• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
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  • 优先权
    • 专利摘要:
    The present invention relates in particular to a method of monitoring a sensor that senses individual reference variables or measurement variables (A, B, C) associated with the process in each case, and is particularly suitable for a vehicle electrical stabilization program (ESP). The analytic redundancy is generated from the process monitoring variables and the process measurement variables that are not currently monitored by the multi-process model 31 for the normal operating mode, and the remaining 33 is formed into redundancy with the currently monitored output signal Particularly high reliability is obtained by monitoring the progress of the output signals of the individual sensors, in an automatic and sequential manner. After the remaining evaluation 36 and comparison 35 with the threshold value, the error signal F is generated when the remainder reaches the threshold value.
    公开号:KR20010093295A
    申请号:KR1020017009598
    申请日:2000-01-25
    公开日:2001-10-27
    发明作者:딩이브리민
    申请人:콘티넨탈 테베스 아게 운트 코. 오하게；
    IPC主号:
    专利说明:

    TECHNICAL FIELD [0001] The present invention relates to a sensor monitoring method and apparatus for an electronic stabilization program for a motor vehicle,
    [2] This type of electronic stabilization program is a vehicle dynamics control system that is used to assist the driver in dangerous driving situations during braking, acceleration, and steering, and to intervene where there is no room for the driver to directly intervene. This control system assists the driver in braking, which may be or may be out of control due to wheels locked on the road, especially on low or variable friction coefficient roads, Assist the driver in steering during cornering which may be oversteering or understeering. Overall, not only comfort but also active safety is significantly improved.
    [3] This type of control system is based on a closed loop control circuit that is typically responsible for control purposes during normal vehicle operation and is intended to stabilize the vehicle as quickly as possible in extreme driving situations. Sensors for sensing various driving dynamics parameters are particularly important as devices for generating actual values. Preconditions for proper control are precisely representative of the actual conditions of the system in which the sensor is controlled. This is particularly important in operation stabilization control operation in extreme operating conditions where control deviations must already be adjusted by control within a very short time. This is why the ESP sensors (yaw rate sensor, lateral acceleration sensor, steering angle sensor) of the electronic stabilization program require constant monitoring. The purpose of the related on-line sensor monitoring is to detect errors in the ESP sensor early in order to prevent erroneous control which may result in dangerous vehicle conditions in terms of safety.
    [1] The present invention relates to a method and apparatus for monitoring a sensor which, in each case, senses a measurement parameter associated with an individual reference variable or process, and more particularly to a method and apparatus for an electronic stabilization program (ESP) for a vehicle.
    [9] 1 is a block diagram of a driving dynamics control system.
    [10] 2 is a schematic diagram of an ESP system architecture;
    [11] 3 is a diagram showing a basic principle of an error diagnosis system;
    [12] Fig. 4 shows a structure of a model-based monitoring system for an ESP sensor.
    [13] 5 is a block diagram of a sensor monitoring arrangement in accordance with the present invention.
    [14] 6A, 6B and 6C are diagrams showing measurement results in an error simulation;
    [15] 7A, 7B and 7C are diagrams showing measurement results in a slalom operation;
    [4] In view of the above, it is an object of the present invention to provide a method and apparatus for monitoring such a sensor of the type that provides the reliability required especially for an electronic stabilization program (ESP) for a vehicle.
    [5] This object is achieved according to claim 1, from a process reference variable and / or a process measurement variable of a current process which is not currently being monitored, based on a multi-process model for a normal operating mode, Generating an analytical redundancy for a variable; generating a remainder by subtracting the generated surplus analytical redundancy from a currently monitored process reference variable or process measurement variable; and evaluating the remainder using the remaining evaluation function, Comparing the remainder to a predetermined threshold value; and generating an error message when the remainder reaches the limit value during at least one predetermined monitoring time, wherein the progression of the output signal of the respective sensor cyclically and sequentially A mother Which it is characterized in that sintering is accomplished using the type of method described above.
    [6] This object is also achieved according to claim 9, from a process reference variable and / or a process measurement variable of a current process which is not currently monitored by a multi-process model for a normal operating mode, A second device for generating the remainder by subtracting the surplus analytic redundancy computed from the currently monitored process reference variable or the process measurement variable, and a second device for computing the remaining analytical redundancy using the remaining evaluation function, A fourth device for generating a threshold value, and a second device for comparing the evaluated remainder to a threshold value and generating an error message when the remainder reaches a threshold value for at least one predetermined monitoring time And a fifth device Which it is achieved by using the above type of apparatus.
    [7] The dependent claims relate to preferred aspects of the present invention.
    [8] Further details, features and advantages of the present invention can be better understood from the following description of the preferred embodiments with reference to the accompanying drawings.
    [16] 1, the act of driving a vehicle can be considered as a control circuit in terms of control technology, in which the driver 1 represents the controller and the vehicle 2 represents the controlled system. In this arrangement, the reference variable is the driver's personal driving demand FW generated by the driver by constantly monitoring the road traffic. The actual value IF is an instantaneous value for the driving direction and speed that the driver perceives with his or her eyes or driving sensation. The calibration variable SF is the position of the accelerator pedal and the brake pedal provided by the driver based on the angle of the steering wheel, the position of the transmission, and the mismatch between the nominal and actual values.
    [17] This type of control is often adversely affected by a disturbance S, such as a coefficient of friction, road irregularity, cross winds or other influences, since the driver can not accurately measure these factors but must consider them in control to be. Thus, the driver 1 can deal with the task of controlling and monitoring the process of driving a car under normal driving conditions, with the help of mastery and experience, which is normally obtained without difficulty. However, there is a risk that, in the presence of the aforesaid emergency operating conditions beyond the physical limits of the friction between the road and the tire and / or in extreme situations, the driver may be too late or misbehaving to lose control over the vehicle.
    [18] In order to take account of such a driving situation, the driving dynamics control system is provided with a lower control circuit 3 (ESP) including a control algorithm 4, a system monitoring device 5 and an error memory 6. The measured operating condition variables are sent to the system monitoring device 5 and the control algorithm 4. If necessary, the system monitoring apparatus 5 generates an error message F which is transmitted to the error memory 6 and the control algorithm 4. [ The control algorithm 4 then acts on the vehicle 2 as a function of the calibration variable generated by the driver 1. A typical control task is performed by this control circuit. The vehicle is stabilized as quickly as possible in extreme driving situations.
    [19] Figure 2 shows the structure of this type of control circuit, generally including an anti-lock system 10, a traction slip control system 11, and a yaw torque control system 12. It should also be understood that the yaw rate sensor 13, the lateral acceleration sensor 14, the steering angle sensor 15, the pressure sensor 16, and the actual value generator for determining deviations, There are provided four wheel speed sensors 17 which are used as an actual value generator for generating a wheel speed.
    [20] The process reference variables generated by the driver 1 by operating the accelerator pedal and the brake pedal and the steering wheel are controlled by the traction slip control system 11, the anti-lock system 10 and the pressure sensor 16 and / or the steering angle sensor 15 ). The nonlinearity of the vehicle, the change of the coefficient of friction, and the cross wind effect are summarized as obstacle or unknown amount 18, which affects the longitudinal and lateral dynamics 19 of the vehicle. The dynamics 19 is further influenced by the above-mentioned reference variable and the output signal of the engine management unit 20 and is transmitted to the wheel speed sensor 17, the yaw rate sensor 13, the lateral acceleration sensor 14, Acts on the sensor 16. The control adjustment 21 to which the output signals of the anti-lock system 10, the traction slip control system 11, the yaw torque control system 12 and the brake intervention algorithm 22 are supplied is transmitted to the engine management unit 20, Is used for the preferential distribution of these signals with respect to their action acting directly on epidemiology (19). The brake intervention algorithm 22 is influenced by the yaw torque control system 12 and the pressure sensor 16. Also provided is an operating condition detecting unit 23 to which signals of the steering angle sensor 15, the yaw rate sensor 13, the lateral acceleration sensor 14 and the wheel speed sensor 17 are transmitted, Output signal affects the single track reference model 24 and the yaw torque control system 12 such that the desired nominal yaw rate is generated.
    [21] As already explained, erroneous sensor signals can cause dangerous improper control. Failure of the yaw rate sensor 13 may result in additional yaw torques occurring, for example, even though the driver wishes to go straight ahead, and suddenly apply lateral forces on the vehicle. This is because the steering angle becomes zero during straight travel, and thus the nominal value of the yaw rate becomes zero, but the actual value of the yaw rate due to the sensor failure is an unspecified value, so that the yaw torque control system 12 has an additional Resulting in the generation of yaw torque. Online monitoring of sensors is very important for this reason. This monitoring should be able to detect early sensor failures in time or partly so that the ESP system can be shut down in a timely manner.
    [22] The sensor monitoring concept of the present invention consists of a multi-stage function of a sensor in which two methods are used. On the other hand, an electrical monitoring is performed so that the observed sensor signal is checked to be within an allowable error band. On the other hand, an analytical redundancy monitoring is performed to enable the signal to be monitored over a useful warhead.
    [23] In the first step, the sensor supply voltage and wiring are tested by an electrical monitoring arrangement. In the second stage, these sensors, made 'intelligently' due to their importance, are always self-tested. When an internal sensor failure occurs, the sensor signal belongs to the error band. Thus, such sensor failure may be detected by an electrical monitoring operation.
    [24] Whether the sensor signal is within the effective range is only checked by electrical monitoring. However, it is not possible in this monitoring to detect other sensor faults, such as erroneous or loosened mounting locations, ground faults, etc. For this reason, the progress of individual sensor signals within the useful range is monitored cyclically and sequentially by a third step, i.e., analytical redundancy calculated from sensor output signals that are not currently monitored based on physical dependencies. To this end, a model ESP monitoring and fault diagnosis system is provided, the basic structure of which is shown in Fig.
    [25] The error diagnosis system 100 basically consists of two parts, the remaining generator 30 and the remaining evaluation unit 34. [
    [26] The remaining generator 30 is configured to generate a process reference variable A and / or a current process (e.g., a process) that is currently occurring, by using a multiple process model (Gl to G4; Q1 to Q4; L1 to L4, (C) from the currently unmonitored process measurement variable (B) that is generated by the process monitoring variable (32) that is currently unmonitored process measurement variable (B) And a first device 31 for calculation. Also provided is a second device 33 for generating the remainder (r) by subtracting the calculated surplus analytical redundancy from the currently monitored process reference variable or process measurement variable (C).
    [27] The remaining evaluation device 34 includes a third device 36 for evaluating the remaining r with the remaining evaluation function and a fourth device 35 for generating a threshold value. In order to raise the threshold when the inaccuracy of the multi-process model is relatively high and to reduce the threshold when the model's inaccuracies are relatively low, currently unmonitored process reference variables and / or process measurement variables A, And is sent to the device 35. A fifth device 37 is also provided for comparing the evaluated remainder to a threshold value and for generating an error message F when the remainder reaches a threshold value for at least one predetermined monitoring time.
    [28] In order to illustrate the problem to be solved by the present invention and to understand the solution of the present invention generally shown in FIG. 3, the following background information is given first.
    [29] Using only one process model (instead of a multi-process model) to generate the remainder, it is already possible to obtain information on the current process conditions, and therefore possible malfunctions. However, validity is highly dependent on the quality of the process model used. As the inaccuracy of the process model increases, it is necessary to increase the threshold value to prevent false alarms. As a result, many errors exist without being recognized. On the contrary, attempts to increase the accuracy of the process model increase the complexity of the model at the same time. When such an attempt is made, the costs arising from the use of the model in on-line calculation are high and the need for development and maintenance is high, Often fail. Therefore, the trade-off between model accuracy and the control of the threshold value, and thus the system sensitivity, plays a major role in the development of a model-based ESP fault diagnosis system.
    [30] It should also be noted that the process of driving a car is known to be largely influenced by many environmental factors, which are still unknown. In addition, the driving dynamics can be mathematically described to some extent. On the other hand, the limit of usability is defined from the beginning by hardware conditions. All these marginal conditions require a solution that is explicitly based on the principles of model-based methods, and the use of them in an ESP system is nevertheless justified.
    [31] The basic concept of model-based error diagnosis is the examination of the physical interrelation described in the form of a mathematical model. It is assumed that Equation 1 is given below.
    [32]
    [33] Where y denotes the output signal of the monitored sensor, u 1 , ..., u m denotes a known or measured physical quantity, and f denotes a mathematical function. In this case, analytical redundancy The
    [34]
    [35] And the remaining r is
    [36]
    [37] .
    [38] The remainder is usually zero if there are no errors. When a sensor failure occurs, these rules are no longer useless because the remainder are significantly away from zero. The difficulty in realizing this concept is that the model only partially describes the process. This so-called model inaccuracy can be expressed by an extension of the process model as follows:
    [39]
    [40] Here, Is an unknown amount depending on process conditions. It is a prerequisite for reliable model-based error diagnosis that the influence of Δ on the rest is kept to the minimum possible.
    [41] In principle, there are two ways to suppress the influence of DELTA.
    [42] 1. Apply modern robust control theory to increase the robustness of the monitoring system: this is usually a passive approach requiring complex design and increased computational complexity (both offline and online)
    [43] 2. Obtain additional information: This can be achieved in two ways: on the one hand, by obtaining offline information, on the other hand by improving the model that further complicates online calculations, and by utilizing additional online information It is an active way. This approach has been found to be particularly advantageous in reaching a solution to the above-mentioned problems in accordance with the present invention.
    [44] The use of additional on-line information allows creating a multiple (redundancy) model for the monitored sensor, that is, by signals from different sensors or unmonitored sources, as well as reconfiguring the behavior and functionality of this sensor . On the other hand, this redundant analytical redundancy improves the reliability of the monitoring system and, on the other hand, improves the robustness to model inaccuracies. One preferred embodiment of the method of the present invention enables the realization of this basic principle, which is presented below.
    [45] It should be assumed that a model can be generated for the operation of the sensor signal being monitored using the following formula system.
    [46]
    [47] ..., m) represent signals from various sources, f 1 , ..., f n are for the partial model, u ij (i = 1, △ 1, ... △ n denotes a model inaccuracies in the individual partial model, PZ is related to the process conditions, GB i (i = 1, ..., n) represents the range for which the partial model applied.
    [48] The availability of individual partial models and model inaccuracies depend on process conditions. The remaining problem is on the one hand, the rest on a multi-process model that is sensitive to errors that are detected, and on the other hand, the robustness about model inaccuracies.
    [49] The driving situation is divided into two groups for this purpose.
    [50] 1. Unusual driving behavior where model inaccuracies are very obvious and only a few partial models are useful.
    [51] 2. Normal operation with normal characteristics with most partial models available and low model inaccuracies.
    [52] 1: Normal driving operation: Since the absolute value of the remaining is used as the remaining evaluation function, the remaining r
    [53]
    [54] Is the result of being the strongest relative model inaccuracies and at the same time being least sensitive to errors. Therefore, it is determined for this operating situation:
    [55] When the number of useful partial models is much smaller than the predetermined number (<< n), the remainder will be evaluated according to the principle of equation (2).
    [56] This is referred to as the "minimum of all," and its basic conception is accompanied by an increase in robustness to an increased degree in the abnormal range, where the model inaccuracy is very obvious.
    [57] According to 2: Normal driving behavior: means that all or almost all partial models are useful
    [58]
    [59] And thus generally when normal process conditions exist, the remainder can be selected using the following algorithm:
    [60] Step 1: Average value Creation of
    [61]
    [62] Step 2: Having the lowest deviation of And Selection of
    [63]
    [64] Step 3: Create the remaining r
    [65] Assuming that,
    [66] to be.
    [67] To illustrate the function principle of this algorithm, two cases are considered.
    [68] a) Fail-safe operation: In this case, the following applies for "best case":
    [69]
    [70] This means that model inaccuracy does not affect the rest. In the "worst case", the following applies:
    [71]
    [72] The maximum possible deviation can be limited by the following formula.
    [73]
    [74] Deviation caused by model inaccuracies is minimized because generation of average values suppresses model inaccuracies in the best case.
    [75] a) Sensor error: In this case, there is an application for "normal case" as follows:
    [76]
    [77] The sensor signal y deviates significantly from its normal value and is therefore far away from y ik , k = 1,2,3 due to the error. As a result, there is a large difference between y and y i2 . In the "worst case"
    [78] to be.
    [79] This means that no error can be detected. However, this case occurs only when the magnitude of the error is within the range of the model inaccuracy. It also shows that the acceptance limits of the monitoring concept are substantially determined by model inaccuracies.
    [80] As already explained, the concept of the remainder of the occurrence is a prerequisite for testing the usability of the model. This includes testing for the reliability of the signals used for the remainder of the generation and testing for model availability in response to operating conditions.
    [81] The signal shall be reliable when the signal is checked in terms of software or hardware. A reliable signal may be a signal from another partial function of the system, or it may be a signal from another sensor, which means mutual monitoring. This is the online information used to create the multi-process model.
    [82] As already explained, the remainder generated is highly dependent on model inaccuracies, and model inaccuracies are affected by various operating conditions. Therefore, it is desirable to develop the remaining evaluation units appropriately matching the operating conditions.
    [83] As is generally known, the driving operation can be described very accurately during stable operation. Conversely, it is very difficult to mathematically regenerate very abrupt drive operations. Therefore, it is desirable to discriminate the operating conditions according to each case and adjust the monitoring limit and time accordingly. Application of the monitoring limit means that on the one hand, an error message is generated in a timely manner when an inappropriate sensor signal appears, and on the other hand, a false error message which can be caused by the inaccuracy of reproduction is prevented. This means that the threshold value should be adjusted higher and the monitoring time should be adjusted longer when the sensor signal reproduction accuracy is low, while in other cases the threshold value should be lowered and the monitoring time should be shortened.
    [84] Below we describe how to implement the concept of monitoring three important ESP sensors, yore-rate sensors, lateral acceleration sensors, and steering wheel sensors, as previously presented.
    [85] 4 shows the structure of a model-based monitoring system for the ESP sensor, i. E. The yaw rate sensor 13, the lateral acceleration sensor 14 and the steering angle sensor 15. Fig. For monitoring each ESP sensor, four redundancy models can be used as long as they are useful. These models are Models G1 to G4 for the yaw rate sensor 13, Models Q1 to Q4 for the lateral acceleration sensor 14 and Models L1 to L4 for the steering angle sensor 15. The mathematical realization of the process model and their usefulness are shown in Table 1. The symbols used in Table 1 are defined as follows.
    [86] - Model. - Rate.
    [87] a qm - Model lateral acceleration.
    [88] δ Lm - Model steering wheel angle.
    [89] - Yo-Late.
    [90] a q - lateral acceleration.
    [91] δ L - steering wheel angle.
    [92] i L - Steering ratio
    [93] l - Wheelbase
    [94] S - track width of vehicle
    [95] v ch - Characteristic operating speed
    [96] Table 1EquationUsability condition Model G1 The two front wheels do not slip, their error flags are not set, and the reproduction is within the useful range. Model G2 The two rear wheels do not slip, their error flags are not set, and the playback is within the useful range. Model G3 The operating speed must exceed zero. Model G4 There is no counter steering and there is no large steering when the vehicle is at high speed. Model Q1 Same as model G1 Model Q2 Same as model G2 Model Q3 - Model Q4 - Model L1 Same as models G1 and G3 Model L2 Same as models G2 and G3 Model L3 Same as Model G3 Model L4 Same as Model G3
    [97] The models are used in the first device 31 and the following signals are used as the input quantity to calculate the redundancy and thus determine the remainder:
    [98] v vr - the wheel speed of the front right wheel
    [99] v vl - Wheel speed of left front wheel
    [100] v hr - Wheel speed of right rear wheel
    [101] v hl - wheel speed of left rear wheel
    [102] v ref - vehicle speed
    [103] These are generated using a partial function of the anti-lock system, yaw rate, lateral acceleration and steering angle, resulting from the three ESP sensors 13, 14 and / or 15. The calculated redundancy is applied to the third device 36 for generation and evaluation of the remainder (including the second device 33 also), together with the respective sensor signal to be monitored. After obtaining the difference between the respective remainder and the limit value generated by the fourth device 35, the fifth device 37, when the difference value exceeds the prescribed value, The error message F / UG, the F / UQ for the lateral acceleration sensor 14 or the F / UL for the steering angle sensor 15. The third, fourth and fifth devices 36, 35 and 37 for the respective sensors 13, 14 and 15 are respectively shown in Fig.
    [104] Reliability testing of the signals generated by the anti-lock system is performed by the monitoring systems present in these. The signals are reliable in the absence of an error message, and can not be applied in the presence of an error message.
    [105] In the case of the three ESP sensor signals (yaw rate, lateral acceleration, steering angle) for the sensor monitoring system described herein, each signal is determined to be reliable in the absence of an error message, The operation is stopped.
    [106] As mentioned above, the above-mentioned method 'majority principle' is sensitive to sensor errors when the sensor is faulty, but the method 'minimum of all' is a system failure and a temporary or extreme It is stronger in terms of driving operation. The above-mentioned monitoring concept is realized as follows.
    [107] When the number of useful models is less than 3, the remainder is generated according to the 'least of all' principle. In all other cases, the rest is generated by the 'most principle'.
    [108] In theory, all these process models apply only to a stable or linear range of operating dynamics. When the operation is no longer within this range, the monitoring limit should be increased and the monitoring time extended. This is done by determining the degree of deviation of the driving operation from the situation detection in the fourth device (threshold value calculation 35) and the stable or linear range (compare with FIG. 4). The signals used for this purpose are: vehicle speed v ref , wheel speeds v vr , v vl , v hl , v hr of four wheels, longitudinal acceleration a 1 of the vehicle caused and derived from the partial function ABS, Calculated redundancy and other ESP sensor signals.
    [109] These monitoring limits and time are set or determined by investigation of vehicle operation in the presence of various types of errors in various operating situations. When a change in the yaw rate sensor having a large degree of variation not related to any of the possible driving operations is detected, the monitoring time is significantly shortened.
    [110] The adjustment of monitoring limits and time is briefly summarized in Table 2.
    [111] Table 2: Monitoring limits and time Driving situationProcess model accuracyMonitoring limitMonitoring time Errors with high slopeOther Stable driving: straight driving and stable round drivingVery accuratelittlenessVery shortshort The driving operation is stable or a temporary running close to the linear range, for example, a slalom operationinaccuracygreatnessVery short긺 A driving operation that is far from a stable or straight range, for example, a sliding operationImpossible to expressInfinitely large (no monitoring)Infinite (no monitoring)
    [112] The structure of the hardware realization is shown in Fig. This structure includes a microprocessor system 40 having an output signal to be sent to unit 41 for brake and engine engagement.
    [113] The microprocessor system 40 includes an analog / digital converter 401 for converting an analog sensor signal and a digital / analog converter 403 connected to the analog / digital converter 401 and for generating an analog output signal And a digital control algorithm 402 connected thereto. The digital sensor signal is again sent to the monitoring system 404 where the amount of system generated by the digital control algorithm 402 is applied and an error message is sent to this unit 402.
    [114] The ESP system, which includes both a digital control algorithm and a monitoring system, is preferably programmed in the C language and subsequently used in the microprocessor system 40. The input signal of the microprocessor system 40 is a signal generated by the sensor 43 installed in the vehicle 42. The input signal of the microprocessor system 40 is a calibration variable that is sent for the brake and engine management system 41. The monitoring system 404 operates in parallel with the control system and monitors the entire system and thus does not affect the control when no error is detected. When an error is detected, the monitoring system 404 sends an error signal to the digital calculation algorithm 402, which will cause the ESP system to cease operation.
    [115] The monitoring system was tested in several operating tests. In Figs. 6a, 6b and 6c, the measurement results of two road test runs are shown as an example, that is, the result of the error simulation of the yaw rate sensor during straight running.
    [116] 6A shows the signal (line 1) of the yaw rate sensor and its four regeneration (lines 2 to 5). Figure 6b shows the progress and limits (lines 2 and 3) of the remainder (line 1). In Fig. 6C, an error message is generated.
    [117] It can be clearly seen from these cities that the yaw rate can be described very accurately. The simulation error was detected within 0.25 seconds before yaw torque control applied high pressure to the wheel.
    [118] Figs. 7A, 7B and 7C show measurement results of monitoring the yaw rate sensor during riding with a slalom operation. During this slalom operation, the y-rate can not be accurately described due to the phase difference between the sensor signal and the model signal. In this situation, when a model is created, such as model inaccuracy generally can not be prevented. To prevent false alarms, the monitoring limit is already increased at the start of the slalom operation. 7A shows the signal (line 1) of the yaw rate sensor and its four regeneration (lines 2 to 5). It can be seen from Fig. 7 (c) that no error message has been generated while the progress of the rest (line 1) and the limits (lines 2 and 3) is shown in Fig. 7b.
    [119] Taken together, a method and apparatus for sensor monitoring in an ESP system has been described wherein the essence is the remainder of the occurrence based on a multi-process model, the deployment of which is first performed in consideration of operational dynamics and practical availability and applicability. Sensor faults and sensor faults with particularly large tilt can be detected during operation by sensor monitoring operation. The monitoring system provides high reliability because this system represents a high degree of inaccuracy of the robustness opponent model on the one hand and high sensitivity to sensor errors on the other hand.
    权利要求:
    Claims (12)
    [1" claim-type="Currently amended] A method of monitoring a sensor that, in each case, detects an individual reference variable or measurement variable associated with a process,
    The process reference variable and / or the process measurement variables A, B and C of the current process 32 that are not currently monitored, based on the multi-process model 31 (G1 to G4; Q1 to Q4; L1 to L4) B) to the currently monitored process reference variable or the process measurement variable (C) ),
    From the process reference variable or the currently monitored process measurement variable (C), the provided surplus analytical redundancy ( ) To produce a remainder (r)
    Evaluating the remainder (r) by the remaining evaluation function,
    Comparing the predetermined threshold with the estimated residual and generating an error message (F) when the remainder reaches a threshold value for at least one predetermined monitoring time
    &Lt; / RTI &gt;
    [2" claim-type="Currently amended] The method of claim 1, wherein the process is part of an electric vehicle operation stabilization program (ESP)
    Wherein said process reference variable and process measurement variables (A, B, C) are yaw rate, lateral acceleration and steering angle.
    [3" claim-type="Currently amended] 3. The method of claim 2, wherein the method is performed in parallel with the electrical operation stabilization program (ESP), and disables the ESP when an error message (F) occurs.
    [4" claim-type="Currently amended] 4. The method according to any one of claims 1 to 3, wherein the multiple process model is formed of a plurality of partial models (G1 to G4; Q1 to Q4; L1 to L4) or process measured variable (C) is a process reference variable or process measured variable that is not monitored (a, B), the wheelbase (l), the speed of doubt the vehicle is determined from the track width (S) and four wheel speed (v ref) Such that the physical quantities are reproducible on the basis of physical correlations.
    [5" claim-type="Currently amended] 5. The method of any one of claims 1 to 4, wherein the remainder of the evaluation function is generated by an algorithm according to the 'least of all' principle in a transition process condition that is distinguished by high model inaccuracies, Is compared to a threshold value and an algorithm according to the 'most principle' is provided in a stable process condition that is distinguished by a low model inaccuracy, in which an average value of the analytical redundancy is formed,
    Wherein the analytic redundancy is used to generate a remainder to be compared with a threshold value which is an intermediate value of three redundancies having a minimum inconsistency from the average value.
    [6" claim-type="Currently amended] 6. The method of claim 5, wherein if the number of available models is less than 3, the remainder is generated according to the 'least of all' principle, otherwise, the remainder is generated according to the 'most principle'.
    [7" claim-type="Currently amended] 7. A method according to any one of claims 1 to 6, characterized in that, on the one hand, an error message is generated in a timely manner when a wrong sensor signal appears, and on the other hand, a false error message due to high model inaccuracy is prevented. that processes the reference variable, and a process measurement variables (a, B), and a wheel speed (v vl, v vr, v hl, v hr), and depending on the process conditions by the vehicle velocity (v ref) and calculates a threshold value Said method comprising the steps of:
    [8" claim-type="Currently amended] 8. A method according to any one of the preceding claims, wherein the monitoring time is configured to suit current process conditions and is selected such that a simple failure of the sensor is allowed.
    [9" claim-type="Currently amended] An apparatus for monitoring a sensor that measures in each case an individual reference variable or measurement variable associated with a process,
    (A, B) of the current process 32 that is not currently monitored by the process model parameters G1 to G4 (Q1 to Q4; L1 to L4) for the normal operation mode A first device 31 for calculating an analytic redundancy for a currently monitored process reference variable or process measurement variable C,
    A second device (33) for generating the remainder (r) by subtracting the calculated surplus analytical redundancy from the currently monitored process reference variable or process measurement variable (C)
    A third device 36 for evaluating the remainder using the remaining evaluation function,
    A fourth device (36) for generating a threshold value,
    A fifth device (37) for comparing the estimated residual (r) to a threshold value and for generating an error signal (F) when the remainder reaches a threshold value for at least one predetermined monitoring time,
    &Lt; / RTI &gt;
    [10" claim-type="Currently amended] 10. The method of claim 9, wherein the process reference variable and / or the currently unmonitored process measurement variable (A, B) are selected to increase the threshold value by the context detection in the case of a multi-process model with relatively high inaccuracy, Is sent to the fourth device (35) in order to reduce the limit value in the case of the multi-process model of the device.
    [11" claim-type="Currently amended] 11. Apparatus according to claim 9 or 10, characterized in that the first to fifth devices are implemented by a microprocessor system (40).
    [12" claim-type="Currently amended] In an electric stabilization program system for a vehicle,
    Characterized in that it comprises an apparatus according to any one of claims 9 to 11 for cyclic monitoring of a yaw rate sensor (13), a lateral acceleration sensor (14) and a steering angle sensor (15).
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    同族专利:
    公开号 | 公开日
    KR100654651B1|2006-12-07|
    JP4693242B2|2011-06-01|
    JP2002536233A|2002-10-29|
    DE19939872A1|2000-08-10|
    DE19939872B4|2012-07-26|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    法律状态:
    1999-02-01|Priority to DE19903934.8
    1999-02-01|Priority to DE19903934
    1999-06-25|Priority to DE19929155
    1999-06-25|Priority to DE19929155.1
    1999-08-24|Priority to DE19939872A
    1999-08-24|Priority to DE19939872.0
    2000-01-25|Application filed by 콘티넨탈 테베스 아게 운트 코. 오하게
    2001-10-27|Publication of KR20010093295A
    2006-12-07|Application granted
    2006-12-07|Publication of KR100654651B1
    优先权:
    申请号 | 申请日 | 专利标题
    DE19903934.8|1999-02-01|
    DE19903934|1999-02-01|
    DE19929155|1999-06-25|
    DE19929155.1|1999-06-25|
    DE19939872A|DE19939872B4|1999-02-01|1999-08-24|Method and device for sensor monitoring, in particular for an ESP system for vehicles|
    DE19939872.0|1999-08-24|
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