installing a plurality of measurement sensors in the operable vehicle structure to perform the measurement in real time.
    [0015] [0015] Advantageously, the method additionally comprises: reading a sensor signal during healthy operation of the vehicle; and formulating an expected signature signal response for healthy vehicle operation based on the sensor signal to provide expected operating data.
    [0016] [0016] Advantageously the method additionally comprises collecting a representative sensor signal during additional operation of the vehicle on a periodic basis to obtain the measurement in real time.
    [0017] [0017] Advantageously the structural anomaly comprises at least one element selected from the group consisting of: an in-flight operation, a stress from wind shear on a support surface, a stress from a fragment impact on a support surface, a tension caused by a gust of wind on a support surface, a vibration in a wing, a flutter in a wing, a fuselage flex, an excessive fuselage curvature, a propulsion system anomaly, an excessive linear displacement, a excessive angular displacement, structural fatigue, a control surface anomaly and a support surface anomaly.
    [0018] [0018] In accordance with an additional aspect of the present disclosure, a structural anomaly detection system based on a real-time model is provided, comprising: a structural anomaly detection module operable for: receiving a real-time measurement corresponding to a location on a vehicle structure during vehicle operation; compare real-time measurement to expected operating data for the location to provide a modeling error signal; calculate a statistical significance of the modeling error signal to provide an error significance; determining a persistence of the error significance if the error significance is below a selected Pfa threshold value; and indicate a structural anomaly, if persistence exceeds a persistence threshold value; and an operable anomaly mitigation module to activate a control mechanism to compensate for the structural anomaly, if the structural anomaly is indicated.
    [0019] [0019] Advantageously, the system additionally comprises: an operable mean / variance estimator to repetitively estimate an estimated mean and an estimated variance of the modeling error signal; an operable error function module to assess a probability that the modeling error signal is significantly distant from zero when computing a False Alarm Probability (Pfa) of the modeling error signal based on the estimated mean and the estimated variance for obtain the significance of error; and a smoother comprising a first order filter and operable to declare an anomaly condition when an output of the first order filter is close enough to one indicating that the error significance is persistently high.
    [0020] [0020] Preferably, the first order filter comprises the selected Pfa threshold value and a filter time constant which are adjustable parameters based on the quality of the modeling error signal and tolerance for false positive indications.
    [0021] [0021] Advantageously the vehicle is an airplane and the step of activating the control mechanism comprises at least one element selected from the group consisting of: a control surface drive, a support surface drive, a flow control drive, activation of alloys with shape memory, activation by means of active structural materials and a change in propulsive power.
    [0022] [0022] Advantageously the system additionally comprises a model formulation module of healthy structure operable to: read a sensor signal during healthy vehicle operation; store the sensor signal in a memory; formulate an expected signature signal response for healthy vehicle operation based on the sensor signal to obtain expected operating data; and provide the expected operating data for the structural anomaly detection module.
    [0023] [0023] Advantageously the system comprises a real-time measurement module operable to: measure a representative sensor signal during additional operation of the vehicle on a periodic basis to obtain the measurement in real time; and provide real-time measurement for the structural anomaly detection module.
    [0024] [0024] Advantageously the system comprises a plurality of sensors comprising at least one element selected from the group consisting of: a deformation sensor, a vibration sensor, a noise sensor, a temperature sensor and an optical sensor.
    [0025] [0025] Advantageously the structural anomaly comprises at least one element selected from the group consisting of: an in-flight operation, a stress from wind shear on a support surface, a stress from a fragment impact on a support surface, a tension caused by a gust of wind on a support surface, a vibration in a wing, a flicker in a wing, a fuselage flex, an excessive fuselage curvature, a propulsion system anomaly, an excessive linear displacement, excessive angular displacement, structural fatigue, a control surface anomaly and a support surface anomaly.
    [0026] [0026] In accordance with a further aspect of the present disclosure, a method is provided to alleviate a structural anomaly, comprising: obtaining an error of modeling a structure; evaluate a probability that the modeling error signal is significantly distant from zero when computing a False Alarm Probability (Pfa) to provide an error significance; introduce a unit signal in a first order filter when the error significance falls below a Pfa threshold value; and indicating a structural malfunction condition when a first order filter outlet is close enough to one.
    [0027] [0027] Advantageously the method comprises activating a control mechanism to compensate for the condition of structural anomaly, if the condition of structural anomaly is indicated.
    [0028] [0028] This summary is provided to introduce a selection of concepts in a simplified form, which are further described below in the detailed description. This summary is not intended to identify key features or essential features of the matter in question, nor is it intended to be used as an aid in determining the scope of the matter in question. Brief Description of Drawings
    [0029] [0029] A more complete understanding of the modalities of the present disclosure can be derived by referring to the detailed description and claims when considered in combination with the following figures, in which equal reference numbers refer to similar elements throughout the figures. Figures are provided to facilitate understanding of the disclosure without limiting the extent, scope, scale or applicability of the disclosure. The drawings are not necessarily made to scale.
    [0030] [0030] Figure 1 is an illustration of an airplane production flowchart and exemplary service methodology.
    [0031] [0031] Figure 2 is an illustration of an exemplary block diagram of an airplane.
    [0032] [0032] Figure 3 is an illustration of an exemplary plane showing a structural anomaly detection system according to a disclosure modality.
    [0033] [0033] Figure 4 is an illustration of an exemplary functional block diagram of a structural anomaly detection system according to a disclosure modality.
    [0034] [0034] Figure 5 is an illustration of an exemplary functional block diagram of a structural anomaly detection module according to a disclosure modality.
    [0035] [0035] Figure 6 is an illustration of an exemplary graph showing a Gaussian probability density function (pdf) showing an error function (erf) versus a modeling error signal according to a disclosure modality.
    [0036] [0036] Figure 7 is an illustration of a functional block diagram exemplary of a smoother according to a disclosure modality.
    [0037] [0037] Figure 8 is an illustration of an exemplary flowchart showing a model-based vehicle structural anomaly detection process according to a disclosure modality.
    [0038] [0038] Figure 9 is an illustration of an exemplary flow chart showing a process to alleviate a structural anomaly according to a disclosure modality. Detailed Description
    [0039] [0039] The following detailed description is exemplary in nature and is not intended to limit the disclosure or the application and uses of the disclosure modalities. Descriptions of specific techniques, applications and devices are provided as examples only. Modifications to the examples described in this document will be readily apparent to persons of ordinary skill in the art, and the general principles defined in this document can be applied to other examples and applications without departing from the spirit and scope of the disclosure. The present disclosure should be given scope consistent with the claims, and not limited to the examples described and shown in this document.
    [0040] [0040] Disclosure modalities can be described in this document in terms of components of functional and / or logic blocks and various processing steps. It should be noted that such block components can be realized using any number of hardware, software and / or firmware components configured to perform the specified functions. For the sake of brevity, conventional techniques and components related to control laws, control systems, measurement techniques, measurement sensors, strain gauges, data transmission, signaling, network control and other functional aspects of the systems (and the components operating systems) may not be described in detail in this document. Furthermore, those skilled in the art will understand which modalities of the present disclosure can be practiced in combination with a variety of hardware and software, and that the modalities described in this document are merely exemplary modalities of the disclosure.
    [0041] [0041] Disclosure modalities are described in this document in the context of a practical non-limiting application, that is, anomaly detection in an airplane structure. Disclosure modalities, however, are not limited to such an airplane structure, and the techniques described in this document can also be used in other applications. For example, but without limitation, modalities may apply to manned and unmanned land, air, sea and submerged vehicles, buildings, windmills and more.
    [0042] [0042] As would be apparent to a person of ordinary skill in the art after reading this description, the following are examples and modalities of the disclosure and are not limited to operate according to these examples. Other modalities can be used and structural changes can be made without departing from the scope of the exemplary modalities of the present disclosure.
    [0043] [0043] Referring more particularly to the drawings, disclosure modalities can be described in the context of an airplane manufacturing method and services 100 (method 100) as shown in figure 1 and an airplane 200 as shown in figure 2 During pre-production, exemplary method 100 may include specification and design 104 for aircraft 200 and procurement of material 106. During production, component manufacturing and subassembly 108 and system integration 110 for aircraft 200 take place. The airplane 200 can then pass certification and delivery 112 in order to be placed in service 114. While in service on a customer, the airplane 200 is scheduled for routine maintenance and service 116 (which may also include modification, reconfiguration , restoration, and so on).
    [0044] [0044] Each of the 100 method processes can be performed by a system integrator, an external entity and / or an operator (for example, a customer). For the purposes of this description, a system integrator may include without limitation any number of aircraft manufacturers and major system subcontractors; an external entity may include without limitation any number of vendors, subcontractors and suppliers; and an operator can be, without limitation, an airline, leasing company, military entity, service organization and more.
    [0045] [0045] As shown in figure 2, the airplane 200 produced by means of the exemplary method 100 can include an airplane structure 218 with a plurality of systems 220 and an interior 222. Examples of high level systems 220 include one or more of a propulsion system 224, an electrical system 226, a hydraulic system 228, an environmental system 230 and a structural anomaly detection system 232. Any number of other systems can also be included. Although an aerospace example is shown, the disclosure modalities can be applied to other industries.
    [0046] [0046] Apparatus and methods incorporated in this document can be used during any one or more of the stages of the production method and services 100. For example, components or subassemblies corresponding to the production process 108 can be constructed or manufactured in a similar way to that of the components or subassemblies produced while airplane 200 is in service. In addition, one or more apparatus modalities, method modalities or a combination thereof can be used during production stages 108 and 110, for example, by substantially speeding up assembly or reducing the cost of a 200 plane. similarly, one or more of the apparatus modalities, method modalities or a combination thereof can be used while the airplane 200 is in service, for example, and without limitation, for maintenance and services 116.
    [0047] [0047] Supplementary drive systems can be used to detect anomalies in a structure. In contrast, modalities of disclosure require a healthy model of airplane structural behavior as a function of flight condition and airplane status as input. The structural anomaly indication can be coupled with structural data measured in flight controls to limit maneuvers of a non-ideal airplane structure within a wrapper that maintains structural loads for the airplane at safe levels.
    [0048] [0048] Disclosure modalities provide a system and methods for detecting structural anomaly in real time of a structure such as an airplane during flight. In-flight anomaly detection may allow the use of flight controls that mitigate structural anomaly effects, preventing further anomaly propagation that can result in extensive aircraft repair. A structural anomaly indication can also provide information for maintenance personnel when indicating a need for structural assessment of the aircraft on the ground. This information can lengthen the required interval between structural assessments in the soil, and thus reduce cost.
    [0049] [0049] The expression real time refers to a signal that is being sent and received continuously, with little or no delay. The term near real time refers to a real time signal with substantially no significant delay time. The delay time can be a delay introduced, for example, but without limitation, by automated data processing or network transmission, between occurrences of an event, and more. In this document, the term real time refers to both real time and near real time.
    [0050] [0050] Figure 3 is an illustration of an exemplary aircraft 300 comprising a structural anomaly detection system 336 (system 336) for detecting structural anomaly of aircraft 300 in real time according to a disclosure modality. The plane 300 may comprise the structural anomaly detection system 336, a plurality of control surfaces and a plurality of support surfaces, and a plurality of measurement units (MUs).
    [0051] [0051] The structural anomaly detection system 336 is operable to detect structural anomaly of the aircraft 300 during flight as explained in more detail below. As mentioned earlier, anomaly detection in flight may allow the use of flight controls that mitigate effects of the structural anomaly, preventing further propagation of the anomaly that may result in extensive repair of the aircraft 300.
    [0052] [0052] For example, the 336 system can activate control surfaces and support surfaces in real time to compensate for structural anomaly. Alternatively, in other modalities, the 336 system can mitigate effects of the structural anomaly through activation, for example, but without limitation, of propulsion systems, active flow control, modeled metal alloys or other active structural materials that expand or contract as a function of a control signal, a combination thereof, or another activation mechanism.
    [0053] [0053] The control surfaces may comprise, for example, but without limitation, a landing gear door (not shown), a flight control surface such as a 306 leading edge (slat) surface part, a aileron 308, a tail 314, a rudder 316, an elevator 318, a flap 344, a speed reducer 338, or other control surface. The support surfaces can comprise, for example, but without limitation, a fuselage 302, a wing 304, a canar (not shown), a horizontal stabilizer 310, or other support surface.
    [0054] [0054] The structural anomaly can comprise, for example, but without limitation, an in-flight operation, a tension from wind shear on a support surface such as the fuselage 302, a tension from an impact of fragments on a surface of support such as the horizontal stabilizer 310, a tension arising from a gust of wind on a support surface such as wing 304, a vibration or flicker in wing 304, a fuselage flex such as flexing in the fuselage 302, excessive bending fuselage 302, a propulsion system anomaly such as propulsion system anomaly 320 (engine 320), excessive linear displacement, excessive angular displacement, structural fatigue, a control surface anomaly, a surface anomaly of lift such as a small wing anomaly 346, or other structural anomaly.
    [0055] [0055] The 336 system collects data from the measurement units (MUs). In one embodiment the MUs comprise, bridges / gauges or strain transducers located at various measurement points of interest on the 300 plane. Alternatively, the MUs may comprise inertial measurement units ("IMUs") located at various measurement points of interest in the plane 300. However, bridges / strain gauges can provide more accurate measurement responses than IMUs.
    [0056] [0056] The 336 system also collects data from a reference MU, which is preferably located in the 302 fuselage. The reference MU is treated as a fixed reference point that is not subject to twisting, bending or displacement during flight. MU provides a measure of change in angle and speed over a short period of time. In practice, the 336 system can measure torsion in real time in relation to the reference MU, but it can also compute the torsion between measurement MUs at various measurement points.
    [0057] [0057] MUs are installed on airplane 300 to provide in-flight torsion and wing / tail / fuselage deflection data for a flight control system (not shown). The MUs shown in Figure 3 in general can comprise, for example, but without limitation, a reference navigation IMU 326 coupled to processing module 410, a plurality of measurement navigation MUs 324/328/330/332 / 334 coupled to the processing module 410, and a GPS receiver (not shown) coupled to the 336 system. A practical modality can comprise, for example, but without limitation, any number of MUs measurement units or sensors located throughout the 300 plane, and the location of such MUs measurement units need not be restricted to the locations shown in figure 3.
    [0058] [0058] In the modality shown in figure 3, a commercial airplane is shown. It will be readily apparent to people of ordinary skill in the art that the modality shown in figure 3 can be applied or adapted to non-traditional structures such as, but without limitation, high-resistance vehicles at high altitudes whose total structure may be a surface of highly flexible controllable support, or other vehicle.
    [0059] [0059] Figure 4 is an illustration of an exemplary functional block diagram of a structural anomaly detection system based on a real-time model 400 (system 400, 336 in figure 3) suitable for detecting structural anomaly and operating one or more real-time control mechanisms to compensate for the structural anomaly detected. The various blocks, modules, processing logic and illustrative circuits described in connection with the 400 system can be implemented or executed with a general purpose processor, a content addressable memory, a digital signal processor, an application specific integrated circuit, an array of field programmable ports, any logic device, distinct port, or suitable programmable transistor logic, distinct hardware components, or any combination of them, designed to perform the functions described in this document.
    [0060] [0060] A processor can be realized as a microprocessor, a controller, a microcontroller, a state machine and more. A processor can also be implemented as a combination of computing devices, for example, a combination of a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in combination with a digital signal processor core, or any another such configuration.
    [0061] [0061] System 400 can comprise, for example, but without limitation, a desktop, a laptop or notebook, a portable computing device (PDA, cell phone, handheld computer, etc.), a large computer , a server, a client, or any other type of computing device for special or general use as may be desirable or appropriate for a given application or environment. System 400 generally comprises a structural anomaly detection module 402, a healthy structure model formulation module 404, a real-time measurement module 406, anomaly mitigation module 408 and a processing module 410 These components can be coupled and communicate with each other via a 416 bus.
    [0062] [0062] The structural anomaly detection module 402 is configured to detect at least one anomaly in the structure of the airplane 300 based on a difference between a healthy airplane response (expected response) and a real-time measurement (measured response) in a given location on plane 300 as explained in more detail in the context of the discussion of figure 5.
    [0063] [0063] The healthy structure model formulation module 404 can be located on board the airplane 300 and is configured to provide the healthy airplane response for a given flight condition and airplane status at the given location in the airplane structure 300. The healthy airplane response is used as an input to structural anomaly detection module 402. The healthy airplane response can comprise, for example, but without limitation, a deformation response, a vibration response, a stress response, a noise response, a temperature response, an optical response and more.
    [0064] [0064] In addition, the healthy aircraft response may comprise, for example, but without limitation, nominal torsion and torsional torsion to nose and wing tip to wing tip gradients, nominal airplane body bending, the MU of 326 reference navigation for each MU 324 / 328-334 measurement unit, landing gear bump and acceleration, desired control surface positions, desired airfoil positions based on current flight conditions (e.g. speed, altitude, Mach), accelerations, bump, positions, rates, navigation status data and more. Airplane parameters associated with these can comprise, for example, but without limitation, altitude, airplane type, model, weight and more. Airplane parameters can be compiled in real time during a flight and later downloaded into a database for use in the healthy structure model formulation module 404.
    [0065] [0065] In one embodiment, the healthy airplane response is obtained by reading a sensor signal from the MUs during healthy operation of the airplane 300. The sensor signal is then stored in memory module 414. A response from The expected signature signal is then formulated by the healthy structure model formulation module 404 representing a healthy operation of the airplane 300 based on the sensor signal providing the expected operating data.
    [0066] [0066] The real-time measurement module 406 is configured to receive real-time measurement for a given flight condition and a state of the plane 300 at the given location in the structure of the plane 300.
    [0067] [0067] Real-time measurement can be obtained using MUs such as strain gauges located on plane 300 as explained above. In one embodiment, MUs measure a representative sensor signal during various vehicle operations on a periodic basis to obtain real-time measurement. The real-time measurement is used as an input to the structural anomaly detection module 402. The real-time measurement can be obtained additionally, for example, but without limitation, through a vibration sensor, a noise sensor, a temperature sensor, an optical sensor and more.
    [0068] [0068] The anomaly mitigation module 408 is configured to activate a control mechanism in response to the structural anomaly detection module 402 confirming the detected structural anomaly to compensate for the detected anomaly. The activation of the control mechanism may comprise activation of the mechanism, for example, but without limitation, a control surface drive, a support surface drive, a propulsive power change, active flow control, flow control drive , activation of alloys with shape memory or other active structural materials that expand or contract as a function of a control signal, a combination of them and more.
    [0069] [0069] The bearing surfaces (for example, wing, canar, fuselage) provide support as a function of engine propulsion, while the control surfaces (for example, ailerons, flaps, rudder) can be displaced by means of actuators to control the plane flight path, commonly called flight control. In addition, actuators such as external / frame actuators and more can also be used to move support surfaces to a more desirable shape (eg fuel efficient) based on measured flight conditions received from the time measurement module real 406.
    [0070] [0070] For example, but without limitation, the anomaly mitigation module 408 is operable to control a position of the flap 344, control a position of the leading edge surface part (slat) 306, control a position of the speed reducer 338 and control positions of other control surfaces, through their respective actuators. Additionally, a series of actuators can be housed inside the fuselage 302, the tail section 340 and the wing 304 respectively, and operate based on commands received from the anomaly mitigation module 408. The anomaly mitigation module 408 receives data from the module formulation of a healthy structure model 404 that provides a desired position of the control surfaces and suitable support surfaces to alleviate a structural anomaly such as flexing, displacement or torsion of the airplane structure 300.
    [0071] [0071] For example, if the airplane 300 receives a gust of wind on one side, the structural anomaly detection module 402 detects a structural anomaly on wing 304 and in response to it the anomaly mitigation module 408 reacts quickly to prevent that the tension becomes too great to deform the wing 304. As another example, if turbulence results in vibration or flicker, and causes the structure of airplane 300 to enter a resonant frequency, motion is detected by the structural anomaly 402. After motion is detected, the anomaly mitigation module 408 generates a flight control command to cancel vibration or flicker. In another example, the system 400 can also relieve stress on at least a part of a fuselage such as a curved upper body half of the fuselage 302.
    [0072] [0072] In this way, the system 400 controls aircraft 300 in real time in response to detecting a structural anomaly in various flight conditions such as takeoff, cruise, approach and landing and other flight condition, without an operator / pilot interaction . However, in one mode, an operator / pilot can adequately overcome / prevent action commanded by the anomaly mitigation module 408 during the various flight conditions.
    [0073] [0073] The processing module 410 may comprise a processor module 412 and a memory module 414.
    [0074] [0074] The processor module 412 comprises processing logic that is configured to perform the functions, techniques and processing tasks associated with the operation of the 400 system. In particular, the processing logic is configured to support the 400 system described in this document. For example, processor module 412 can provide data from memory module 414 to structural anomaly detection module 402. As another example, processor module 412, in one embodiment, provides desired positional changes to the model formulation module. healthy structure 404 for anomaly mitigation module 408, which in turn uses raw data to calculate adjustments to be made to control surfaces and supporting surfaces, by operating one or more of the various control mechanisms described previously.
    [0075] [0075] The processor module 412 also accesses data stored in various databases in the memory module 414, to support functions of system 400. Thus, the processor module 412 enables activation of a control mechanism in the airplane 300 in response to detecting a structural anomaly in such a way that the structural anomaly is mitigated.
    [0076] [0076] The data can comprise, for example, but without limitation, an airspeed, an altitude, a desired position of control surfaces (for example, aileron 308) and desired position of the support surface (for example, of the wing 304), real-time measurement data, a modeling error signal, an estimated average value of the modeling error signal, an estimated variance of the modeling error signal, a measured data entry, an expected data entry, a computed False Alarm Probability (Pfa), an error significance, an anomaly indication output, a Pfa threshold value selected by the user, a persistence threshold value, a filter time constant and other data, as explained with more details to follow.
    [0077] [0077] The modeling error signal can be used to determine the existence of the structural anomaly as explained in more detail below. Data from memory module 414 can be used to construct or update, without limitation, the estimated mean, the estimated variance of the modeling error signal and the error significance.
    [0078] [0078] The processor module 412 can be implemented, or implemented, with a general purpose processor, a content addressable memory, a digital signal processor, an application specific integrated circuit, an array of field programmable ports, any device logic, distinct port, or suitable programmable transistor logic, distinct hardware components, or any combination thereof, designed to perform the functions described in this document.
    [0079] [0079] In this way, a processor can be realized as a microprocessor, a controller, a microcontroller, a state machine or the like. A processor can also be implemented as a combination of computing devices, for example, a combination of a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in combination with a digital signal processor core, or any other such configuration.
    [0080] [0080] The 414 memory module can be a data storage area with memory formatted to support the operation of the 400 system. The 414 memory module is configured to store, maintain and provide data as needed to support the 400 system functionality. in the manner described below. In practical terms, the memory module 414 may comprise, for example, but without limitation, a non-volatile storage device (non-volatile semiconductor memory, hard disk device, optical disk device and more), a storage device random access (eg, SRAM, DRAM), or any other form of storage media known in the art. The memory module 414 can be coupled to the processor module 412 and configured to store the data mentioned above.
    [0081] [0081] Additionally, the memory module 414 can represent a dynamically updating database containing a table for updating several databases. The memory module 414 can also store the data mentioned above, a computer program that is executed by the processor module 412, an operating system, an application program, experimental data used in the execution of a program and more.
    [0082] [0082] Memory module 414 can be coupled to processor module 412 in such a way that processor module 412 can read information and write information to memory module 414. As an example, processor module 412 and memory module 414 can reside in respective application-specific integrated circuits (ASICs). The memory module 414 can also be integrated with the processor module 412. In one embodiment, the memory module 414 can comprise a cache memory for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor module 412.
    [0083] [0083] Figure 5 is an illustration of an exemplary functional block diagram of structural anomaly detection module 402 (system 500) according to a disclosure modality. Figure 6 is an illustration of a graph 600 showing an exemplary Gaussian probability density (PDF) function 602 showing erf versus a modeling error signal according to a disclosure modality. The 500 system is described in this document with reference to the 600 chart. The 500 system can have functions, materials and structures that are similar to the modalities shown in figures 3-4. Therefore, common features, functions and elements may not be described here redundantly.
    [0084] [0084] System 500 may comprise an average / variance estimator 502, an error function module (erf) 504 and a smoother 506.
    [0085] [0085] The system 500 receives a measured_L 508 measured data input from the real-time measurement module 406 and an expected_L 510 expected data input from the healthy structure model formulation module 404. The measured_L 508 measured data input comprises a real-time measurement such as a measured deformation corresponding to a location on airplane 300 during operation of airplane 300.
    [0086] [0086] The measured_L 508 measured data input can be measured, for example, by a deformation sensor such as the MUs located on the plane 300 and be stored in the real-time measurement module 406. The expected_L 510 expected data input comprises the deformation expected at the same location on plane 300. System 500 generates anomaly indication output anom_detect 512 comprising a logic value. The logical value indicates TRUE if a structural anomaly is detected, or FALSE if a structural anomaly is not detected.
    [0087] [0087] System 500 then compares the real-time measurement to expected operating data for the location to provide a modeling error signal. In this way, the difference between the measured_L 508 measured data input, and the expected_L 510 expected data input is computed at a summation junction 514 to provide the modeling error signal 516. When the plane 300 is in a healthy state, the modeling error signal 516 should be approximately zero. A structural malfunction condition is indicated when the modeling error signal 516 is significantly away from zero.
    [0088] [0088] System 500 then determines whether a modeling error signal 516 or whether a significance of the modeling error signal 516 confirms indication of a structural anomaly for airplane 300 based on a statistical analysis. In this way the system 500 calculates a statistical significance of the modeling error signal 516 to provide an error significance to assess a probability that an anomalous structural indication would be in error. System 500 indicates a structural anomaly if a persistence of the error significance exceeds a persistence threshold value.
    [0089] [0089] The mean / variance estimator 502 is configured to repetitively estimate the estimated mean_est 528 and the estimated var_est 518 of the modeling error signal 516. Mean_est 528 and var_est 518 are used to determine statistical significance of the modeling error signal 516, thereby determining an error significance, as described below. The statistical significance of the modeling error signal 516 is determined by means of the estimated mean mean_est 528 and is a function of the estimated variance var_est 518 for the modeling error signal 516. Significance level (high / low) of the error significance is determined based on a user-selectable Pfa threshold value 702, as explained below in the context of the discussion in figure 7.
    [0090] [0090] A normal Gaussian probability density (PDF) function 602 (figure 6) is assumed for a process noise in the modeling error signal 516. PDF 602 comprises a Pfa 604 False Alarm Probability area and an area detection probability (Pd) 606. Using this assumption, a probability that the modeling error signal 516 is significantly distant from zero is evaluated when computing the Pfa 604. In this way, the statistical significance of the modeling error signal 516 is calculated by providing the error significance. Pfa 604 is defined by equation (1):
    [0091] [0091] where µ is the signal average and σx is the signal standard deviation.
    [0092] [0092] The integral in equation (1) has no closed solution. Thus equation (2) is used to approximate Pfa 604; where the input signal (In1) 530 (x in equation (2)) is the magnitude | µ | 522 of mean_est 528 (estimated signal mean µ) divided by the estimated standard deviation σx computed using a square root 524 of var_est 518.
    [0093] [0093] As shown in figure 6, Pfa 604 is computed by means of an integral (area 604) from negative infinity to zero. Thus, Pfa 604 comprises a standardized measure of the significance of the modeling error signal other than zero 516 providing the error significance.
    [0094] [0094] Pfa 604 in equation (2) is computed by the error function module 504. Depending on the user selectable Pfa threshold value 702 (figure 7), the computed Pfa value 534 (error significance) can be sent for smoother 506 to obtain anomaly indication output value anom_detect 512 determining the persistence of the error as explained in more detail in the discussion context of figure 7.
    [0095] [0095] Figure 7 is an illustration of an exemplary functional block diagram of smoother 506 (system 700) according to a disclosure modality. If the computed Pfa value 534 falls below the user selectable Pfa threshold value 702 (indicating a high level of error significance), a unit signal 704 is passed through a switch 714 to a first order filter 706. The first order filter 706 comprises a user selectable time constant tau 708. An output 710 of the first order filter 706 is compared to a value close to 1 in a comparison block 712. When output 710 of the first order filter 706 is close enough to 1, the error significance is high with sufficient persistence and an anomaly condition is transmitted to the anom_detect 512 anomaly indication output indicating the TRUE logical value. When the output 710 of the first order filter 706 is not close enough to 1, the error significance is not persistently high and the fault condition is not transmitted to the fault indication output anom_detect 512, indicating the logical value FALSE.
    [0096] [0096] The user selectable Pfa threshold value 702 (selected Pfa threshold value 702) and the user selectable time constant tau 708 (filter time constant 708) are adjustable parameters that depend on the quality of the error signal. 516 modeling and the tolerance for false positive indications. The quality of the modeling error signal 516 depends on a signal-to-noise ratio of a measurement signal such as the measured data input measured_L 508. If the measurement signal is heavily corrupted by noise, the mean_est estimates 528 (recursive mean ) and var_est 518 (recursive variance) of the modeling error signal 516 may be less accurate, which may result in variation in the computed Pfa value 534 and, in turn, in indications of false positive anomaly.
    [0097] [0097] The selected Pfa threshold value 702 can be selected, for example, but without limitation, within a range having values from about 0.0001 to about 0.01, or a similar range. The filter time constant 708 can be selected, for example, but without limitation, within a range having values from about 0.05 seconds to about 5 seconds or more, or a similar range.
    [0098] [0098] In this way, the anom_detect 512 anomaly indication output can be coupled with structural data measured by the real-time measurement module 406 and the anomaly mitigation module 408 to limit maneuvers of a non-ideal airplane structure within a wrap that maintains structural loads for the plane 300 at substantially ideal levels.
    [0099] [0099] Figure 8 is an illustration of an exemplary flowchart showing a process for detecting structural anomaly of vehicles based on model 800 according to a disclosure modality. The various tasks performed in connection with the 800 process can be performed mechanically, using software, hardware, firmware, computer-readable media having computer-executable instructions for executing the process method, or any combination thereof. It should be noted that process 800 can include any number of additional or alternative tasks, the tasks shown in figure 8 do not need to be performed in the order illustrated and process 800 can be incorporated into a more comprehensive procedure or process with additional functionality not described in detail. in this document.
    [0100] [00100] For illustrative purposes, the following description of process 800 may refer to elements previously mentioned in connection with figures 3-7. In practical terms, parts of process 800 can be performed by elements other than system 400 such as: structural anomaly detection module 402, healthy structure model formulation module 404, real-time measurement module 406, anomaly mitigation module 408 and processing module 410. Process 800 can have functions, materials and structures that are similar to the modalities shown in figures 3-7. Therefore, common features, functions and elements may not be described here redundantly.
    [0101] [00101] Process 800 can start by installing a plurality of measurement sensors such as MUs 324/328/330/332/334 in a vehicle vehicle structure, such as aircraft structure 300, that are operable for perform a real-time measurement such as measured_L 508 measured data entry (task 802).
    [0102] [00102] Process 800 can continue to read a sensor signal during healthy vehicle operation (task 804).
    [0103] [00103] Process 800 can continue to collect a representative sensor signal during additional operation of the vehicle on a periodic basis to obtain the measurement in real time (task 806).
    [0104] [00104] Process 800 can continue to formulate an expected signature signal response for healthy vehicle operation based on the sensor signal to provide expected operating data (task 808).
    [0105] [00105] Process 800 can continue to receive the measurement in real time corresponding to a location in the vehicle structure during a vehicle operation (task 810).
    [0106] [00106] Process 800 can continue when comparing real-time measurement such as measured_L 508 to expected operating data such as expected_L 510 for location to provide a modeling error signal such as modeling error signal 516 (task 812). For example, expected operating data may comprise a 7 degree frame twist with a 1 degree / second twist gradient. If the real-time measurement data indicates a structure twist that exceeds 7 degrees with a twist gradient greater than 1 degree / second, the modeling error signal 516 is a non-zero value.
    [0107] [00107] Process 800 can continue when calculating a statistical significance of the modeling error signal 516 to provide an error significance (task 814).
    [0108] [00108] Process 800 can continue to determine a persistence of the error significance (task 816) as explained previously in the context of the discussion of figure 7.
    [0109] [00109] Process 800 can continue to indicate a structural anomaly, if persistence exceeds a persistence threshold value (task 818). The persistence threshold value can be, for example, but without limitation, about 0.5, about 0.8, about 0.95 or another suitable threshold value, depending on the tolerance for a false positive structural anomaly detection and the convergence properties of the mean_est 528 (recursive mean) and var_est 518 (recursive variance) estimates of the modeling error signal 516 for a given application.
    [0110] [00110] Process 800 can continue to activate a control mechanism to compensate for the structural anomaly, if the structural anomaly is indicated (task 820). For example, if the structure torsions exceed 7 degrees with a torsion gradient greater than 1 degree / second, the persistence of error may be high, causing a structural anomaly to be indicated. A control can then be initiated by the anomaly mitigation module 408 to relieve structural stress by using a control mechanism to override the gradient and return the example structure to a 7 degree twist. The control mechanism may comprise, for example, but without limitation, a propulsion system, controllable bearing surfaces, flight control surfaces, active flow control, patterned metal alloys or other active structural materials that expand or contract as a function of a control signal, and more.
    [0111] [00111] Additionally, if the gradient is less than about 1 degree / second, but if the twist exceeds about 9 degrees with about 95% confidence, the persistence of error is high causing the anomaly detection module structural 402 identifies a structural anomaly. A control is initiated by the anomaly mitigation module 408 to reduce this twist back to around 7 degrees. Similarly, as an example, the 406 real-time measurement module measures in real time a twist of about 7 degrees with a gradient of about 1 degree / second and when it goes above 7 degrees of twist with this gradient, torsion and gradient indicate that the structure can continue for additional stress outside tolerance.
    [0112] [00112] In response, a control is initiated by the anomaly mitigation module 408 to cancel the torsion gradient and propel the torsion back in the direction of 7 degrees. In an alternative example, the real-time twist can reach about 9 degrees with about 95% confidence with little to no twist gradient. In response, a control is initiated by the anomaly mitigation module 408 to reduce the structural stress back in the direction of 7 degrees. In this way, alleviating the structural anomaly prolongs the structural life of the aircraft 300.
    [0113] [00113] Figure 9 is an illustration of an exemplary flow chart showing a 900 process for alleviating a structural anomaly according to a disclosure modality. The various tasks performed in connection with the 900 process can be performed mechanically, using software, hardware, firmware, computer-readable media having computer-executable instructions for executing the process method, or any combination thereof. It should be noted that process 900 can include any number of additional or alternative tasks, the tasks shown in figure 9 do not need to be performed in the order illustrated and process 900 can be incorporated and a more comprehensive procedure or process having additional functionality not described in detail. in this document.
    [0114] [00114] For illustrative purposes, the following description of process 900 may refer to elements previously mentioned in connection with figures 3-7. In practical modalities, parts of process 900 can be executed by elements other than system 400 such as: structural anomaly detection module 402, healthy structure model formulation module 404, real-time measurement module 406, anomaly mitigation module 409 and processing module 410. Process 900 can have functions, materials and structures that are similar to the modalities shown in figures 3-7. Therefore, common features, functions and elements may not be described here redundantly.
    [0115] [00115] Process 900 can start when obtaining a modeling error signal such as the modeling error signal 516 of a structure such as plane 300 (task 902).
    [0116] [00116] Process 900 can continue through an average / variance estimator such as the average / variance estimator 502 by repetitively estimating an estimated average and an estimated variance of the modeling error signal 516 (task 904).
    [0117] [00117] Process 900 can continue through a structural anomaly detection module such as structural anomaly detection module 402 (system 500) assessing a probability that the modeling error signal 516 is significantly distant from zero by using an error function module, such as error function module 504, computing a Pfa such as Pfa 604 from the modeling error signal 516 based on the estimated mean and estimated variance to provide an error significance ( task 906). As mentioned earlier, the significance of error provides an assessment of the probability that an anomalous structural indication would be in error. To justify a declaration of structural anomaly, the persistence of error is then determined.
    [0118] [00118] Process 900 can continue through system 500 by introducing a unit signal into a first order filter 706 such as first order filter 706 when the error significance falls below a selected Pfa threshold value such as the threshold value of Pfa selected by user 702 (task 908). The Pfa threshold value selected by user 702 and the user selectable time constant tau 708 of the first order filter 706 can be adjustable / selectable parameters that depend on the quality of the modeling error signal 516 and the tolerance for false indications. positive as explained above.
    [0119] [00119] Process 900 can continue through a smoother such as smoother 506 by comparing an output 710 of a first order filter such as first order filter 706 to a value close to one (task 910).
    [0120] [00120] Process 900 can continue through system 500 indicating a structural malfunction condition when output 710 of first order filter 706 is close enough to one, where persistence is high (task 912), indicating sufficient persistence anomalous structural behavior to justify a declaration of structural anomaly.
    [0121] [00121] Process 900 can continue through an anomaly mitigation module such as anomaly mitigation module 408 by activating a control mechanism to compensate for a detected structural anomaly, if the structural anomaly condition is indicated (task 914) .
    [0122] [00122] In this way, a system and methods are provided to detect and alleviate a structural anomaly.
    [0123] [00123] The previous description refers to elements or nodes or resources being "connected" or "coupled" together. As used in this document, unless expressly stated otherwise, "connected" means that an element / node / resource is directly linked to (or communicates directly with) another element / node / resource, and not necessarily mechanical form. Likewise, unless expressly stated otherwise, "coupled" means that an element / node / resource is directly or indirectly linked to (or communicates directly or indirectly with) another element / node / resource, and not necessarily mechanically. Thus, although figures 3-7 represent sample arrangements of additional elements, elements, devices, resources or components, they may be present in a disclosure modality.
    [0124] [00124] Terms and phrases used in this document, and variations thereof, unless expressly stated otherwise, should be interpreted as unlimited as opposed to limited. As examples of the precedent: the term "including" should be read to mean "including, without limitation" or the like; the term "example" is used to provide exemplary cases of the item under discussion, not an exhaustive or limiting list; and adjectives such as "conventional", "traditional", "normal", "standard", "known" and terms of similar meaning should not be interpreted as limiting the item described to a given period of time or to an item available as a given time, but should instead be read to cover conventional, traditional, normal or standard technologies that may be available or known now or at any time in the future.
    [0125] [00125] Likewise, a group of items linked with the conjunction "e" should not be seen as requiring that all of these items be present in the grouping, but should instead be seen as "and / or" unless expressly reported in another way. Similarly, a group of items linked with the conjunction "or" should not be seen as requiring mutual exclusivity between that group, but should also be seen as "and / or" unless expressly reported otherwise. .
    [0126] [00126] Furthermore, although items, elements or components of the disclosure may be described or claimed in the singular, the plural is considered to be within the scope of the same unless the limitation for the singular is explicitly reported. The presence of extension words and phrases such as "one or more", "at least", "but not limited to this" or other similar phrases in some cases should not be seen as meaning that the more limited case is intended or required in instances where such extension phrases may be missing. The expression "about" when referring to a value or numerical range is intended to cover values resulting from an experimental error that can occur when making measurements.
    权利要求:
    Claims (17)
    [0001]
    Method for detecting structural vehicle anomaly based on a real-time model (800), characterized by the fact that it comprises: receiving (810) a real-time measurement from a plurality of sensors (324, 328, 330, 332, 334) in a vehicle structure corresponding to a location in the vehicle structure during a vehicle operation; comparing (812) the real-time measurement to expected operating data for the location to provide a modeling error signal (516) of the vehicle structure; calculating (814) a statistical significance of the modeling error signal (516) based on a False Alarm Probability (Pfa) to provide an error significance of a structural vehicle anomaly; determine (816) a persistence of the error significance based on a user selectable False Alarm (Pfa) threshold value, where the user selectable Pfa depends on the quality of the modeling error signal (516) and a tolerance for false positive indications in real time; and indicate (818) the structural anomaly of the vehicle, in real time, if the persistence exceeds a threshold value of persistence of the structural anomaly of the vehicle.
    [0002]
    Method, according to claim 1, characterized by the fact that the step of calculating the statistical significance of the modeling error signal (516) still: repeatedly estimate (904) an estimated mean (528) and an estimated variance (518) of the modeling error signal (516) to determine whether the modeling error signal (516) confirms indication of the structural vehicle anomaly; and evaluate (906) a probability that the modeling error signal (516) is far from zero when computing the False Alarm Probability (Pfa) of the modeling error signal (516) for the detection of the vehicle structure based anomaly in the estimated average (528), and in the estimated variance (518) to provide the error significance.
    [0003]
    Method, according to claim 1, characterized by the fact that the step of determining the persistence of the error significance further comprises: introducing (807) a unit signal (704) into a first order filter (706) when the error significance falls below the user-selectable Pfa threshold value; comparing (910) a first order filter output (706) to a value close to one; and indicate (912) a structural malfunction condition when the output of the first order filter is close to one, indicating that the error significance is high.
    [0004]
    Method according to claim 3, characterized by the fact that it still comprises activating (914) a control mechanism to compensate for the structural anomaly, if the structural anomaly is indicated; and that the step of activating (914) the control mechanism comprises at least one element selected from the group consisting of: a control surface drive, a support surface drive, a flow control drive, alloy memory drive form, activation by means of active structural materials and a change in propulsive power.
    [0005]
    Method according to claim 1, characterized by the fact that it further comprises installing (805) a plurality of measurement sensors (324, 328, 330, 332, 334) in the operable vehicle structure to perform the measurement in real time.
    [0006]
    Method, according to claim 1, characterized by the fact that it still comprises: read (804) a sensor signal during healthy vehicle operation; and formulating (808) an expected signature signal response for healthy vehicle operation based on the sensor signal to provide expected operating data.
    [0007]
    Method according to claim 1, characterized by the fact that it still comprises collecting (806) a representative sensor signal during the additional operation of the vehicle on a periodic basis to obtain the measurement in real time.
    [0008]
    Method according to claim 1, characterized by the fact that the structural anomaly comprises at least one element selected from the group consisting of: an in-flight operation, a stress from wind shear on a support surface, a stress from an impact of fragments on a support surface, a tension from a gust of wind on a support surface, a vibration in a wing (304), a flutter in a wing (304), a flexion of the fuselage (302), an excessive curvature of the fuselage (302), a propulsion system anomaly, an excessive linear displacement, an excessive angular displacement, a structural fatigue, a control surface anomaly and a support surface anomaly.
    [0009]
    Structural anomaly detection system based on a real-time model, characterized by the fact that it comprises: an operable structural anomaly detection module for: receiving (810) a real-time measurement from a plurality of sensors (324, 328, 330, 332, 334) in a vehicle structure corresponding to a location in the vehicle structure during a vehicle operation; comparing (812) the real-time measurement to expected operating data for the location to provide a modeling error signal (516) in the vehicle structure; calculate (814) a statistical significance of the modeling error signal (516) based on a False Alarm Probability (Pfa) to provide an error significance of the vehicle structure anomaly; determine (816) a persistence of error significance based on a user-selectable False Alarm Probability (Pfa) threshold value if the error significance is below a user-selectable Pfa threshold value, where Pfa is selectable by user depends on the quality of the modeling error signal (516) and tolerance for false positive indications in real time; and indicate (818) a structural anomaly of the vehicle in real time, if the persistence exceeds a threshold value of persistence of the structural anomaly of the vehicle; and an operable anomaly mitigation module to activate a control mechanism to compensate for structural vehicle anomaly, if structural vehicle anomaly is indicated.
    [0010]
    System, according to claim 9, characterized by the fact that it still comprises: an average / variance estimator operable to repeatedly estimate an estimated average (528) and an estimated variance (518) of the modeling error signal (516); an operable error function module to evaluate a probability that the modeling error signal (516) is far from zero when computing a False Alarm Probability (Pfa) from the modeling error signal (516) based on the estimated average (528), and the estimated variance (518) to obtain the error significance; and a smoother comprising a first order filter (706) and operable to declare an anomaly condition when an output of the first order filter (706) is close to one indicating that the error significance is persistently high; and where the first order filter (706) comprises the selected Pfa threshold value and a filter time constant (708) which are adjustable parameters based on the quality of the modeling error signal (516) and tolerance for false indications positive.
    [0011]
    System, according to claim 9, characterized by the fact that the vehicle is an airplane (300) and the step of activating the control mechanism comprises at least one element selected from the group consisting of: a control surface drive, a support surface actuation, flow control actuation, actuation of alloys with shape memory, activation by means of active structural materials and a change in propulsive power.
    [0012]
    System, according to claim 9, characterized by the fact that it still comprises a formulation module of healthy structure model (404) operable for: read (804) a sensor signal during healthy vehicle operation; store the sensor signal in a memory (414); formulating (808) an expected signature signal response for healthy vehicle operation based on the sensor signal to obtain the expected operating data; and provide the expected operating data for the structural anomaly detection module.
    [0013]
    System according to claim 9, characterized by the fact that it still comprises a real-time measurement module operable for: measure a representative sensor signal during additional vehicle operation on a periodic basis to obtain real-time measurement; and provide real-time measurement for the structural anomaly detection module (402).
    [0014]
    System according to claim 9, characterized by the fact that it still comprises a plurality of sensors (324, 328, 330, 332, 334) comprising at least one element selected from the group consisting of: a deformation sensor, a vibration, a noise sensor, a temperature sensor and an optical sensor.
    [0015]
    System according to claim 9, characterized by the fact that the structural anomaly comprises at least one element selected from the group consisting of: an in-flight operation, a stress from wind shear on a support surface, a stress from an impact of fragments on a support surface, a tension from a gust of wind on a support surface, a vibration in a wing (304), a flutter in a wing (304), a flexion of the fuselage (302), an excessive curvature of a fuselage (302), a propulsion system anomaly (320), an excessive linear displacement, an excessive angular displacement, structural fatigue, a control surface anomaly and a support surface anomaly.
    [0016]
    Method to alleviate a structural anomaly (900), characterized by the fact that it comprises: monitor an entire vehicle structure in real time; obtaining (902) a modeling error signal (516) from a location in a vehicle structure out of the entire vehicle structure; evaluate (906) a probability that the modeling error signal (516) is significantly distant from zero when computing a False Alarm Probability (Pfa) to provide an error significance of a vehicle structure anomaly; determine (816) in real time a persistence of the error significance based on a user selectable False Alarm Probability (Pfa) threshold value, where the user selectable Pfa depends on a quality of the modeling error signal (516 ) and a tolerance for false positive indications in real time, through: input (908) a unit signal to a first order filter (706) when the error significance falls below the user-selectable Pfa threshold value and input a zero signal to the first order filter (706), otherwise; and indicate (912) a structural malfunction condition when a first-order filter outlet (706) is close enough to one, while not indicating the vehicle's structural malfunction condition when a first-order filter outlet (706) is not sufficiently close next to one.
    [0017]
    Method, according to claim 16, characterized by the fact that it still comprises activating (914) a control mechanism to compensate for the condition of structural vehicle anomaly, if the condition of structural anomaly is indicated.
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    同族专利:
    公开号 | 公开日
    CA2783388A1|2013-03-19|
    EP2570880A3|2013-09-11|
    BR102012023565A2|2013-10-29|
    CN103018060A|2013-04-03|
    CA2783388C|2015-06-16|
    US9020689B2|2015-04-28|
    EP2570880B1|2021-05-05|
    US20130073141A1|2013-03-21|
    CN103018060B|2017-09-12|
    EP2570880A2|2013-03-20|
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    法律状态:
    2013-10-29| B03A| Publication of a patent application or of a certificate of addition of invention [chapter 3.1 patent gazette]|
    2018-12-11| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|
    2019-10-29| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    2020-10-20| B09A| Decision: intention to grant|
    2021-01-12| B16A| Patent or certificate of addition of invention granted|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 18/09/2012, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    US13/236,448|2011-09-19|
    US13/236,448|US9020689B2|2011-09-19|2011-09-19|Method for real-time model based structural anomaly detection|
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