• 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 invention relates to a method for monitoring at least one exhaust gas turbocharger (ATL) of a large internal combustion engine (BKM), with at least one compressor (1a) and a shaft-like arranged exhaust gas turbine (1b), wherein the actual pressures (p1, p2) upstream and downstream of the Compressor (1a) and the current temperatures (T1, T4) are measured upstream of the compressor (1a) and upstream of the exhaust gas turbine (1b). In order to effectively and easily monitor an exhaust gas turbocharger, it is provided that the actual pressures (p4, p5) upstream and downstream of the exhaust gas turbine (1b), the actual temperatures (T2, T5)) downstream of the compressor (1a) and downstream of the compressor Exhaust gas turbine (1b) and the speed (nA) of the exhaust gas turbine (1b) - preferably continuously - are measured, that from the measured data, the efficiencies (nv) of the compressor (1a) and the exhaust gas turbine (1b) are calculated, and that a diagnostic algorithm is started at a detected deterioration of the efficiency (nTV, nT) of the compressor (1a) and / or the exhaust gas turbine (1b) or after the expiry of a defined time interval.
    公开号:AT513137A4
    申请号:T50389/2012
    申请日:2012-09-13
    公开日:2014-02-15
    发明作者:Christoph Pfister；Hinrich Mohr；Ruediger Teichmann；Christian Strasser；Wilhelm Gutschi
    申请人:Avl List Gmbh；
    IPC主号:
    专利说明:

    1 56485
    The invention relates to a method for monitoring at least one exhaust gas turbocharger of a large internal combustion engine, with at least one compressor and a shaft arranged turbocharger, the actual pressures upstream and downstream of the compressor and the current temperatures upstream of the compressor and upstream of the exhaust gas turbine are measured.
    The description of an exhaust gas turbocharger and its current operating point are usually given via a so-called compressor power card.
    By measuring the pressure ratio at the compressor, the volumetric flow through the compressor and the exhaust gas turbocharger speed, one can infer the actual operating point and (for monitoring applications) the efficiency. A measurement of the pressure ratio together with the volume flow and / or the rotational speed is provided, for example, in US Pat. No. 6,298,718 B1 or DE 10 2004 059 369 A1. Together with the power card can thus a control (as in US 6 2 98 718 Bl) or a monitoring (as in DE 10 2004 059 369 Al) of the compressor side can be realized, which is mainly on the edge areas, in particular on surge limit and Demolition characteristic, pay attention.
    From DE 10 2005 001 659 Al a method and a system for determining the service life of an exhaust gas turbocharger is known. In this case, the duty cycle of the exhaust gas turbocharger is monitored. The known method uses for the measurements the engine speed, the pressure before and after the compressor, as well as the compressor temperature at the inlet and the temperature at the turbine inlet. The turbine speed is not measured directly, but it is concluded from the ratio of the inlet pressure to the outlet pressure of the compressor to the turbine speed. However, effective monitoring of the turbine side is not possible.
    In many cases volume flow data or the like are not available for large engines, since the volume flow is measured neither on the compressor side nor on the turbine side.
    The object of the invention is to monitor an exhaust gas turbocharger - not only the compressor side, but also the turbine side - effectively and in the simplest possible way. 2
    According to the invention this is achieved in that the current pressures upstream and downstream of the exhaust gas turbocharger, the temperatures downstream of the compressor and downstream of the exhaust gas turbine and the speed of the exhaust gas turbine - are measured - preferably continuously - that calculates the efficiencies of the compressor and the exhaust gas turbine from the measured data be, and that when a detected deterioration in the efficiency of the compressor and / or the exhaust gas turbine, a diagnostic algorithm is started.
    Preferably, the diagnostic algorithm provides that at least one measured value recorded at different engine loads is normalized. Preferably, whenever possible, each measured value is normalized. For the normalized measured value, a regression analysis over time can be carried out, wherein - particularly advantageously - a weighting of the measured values is carried out. In this case, younger measured data can be weighted higher than older measured values.
    The result of the regression analysis of the measured value can be assigned a symptom. From the combination of several symptoms of different measured values finally a defined error can be determined.
    In the present method, the compressor performance map is not included for the diagnosis of the component, because in the case of gnous motors, the volume flow through the exhaust gas turbine is generally not measured. It may also be that the compressor performance card is not available. The compressor performance card may however be used for the visualization of the operating point. While known systems are limited to monitoring the compressor side, the turbine side is also monitored by the method according to the invention. Therefore, pressure and temperature sensors are also installed on the turbine side. In addition, cylinder pressure sensors can also be provided in order to detect the operating point of the engine. In this case, sensors of anyway provided engine monitoring systems can also be used. The present method can thus supplement existing engine monitoring systems with respect to the - not yet carried out - monitoring of the exhaust gas turbine. By installing sensors on the turbine side, the exhaust gas turbine can now be monitored directly. This 3 can be particularly useful for large engines to detect contamination early and plan the cleaning of the exhaust turbine and optimize.
    It is particularly advantageous if at least one degree of manifestation of the error is calculated from the combination of several symptoms.
    The method according to the invention is able to use measurement data to: - diagnose the presence of this fault for one or more faults of an exhaust gas turbocharger or one of its subsystems - to determine a severity or degree of severity for the abovementioned faults - from a plurality of possible faults and, if applicable, their severity to determine an overall state of the exhaust gas turbocharger and / or one of its subsystems (eg compressor, exhaust gas turbine, etc.) - even in the absence and / or implausibility of one or more measurement signals to make statements about at least some possible errors.
    In this case, the method according to the invention is so flexible that: both an extension to new errors and / or new measuring signals and a connection between empirical expert knowledge and functional knowledge is possible; - Despite the mutual influence of the errors isolation and evaluation of individual errors is possible.
    Preferably, it is provided that the overall state of the exhaust gas turbocharger and / or its subsystems is determined and classified from the errors and their degrees of expression.
    Simple error detection is possible if each error is identified by means of an error symptom routine - for example a flowchart - the error symptom routine for each symptom derived from the deviations and for each fault to which the exhaust gas turbocharger and / or examines its subsystems, the relationship between symptom and error contains. 4
    In a further embodiment of the invention, it is provided that for each error and for each relevant symptom for this symptom
    Error probability function FW is defined with which, depending on the size of the symptom, a probability is calculated, with which the symptom contributes to the error, preferably the error based on the error probability Wj, calculated as the sum of all error sub-probabilities W | k normalized to the sum of all maxima Aik the error part Probability functions: n
    (i) are detected. A simple embodiment of the invention provides that the error probability function FW is a simple ramp or sigmoid function that increases linearly from the value zero to the amplitude Au in the specific range of symptom size.
    A particularly detailed fault diagnosis can be achieved if at least two different degrees of expression of at least one fault can be detected.
    In order to avoid a falsification of the measurement result, it is provided that those symptoms, which can not be calculated due to non-existent and / or implausible measurement values, are rejected in the detection of errors, and that an error is classified as not recognizable if the Weight of all unpredictable symptoms is greater than a defined threshold, preferably the weight Pi of all non-calculated symptoms as the sum of the maxima Aik of all corresponding error probability components FW normalized to the sum of all maxima
    (3) 5 is calculated. Moreover, it is advantageous if a variable Gz, which maps the overall state of each individual subsystem of the exhaust gas turbocharger by the formula 5 m
    max (K;) G, - mim 1; ≫ (4), where Kj is a weighting factor that evaluates the importance of error V for state of subsystem " z " The magnitude Wiz reflects the overall probability of the first degree of the error " i " in the subsystem " z ", and the function H (x) is a filtering function that ensures that the error is not taken into account until it is called " probable " and that the state of the subsystem is " faulty " is defined when the size Gz is greater than a defined threshold value Gs.
    The overall condition of the exhaust gas turbocharger can be determined from the state of its subsystems. In order to further increase the significance of the method, it is particularly advantageous if an overall condition of the exhaust-gas turbocharger and / or its subsystems is classified as un-evaluable if the weight of the unrecognizable errors is greater than a threshold value.
    The invention will be explained in more detail below with reference to FIG.
    It show schematically
    1 shows the basic measurement setup for carrying out the method according to the invention,
    2 shows the sequence of fault detection of a fault (decreasing compressor power) using an example,
    3 shows an example of an error partial probability function FW;
    Figure 4 is a flow chart for the classification of a single error for the two severity levels; 6
    Fig. 5 is an example of a flowchart for the classification of the overall state of the internal combustion engine or a subsystem
    6 shows an example of the filter function used in calculating the overall state of the individual subsystems of the exhaust gas turbocharger;
    Fig. 7 is a flowchart for the classification of the overall condition of the exhaust gas turbocharger.
    Measurement setup fFio. 1 ^
    The test setup for carrying out the method according to the invention for monitoring an exhaust gas turbocharger ATL of a large internal combustion engine BKM is shown schematically in FIG. For monitoring a monitoring computer 3, a database 4, a measuring hardware 5 and a number of transducers (sensors) 6a, 6b, 7 to 16 is needed. Specifically, pressure sensors 6a, 6b, 9, 11, 13 and 15; Temperature sensors 10, 12, 14, 16; and speed sensor 7, 8 eingestzt. The following parameters are measured for the monitoring of the exhaust gas turbocharger ATL: • pressure pi, p2; Ρ4, Ps and temperature Ti, T2, T4, T5 before and after the compressor la and before and after the exhaust gas turbine lb via the sensors 9 to 16. The pressure p2 and the temperature T2 is measured upstream of a charge air cooler. • Exhaust gas turbocharger speed Πα via speed sensor 8 • Engine speed n via crank angle sensor 7 • Cylinder pressure p at at least two cylinders Z via pressure sensor 6a, 6b
    The measured data are sent via a measuring hardware 5 to the monitoring computer 3, where they can be processed and displayed accordingly. 7
    Diaanoseal disorder fFia. 2Ί
    According to the present method, when the diagnostic algorithm is called up, the measurement data of earlier measurements are read from the database 4. Together with a current measurement, it is determined how the measured quantities change over time. By certain combination of these changes can then be pointed to various errors, which in turn give information about the state of the exhaust gas turbocharger ATL.
    In detail, the pressures pi, P2; p4, Ps and temperatures Ti, T2, T4, T5 and rotational speeds n, nA continuously measured and sent via the measuring hardware 5 to a monitoring computer 3. On the monitoring computer 3, these can then be displayed the same. Furthermore, the efficiency of the compressor 1a, the exhaust gas turbine 1b and the exhaust gas turbocharger ATL can be calculated from the measured data. This gives a first indicator about its condition, but serves only as an input variable and is not used for a direct statement about errors.
    The diagnostic algorithm itself can be started according to various criteria, for example after the expiry of a certain time interval or even when the efficiency of compressor la or exhaust gas turbine lb seems to be worsening.
    When the diagnostic algorithm starts, the measured data is recorded. The internal combustion engine BKM should be in a stationary operating state so that an average value can be calculated from the measured values over time. All of these averages are then written to the database 4 together with a time stamp. Thus, an entry in the database 4 consists of the average values of the measured pressures pi, p2; p4, Ps, temperatures Ti, T2, T4, Ts and rotational speeds n, nA, as well as other characteristic quantities and data for the identification of the exhaust gas turbocharger ATL (several exhaust gas turbochargers can be monitored simultaneously).
    A first peculiarity of the present method is the way of analyzing this data over time. The diagnostic algorithm reads all the data of the exhaust gas turbocharger ATL to be examined, which were recorded during a certain time interval (for example, over a month), from the database 4. Since FIG. 8 shows the different measured values at different engine loads (and thus also at different operating points of the exhaust gas turbocharger ATL). they are first normalized '. For example, the ratio of the outlet pressure p2 to the inlet pressure pi of the compressor la may be represented as a function of the exhaust gas turbocharger speed nA. Thus, the measurement data for the outlet pressure p2 of the compressor la can be made independent of the exhaust gas turbocharger rotational speed nA, ie be normalized by this function. This then further allows a regression analysis of these measurement data over time. Here further modifications can be made. For example, more up-to-date measurement data may be weighted higher than those whose record has been older. The result of the regression analysis is called a symptom and corresponds to the development of the corresponding measurand over time.
    The core of the diagnostic algorithm is the combination of various symptoms to calculate defined errors. Fig. 2 shows a typical procedure for the detection of a fault, wherein as an example a reduced outlet pressure p2 of the compressor la is detected. This drop in outlet pressure p2 can have various causes. It can be caused either by the compressor la itself or caused by a leak after the compressor la. Each of the boxes of the block diagram in Fig. 2 represents a potential symptom Sl, S2, S3, ... Sn. These symptoms Sl, S2, S3, ... Sn are now combined according to the block diagram to give a probability of the error to obtain. In this case, a method for calculating the error probability, as well as their classification and further the determination of the states of subsystems and ultimately the state of the entire exhaust gas turbocharger ATL is used, which is described in detail in AT 502 913 Bl. The content of AT 502 913 Bl is hereby expressly incorporated by reference into the present application.
    Explanation to Fia. 2:
    In the example shown in Fig. 2, the parameters input side pressure pi of the compressor la, output side pressure p5 of the exhaust gas turbine lb input side temperature Ti of the compressor la, temperature T3 after intercooler, engine speed n, fuel air ratio X engine of the internal combustion engine BKM, geometry of the exhaust gas turbocharger ATL and input temperature T «of the 9
    Exhaust gas 1b assumed to be constant. Furthermore, it is assumed that only small changes occur, so that the respective densities can be regarded as constant.
    Sl: The decrease of the output side pressure P2 of the compressor 1a could be attributed to decreasing compressor power or to leakage in the charge air line. S2: P2 / P1 decreases because the input pressure pi of the compressor la remains constant. S3: The turbocharger efficiency tiatl decreases as the compressor power decreases. S4: The compressor mass flow mv decreases due to a lower compressor speed or exhaust gas turbocharger speed nA. S5, S6, S7: The isentropic efficiency of the compressor η5ν can - depending on the position of the previous operating point in the compressor map - increase, remain the same or decrease. S8, S9, S10, Sil: the output-side temperature T2 of the compressor la can, depending on Τι, Ρ2 / Ρ1 and η5ν - assume, remain the same or take. S12: Mass flow me by internal combustion engine decreases, since air-fuel ratio λΜ (* 0Γ constant) S13: The exhaust gas temperature T4 remains constant, since the fuel-air ratio aotor and the engine speed n remain constant S14: The pressure ratio P4 / P5 between Input and output side of the exhaust gas turbine 1b decreases because of a smaller compressor pressure ratio Ρ2 / Ρ1 (power balance) S15: The pressure p4 upstream of the exhaust gas turbine 1b decreases because the pressure p5 downstream of the exhaust gas turbine 1b remains constant S16: The turbine mass flow mj decreases, the turbine efficiency decreases as a function of the turbine mass flow mT, T4, p4, p4 / ps, and the turbocharger efficiency ηΑτι_ S18: Depending on the temperature T4 upstream of the exhaust gas turbine lb, the turbine pressure ratio P4 / P5, and of the turbine efficiency ητ, the temperature T5 rises downstream of the exhaust gas turbine 1b.
    In the method according to the invention, based first on expert knowledge, experimental and theoretical investigations and simulations of mathematical models, the features (for example measuring signals) are identified which react particularly strongly to the errors to be detected. The symptoms Sl, S2, S3, ... Sn, i. the error-related deviations of these characteristics from associated reference values form the basis for the subsequent fault diagnosis. The reference values are either nominal values which were measured on a non-defective turbocharger or model values from mathematical simulations of the normal process.
    In a first, particularly simple embodiment of the method, the errors F1, F2, F3,... Fm are identified on the basis of an error symptom routine, for example corresponding flow diagrams, for which an example is shown in FIG. For each symptom SI, S2, S3,... Sn for the corresponding fault of the exhaust gas turbocharger 1, this table contains the relationship between symptom and error. For each error Fl, F2, F3,... Fm recognizable by the method according to the invention, it is then calculated whether the symptoms S1, S2, S3,... Sn correspond to the conditions of the error symptom routine (flowchart). If all or at least a large proportion of the symptoms correspond to these conditions, the method recognizes the error as being present. For the example of FIG. 2, this clearly indicates that if the amount of symptom Sl (compressor pressure p2) deviates negatively, symptom S2 (p2 / p2) is also negative and the other symptoms also deviate in accordance with the indicated arrows, the error F1 is recognized , regardless of the value of other symptoms not listed in the flowchart.
    An advantageous development of this method determines at least for some errors more than one degree of expression. For example, a first state of occurrence of an error may be said to be " faulty " or " yellow " and a second excursion wheel of the same error as state " Critical " or " red " be determined. It is particularly advantageous if the threshold values are greater for the second degree of expression of the error than for the first degree of expression of the error. Thus, the second degree of manifestation of an error is achieved with larger deviations between feature and reference value than the first degree of expression.
    In a second embodiment of the method according to the invention, instead of a simple but rigid flowchart, a probability of an error is calculated from the symptoms. For this purpose, for each error whose number is denoted by the index i and for each symptom relevant for this error, whose index is denoted by j, an error probability probability function FW is defined. With this error partial probability function FW, for which an example is shown in FIG. 3, a probability Wy is calculated for each error i depending on the size of the symptom Sj, with which the symptom Sj contributes to the error i. This takes into account the fact that an error can occur even if several features each have a medium deviation from their respective reference values. This measure reduces the sensitivity of the diagnostic results to measurement inaccuracies and noise.
    The value of the error probability component function FW is always greater than or equal to zero, the maximum of this function is named Ay. The amplitude Ay corresponds to the importance of the symptom Sj for detecting the error " i ". The more clearly the relationship between the error " i " and the symptom " j " is, the greater the amplitude Ay in comparison to other amplitudes Aik, (k = l..n). The size Ay is determined on the basis of the expert knowledge and / or simulation of the process model.
    In a particularly advantageous embodiment, which is illustrated in FIG. 3, the error probability function FW is a simple ramp or sigmoid function which increases linearly from the value zero to the amplitude Ay in the range of the symptom size from Vj to Uj.
    In a second step, the total probability Wj of the error V is then calculated by summing the error probability for all symptoms W | k (k = l..n) normalized to the sum of all the amplitudes A *: 12wt Σ " * * = 1
    CD t = i
    If some of the required measurement data is not present or not plausible, so that it is not possible to detect a symptom Sk, all errors " i " the probabilities Wik in sum (1) set to zero.
    Also in this second embodiment of the method according to the invention, an advantageous development is to consider different degrees of expression of the errors. In this case, the error, for example a first degree of expression "yellow" for each degree of expression, then becomes. and a second expressiveness " red ", defined error probability probability functions. In this case, according to the value W introduced above, the value of the second error probability function for the second degree of expression of the errors is denoted by Eq. The maximum of the second error probability function Ejj is denoted by Bq. It is particularly advantageous if, for the same size of the symptom Sj, the value of the second error probability probability function Eg is always less than or equal to the value of the first error probability probability function Wij. Thus, the second degree of manifestation of the error is detected with larger deviations between feature and reference value than the first degree of expression.
    Similarly, the total probability Ei of the second degree of expression of the error " i " as the sum of all Eik (k = l..n), normalized to the sum of all amplitudes Bjk of the second error probability components, calculated:
    (2)
    As with the first error probabilities, the quantities E "< in sum (2) set to zero if some symptoms Sk can not be determined because of missing or implausible measurement data. 13 It is easy for the person skilled in the art to recognize that with the scheme described above, further degrees of expression of the errors can be calculated.
    A further advantageous further development of the method according to the invention now classifies the individual identifiable errors according to one or more error classes. This can be done particularly simply if an error i is classified as present if its probability Wj is greater than a threshold value Ws.
    However, it is even more advantageous to classify the errors into several classes for several degrees of expression. Fig. 4 exemplifies how an error, which may exist in two states of expression, is classified into 4 classes Al, A2, A3, A4. These 4 classes can e.g. be: Al- " dangerous " or " red ", A2 " likely " or " yellow ", A3 " not recognizable " or " gray " and A4- " unlikely " or " green ".
    The classification process begins (beginning at " 0 ") with the calculation of the probability Wi for the first degree of the error (step 21) and the calculation of the probability Ej for the second degree of the error (step 22). Thereafter, in step 23, the probability Ej for the second degree of the error is compared with a second threshold Es (for example, Es = 0.9). If Ej is higher than ES (Y), the error " i " as " dangerous " classified (Al). If this is not the case, then it is checked in step 24 whether the probability Wj for the first expression level of the error is greater than a first threshold value Ws (for example Ws = 0.7). If Wj > If it is, then the error " i " as " likely " classified (A2). Otherwise, it is judged whether or not the value of Wj is small due to missing or implausible measurement data (referred to as 'np' data). For this, in step 25, the weight Pj of all symptoms not calculated is determined as the normalized sum of all corresponding amplitudes Aik:
    λ = 1 (3)
    If the value of P, is greater than a predefined threshold Ps (for example, Ps = 0.6), it means that the error " i " based on existing 14
    Measurement data can neither be detected nor excluded. In this case, the error " i " as " not recognizable " (A3) (step 26 in FIG. 4). If none of the previous checks in steps 23 through 26 have a positive result, the error is considered " unlikely " (A4) classified. With " 1 " is the end of the first process called. It will be readily apparent to those skilled in the art how to extend the method described above to even more levels of error and / or other classes.
    The last step of the method according to the invention calculates an overall condition of the exhaust-gas turbocharger ATL from individual errors which may occur in one or more degrees of expression and / or classes. For the most relevant case, the individual errors are classified as dangerous " (Al), " likely " (A2), " not recognizable " (A3) and " unlikely " (A4), Fig. 56 shows the flowchart for the classification of the exhaust gas turbocharger ATL or a subsystem. Such a subsystem may be the compressor or the exhaust gas turbine. In the following explanation, only one subsystem " z " the exhaust gas turbocharger ATL reference, wherein the subsystem but also the entire exhaust gas turbocharger ATL can be.
    In a first step (31), query VI checks to see if at least one of the subsystem errors is " dangerous " (Al) was classified. In this case (Y), the entire subsystem is called " critical " (Bl) classified. If the result of this check is negative (N), in a second step (32) a variable Gz is calculated, which represents the overall state of the subsystem "z": m
    max (£ T)
    Gz = min- {1; - (4)
    Where K is a weighting factor that indicates the importance of the error " i " for the state of the subsystem " z " reflects. The size of Kj is determined based on expert knowledge and / or simulation of the process model. The quantity Wlz is the total probability Wi of the first degree of expression of the error " i " in subsystem " z ". The function H (x) is a filter function that ensures that the error is not taken into account until it is called " probable " 15 (i.e., only when Wj> Ws) and that the error is fully considered only when the error probability is large enough (e.g., Wj> 0.9). An example of a filter function H is shown in FIG. If the error " I " as " not recognizable " it is not included in Gz: H (Wiz) = 0.
    In step 33 in Fig. 5, the quantity Gz is compared with a predefined threshold value Gs (for example, Gs = 0.7). If Gz is higher than Gs, the state of the subsystem " z " as " erroneous " (B2) defined. If this is not the case, then it is judged whether the value of Gz is small due to any unrecognizable errors. For this, the weight Xj of unrecognizable One ') errors is calculated in step 34 as the normalized sum of all corresponding weights Kj:
    (5)
    If the value of Xz is greater than the predefined threshold Xs (for example, Xs = 0.3), the overall state of the subsystem " z " as " not assessable " (B3) (step 35 in Fig. 45). Otherwise, the overall state of the subsystem " z " as " healthy " (B4) classified. With " 2 " is the end of the routine called.
    The person skilled in the art will easily recognize here how the method of classifying a subsystem can also be extended to cases with further states of expression of the errors and / or further error classes.
    In a last advantageous embodiment of the method according to the invention, the condition of the entire exhaust gas turbocharger ATL is calculated from the state of the subsystems of the exhaust gas turbocharger ATL. An example of a flowchart of this calculation is shown in FIG.
    If at least one of the subsystems is " critical " (Bl), the overall condition of the exhaust gas turbocharger 1 in the query V2 is also called " critical " (Cl) classified (step 41 in Fig. 7). Otherwise, in step 42, the weight Nf of the faulty subsystems of subsystems is calculated using the weighting factors Dk 16, which determine the importance of the state of each subsystem " k " on the overall condition of the exhaust gas turbocharger ATL represent:
    k = all subsystems (6)
    In step 43 it is checked if the weight of the subsystems is rated " faulty " Nf greater than the predefined threshold Ν & (for example, Ν & = 0.3). In this case, the overall condition of the exhaust gas turbocharger ATL is also called " faulty " (A10). Otherwise, in step 44, the weight N (nb) of the non-evaluable subsystems is calculated:
    All subsystems (7)
    In step 45, it is checked whether the weight of the subsystems is rated " not assessable " N (nto is greater than a threshold N (ni,) s (for example, N (nb) s = 0.3) If this is the case, the overall condition of the exhaust gas turbocharger ATL is also evaluated as " un-evaluable " (C3) Otherwise, the overall condition of the exhaust gas turbocharger is classified as " healthy " (C4). &Quot; 3 ", the end of the process is designated, and it will be readily apparent to those skilled in the art how to classify the condition of the entire exhaust gas turbocharger ATL Cases with more error classes can be extended.
    权利要求:
    Claims (18)
    [1]


    1. A method for monitoring at least one exhaust gas turbocharger (ATL) of a large internal combustion engine (BKM), comprising at least one compressor (la) and a shaft-mounted exhaust gas turbine (lb), wherein the actual pressures (plf p2) upstream and downstream of the compressor (la) and the actual temperatures (Ti, T4) upstream of the compressor (la) and upstream of the exhaust gas turbine (lb) are measured, characterized in that the actual pressures (p4, Ps) upstream and downstream of the exhaust gas turbine (lb), the Current temperatures (T2, T5) downstream of the compressor (la) and downstream of the exhaust gas turbine (lb) and the speed (nA) of the exhaust gas turbine (lb) - preferably continuously - are measured, that from the measured data, the efficiencies (ην) of the compressor (la) and the exhaust gas turbine (lb) are calculated, and that a diagnostic algorithm with determined deterioration of the efficiency (η ™, ητ) of the compressor ( la) and / or the exhaust gas turbine (lb) or after a defined time interval is started.
    [2]
    2. The method according to claim 1, characterized in that the diagnostic algorithm provides that at least one recorded at different engine loads measured value is normalized.
    [3]
    3. The method according to claim 2, characterized in that a regression analysis over time is performed for the normalized measured value.
    [4]
    4. The method according to any one of claims 2 or 3, characterized in that a weighting of the measured values is carried out, wherein preferably younger measured data are weighted higher than older measured values.
    [5]
    5. The method according to claim 3 or 4, characterized in that the result of the regression analysis of the measured value is associated with a symptom.
    [6]
    6. The method according to claim 5, characterized in that the symptom for an error from the deviation between a from the measurement signal

    18 obtained feature and a reference value for this feature is obtained.
    [7]
    7. The method according to claim 4 or 5, characterized in that from the combination of several symptoms of different measured values, a degree of expression of a defined error is determined.
    [8]
    8. The method according to claim 7, characterized in that the overall state of the exhaust gas turbocharger (ATL) and / or its subsystems from the errors and their degrees of expression is determined and classified
    [9]
    A method according to claim 7 or 8, characterized in that each error is described on the basis of an error symptom routine, preferably a flowchart established on the basis of the error symptom routine, the error symptom routine being for each the symptom obtained from the abnormalities and for each error the relationship between symptom and error.
    [10]
    10. The method according to any one of claims 7 to 9, characterized in that for each error and for each symptom relevant to this error, an error probability function FW is defined, with which depending on the size of the symptom a probability is calculated, with which the symptom for Error contributes.
    [11]
    11. The method according to claim 10, characterized in that the errors based on the error probability Wj, calculated as the sum of all error part probabilities Wik normalized to the sum of all maxima Aik of the error probability components:

    (1) are detected.
    [12]
    12. The method according to claim 10, wherein the error probability function FW is a simple ramp or sigmoid function which increases linearly from the value zero to the amplitude Ay in the specific range of symptom size.
    [13]
    13. The method according to any one of claims 7 to 12, characterized in that at least two different degrees of expression of at least one error can be detected.
    [14]
    14. The method according to any one of claims 5 to 13, characterized in that those symptoms that can not be calculated due to non-existent and / or implausible measured values are rejected in the detection of errors, and that an error is classified as not recognizable when the weight of all unpredictable symptoms becomes greater than a defined threshold.
    [15]
    15. The method according to claim 14, characterized in that the weight P, of all non-calculated symptoms as the sum of the maxima Aik of all corresponding error probability components FW normalized to the sum of all maxima Aik:

    Σ4 * k = all (np) data (3) is calculated.
    [16]
    16. The method according to any one of claims 1 to 15, characterized in that a variable Gz, which maps the overall state of each subsystem of the exhaust gas turbocharger, by the formula m Σκ > - " ft) G2 = min i = 1 (4) max (X.) where Kj is a weighting factor that determines the importance of the error " i " for a state of the subsystem " z " the magnitude Wiz is the overall probability of the first manifestation of the error " Γ in the subsystem " z ", and the function H (x) is a filtering function which ensures that the error is not considered until "; likely " and that the state of the subsystem is " faulty " is defined when the size Gz is greater than a defined threshold value Gs.
    [17]
    17. The method according to any one of claims 1 to 16, characterized in that an overall state of the exhaust gas turbocharger (ATL) and / or its subsystems is classified as not assessable if the weight of the unrecognizable error is greater than a threshold value.
    [18]
    18. The method according to any one of claims 1 to 16, characterized in that the overall state of the exhaust gas turbocharger (ATL) is calculated from the state of its subsystems. 2012 09 13 Fu
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    同族专利:
    公开号 | 公开日
    EP2895705A1|2015-07-22|
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    WO2014041013A1|2014-03-20|
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    US20150275753A1|2015-10-01|
    US10060346B2|2018-08-28|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US6298718B1|2000-03-08|2001-10-09|Cummins Engine Company, Inc.|Turbocharger compressor diagnostic system|
    DE102004059369A1|2003-12-18|2005-07-14|Caterpillar Inc., Peoria|Engine turbocharger control management system|
    US20050193810A1|2004-03-02|2005-09-08|Gladden John R.|Method and system of determining life of turbocharger|
    US20100023369A1|2008-07-24|2010-01-28|Scavengetech I, LLC|Turbocharger fleet management system|
    US6543227B2|2001-01-31|2003-04-08|Cummins Engine Company, Inc.|Automated active variable geometry turbocharger diagnosis system|
    US6785604B2|2002-05-15|2004-08-31|Caterpillar Inc|Diagnostic systems for turbocharged engines|
    JP4244979B2|2005-09-22|2009-03-25|トヨタ自動車株式会社|Supercharging pressure control device for internal combustion engine|
    US7380445B2|2006-06-30|2008-06-03|International Engine Intellectual Property Company, Llc|Turbocharger performance qualification method and apparatus|
    WO2008090021A2|2007-01-25|2008-07-31|Avl List Gmbh|Method for the diagnosis and classification of defects of an internal combustion engine|
    AT502913B1|2007-01-25|2008-05-15|Avl List Gmbh|METHOD FOR DIAGNOSIS AND CLASSIFICATION OF FAULTS OF AN INTERNAL COMBUSTION ENGINE|
    WO2009002233A1|2007-06-26|2008-12-31|Volvo Lastvagnar Ab|Charge air system and charge air system operation method|
    AT10073U9|2008-01-14|2009-02-15|Avl List Gmbh|METHOD AND DEVICE FOR ANALYZING AND EVALUATING MEASUREMENT DATA OF A MEASURING SYSTEM|
    AT505836B1|2009-01-19|2011-05-15|Avl List Gmbh|METHOD FOR OPERATING AN INTERNAL COMBUSTION ENGINE|
    IT1403512B1|2010-10-11|2013-10-31|Magneti Marelli Spa|METHOD OF CHECKING THE SPEED OF AN INTERNAL COMBUSTION MOTOR SUPPLIED BY A TURBOCHARGER.|
    FR2980525B1|2011-09-26|2013-08-30|Renault Sa|METHOD AND SYSTEM FOR DIAGNOSING A MOTOR PUMPS GROUP WITH TWO TURBOCHARGERS.|AT517548B1|2015-08-05|2017-10-15|Ge Jenbacher Gmbh & Co Og|Internal combustion engine with a turbocharger|
    US9765690B2|2015-09-30|2017-09-19|Deere & Company|Variable geometry turbocharger prognostics|
    GB2591776A|2020-02-06|2021-08-11|Caterpillar Inc|Improvements in turbocharger efficiency|
    法律状态:
    优先权:
    申请号 | 申请日 | 专利标题
    ATA50389/2012A|AT513137B1|2012-09-13|2012-09-13|Method for monitoring at least one exhaust gas turbocharger|ATA50389/2012A| AT513137B1|2012-09-13|2012-09-13|Method for monitoring at least one exhaust gas turbocharger|
    EP13762454.0A| EP2895705B1|2012-09-13|2013-09-11|Process for monitoring of at least one turbocharger|
    PCT/EP2013/068789| WO2014041013A1|2012-09-13|2013-09-11|Method for monitoring at least one exhaust gas turbocharger|
    US14/427,479| US10060346B2|2012-09-13|2013-09-11|Method for monitoring at least one exhaust gas turbocharger|
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