Converter fault diagnosis method and device, storage medium and electronic equipment

Abstract

The application relates to the technical field of fault diagnosis, in particular to a converter fault diagnosis method and device, a storage medium and an electronic device, which comprises the following steps: performing structure analysis on a target traction converter which has completed structure optimization to obtain a first structured model of the target traction converter which has completed structure optimization; determining at least one set of structure minimum overdetermined equations for the first structured model; determining the residual error of each set of structure minimum overdetermined equations; calculating the detection value corresponding to each residual error; comparing the detection value with a corresponding detection threshold value to obtain a diagnosis result. The application can isolate and detect various faults in the traction converter through structure optimization of the traction converter, and realizes accurate online fault diagnosis through real-time determination of the detection value.

Description

Technical Field

[0001] This application relates to the field of fault diagnosis technology, and in particular to a method, apparatus, storage medium and electronic equipment for diagnosing converter faults. Background Technology

[0002] In traction control systems, to achieve effective control, anomaly detection, and protection of locomotive and EMU transmission systems, it is necessary to collect various sensor signals such as voltage, current, and speed from the main circuit. Sensor failures can lead to system control failure, false alarms, and false protection, severely impacting locomotive availability. In electric traction transmission systems, traction converters, due to their complex structure and the significant influence of external environment and operating conditions on components such as sensors, have a relatively high failure rate. Statistical analysis shows that sensor failures in traction converters currently account for approximately 25% of the overall traction system failure rate.

[0003] Therefore, achieving accurate online diagnosis of various sensors related to traction converters, and timely and accurate diagnosis and isolation of faulty components under abnormal operating conditions, is of profound significance for improving the availability of locomotives and EMUs and reducing the waste of human and material resources caused by accidents such as shutdowns and machine breakdowns due to faults. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a converter fault diagnosis method, apparatus, storage medium, and electronic device, which solves the technical problem in related technologies that prevents accurate online fault diagnosis by sensors.

[0005] In a first aspect, this application provides a converter fault diagnosis method, the method comprising:

[0006] Structural analysis is performed on the target traction converter that has undergone pre-optimized structure to obtain the first structured model of the target traction converter that has undergone pre-optimized structure.

[0007] For the first structured model, at least one set of structurally minimal overdetermined equations is determined;

[0008] Determine the residuals for each set of minimal overdetermined equations for the structure;

[0009] Calculate the detection value corresponding to each residual;

[0010] The detected value is compared with the corresponding detection threshold to obtain the diagnostic result.

[0011] In some embodiments, the process of structural optimization of the target traction converter includes:

[0012] Structural analysis was performed on the target traction converter to obtain a second structured model of the target traction converter.

[0013] The second structured model is decomposed into DM to obtain the DM decomposition results;

[0014] Based on the DM decomposition results, determine whether all faults in the target traction converter can be isolated and detected;

[0015] If so, the target traction converter does not require structural optimization;

[0016] If not, then the target traction converter is optimized to isolate all payments within the target traction converter.

[0017] In some embodiments, determining at least one set of structurally minimal overdetermined equations for the first structured model includes:

[0018] Calculate all minimal overdetermined equations of the first structured model based on its mathematical model.

[0019] The minimum collision set method is used to process all minimal overdetermined equation sets, resulting in at least one structural minimal overdetermined equation set.

[0020] In some embodiments, the structural analysis of the pre-optimized target traction converter to obtain a first structured model of the pre-optimized target traction converter includes:

[0021] Based on the structure of the pre-optimized target traction converter, a first mathematical model of the pre-optimized target traction converter is obtained.

[0022] Based on the first mathematical model, the first structured model of the target traction converter that has undergone pre-optimized structure is obtained.

[0023] In some embodiments, the structural analysis of the target traction converter to obtain a second structured model of the target traction converter includes:

[0024] Based on the structure of the target traction converter, a second mathematical model of the target traction converter is obtained;

[0025] Based on the second mathematical model, the second structured model of the target traction converter is obtained.

[0026] In some embodiments, determining whether all faults in the target traction converter can be isolated and detected based on the DM decomposition results includes:

[0027] Based on the structural overdetermined portion of the DM decomposition results, determine whether various faults are detectable;

[0028] If it is determined that various faults are detectable, then according to the following formula:

[0029]

[0030] Determine whether the various faults can be isolated from each other;

[0031] In the formula, and Each contains a fault and The equation, To eliminate equations The overdetermined portion of the subsequent structure.

[0032] In some embodiments, calculating the detection value corresponding to each residual includes:

[0033] According to the formula:

[0034]

[0035] Calculate the detection value T corresponding to each residual R. 2 ,in, Satisfying the condition having N-1 degrees of freedom Standard distribution, and , Indicates the fault-free assumption Detection quantity under conditions Greater than The probability, Indicates the confidence level. It is related to the measurement noise and harmonics of the residual R.

[0036] Secondly, a converter fault diagnosis device, the device comprising:

[0037] The analysis unit is used to perform structural analysis on the target traction converter that has undergone pre-optimized structure, and to obtain the first structured model of the target traction converter that has undergone pre-optimized structure.

[0038] A determining unit is used to determine at least one set of structurally minimal overdetermined equations for the first structured model;

[0039] Design unit for determining the residuals of each of the minimum overdetermined equation sets of the structure;

[0040] A calculation unit is used to calculate the detection value corresponding to each residual;

[0041] The comparison unit is used to compare the detected value with the corresponding detection threshold to obtain a diagnostic result.

[0042] Thirdly, a storage medium storing a computer program that can be executed by one or more processors to implement the converter fault diagnosis method as described in the first aspect above.

[0043] Fourthly, an electronic device includes a memory and a processor, wherein a computer program is stored on the memory, and the memory and the processor are communicatively connected to each other, wherein when the computer program is executed by the processor, it performs the converter fault diagnosis method as described in the first aspect above.

[0044] This application provides a converter fault diagnosis method, apparatus, storage medium, and electronic device, comprising: performing structural analysis on a target traction converter that has undergone pre-optimized structure to obtain a first structured model of the pre-optimized target traction converter; determining at least one set of minimum overdetermined structural equations for the first structured model; determining the residual of each set of minimum overdetermined structural equations; calculating the detection value corresponding to each residual; and comparing the detection value with a corresponding detection threshold to obtain a diagnosis result. This application enables the isolation and detection of various faults in the traction converter through structural optimization, and achieves accurate online fault diagnosis through real-time determined detection values. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0046] Figure 1 A flowchart illustrating a converter fault diagnosis method provided in an embodiment of this application;

[0047] Figure 2 A typical main circuit schematic diagram of a traction converter provided in the embodiments of this application;

[0048] Figure 3 This is a schematic diagram of the second structured model of the traction converter provided in the embodiments of this application;

[0049] Figure 4 This is a schematic diagram of the DM decomposition region provided in an embodiment of this application;

[0050] Figure 5 This is a schematic diagram of the DM decomposition results of the traction converter model provided in the embodiments of this application;

[0051] Figure 6 This is a schematic diagram of a traction converter fault isolation matrix provided in an embodiment of this application;

[0052] Figure 7 This is a schematic diagram of the fault isolation matrix of the traction converter after adding a W-phase current sensor, provided in an embodiment of this application.

[0053] Figure 8 This is a schematic diagram of the structure of a converter fault diagnosis device provided in an embodiment of this application;

[0054] Figure 9 This is a connection block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0055] The following detailed description of the embodiments of this application, in conjunction with the accompanying drawings, will provide a thorough understanding of how this application uses technical means to solve technical problems and achieve corresponding technical effects, enabling its implementation. The embodiments of this application and the various features within them can be combined with each other without conflict, and all resulting technical solutions are within the protection scope of this application.

[0056] Example 1

[0057] Figure 1 This is a flowchart illustrating a converter fault diagnosis method provided in an embodiment of this application, as shown below. Figure 1 As shown, this method includes:

[0058] S101. Perform structural analysis on the target traction converter that has undergone pre-optimized structure to obtain the first structured model of the target traction converter that has undergone pre-optimized structure.

[0059] S102. Determine at least one set of structural minimal overdetermined equations for the first structured model;

[0060] S103. Determine the residuals of each set of minimum overdetermined equations for the structure;

[0061] S104. Calculate the detection value corresponding to each residual;

[0062] S105. The detected value is compared with the corresponding detection threshold to obtain the diagnostic result.

[0063] It should be noted that this application is based on the optimization and diagnosis of a typical traction system for locomotives and EMUs. The typical main circuit schematic of the traction converter in the typical traction system is shown below. Figure 2 As shown, the structure of the traction converter is the optimized structure, and the original structure did not include the sensor LH4.

[0064] Specifically, such as Figure 2As shown, the traction system mainly consists of three parts: a traction transformer, a traction converter (including a charging circuit, a four-quadrant rectifier, an intermediate DC link, an inverter, etc.), and a traction motor. Single-phase 25kV AC power flows into the car body through the pantograph, the main circuit breaker VCB, and the primary winding of the traction transformer. The secondary winding of the traction transformer provides AC power to the converter circuit. The AC current is converted into DC power by the four-quadrant rectifier, filtered by the intermediate DC link, and then converted into three-phase AC power with variable frequency and amplitude by the inverter to drive the traction motor, thereby controlling the locomotive to move forward at different speeds and traction forces.

[0065] The main circuit operation process is mainly divided into four working conditions as shown in Table 1. The locomotive start-up process or fault restart process generally goes through working conditions (1) to (4) or several working conditions in between in sequence. Steady-state traction or braking operation will continue to work in working condition (4); when an overvoltage or overcurrent fault occurs, it will switch from one working condition to another. This invention is to accurately diagnose sensor faults (power supply voltage sensor TA, four-quadrant input current sensor LH1, intermediate voltage sensors VH1 and VH2, motor current sensors LH2, LH3 and LH4) that occur in the traction system under steady-state traction working conditions.

[0066]

[0067] Table 1

[0068] In some embodiments, the process of structural optimization of the target traction converter includes:

[0069] Structural analysis was performed on the target traction converter to obtain a second structured model of the target traction converter.

[0070] The second structured model is decomposed into DM to obtain the DM decomposition results;

[0071] Based on the DM decomposition results, determine whether all faults in the target traction converter can be isolated and detected.

[0072] If so, the target traction converter does not require structural optimization;

[0073] If not, then the target traction converter is optimized to isolate all payments within the target traction converter.

[0074] It should be noted that, in order to achieve real-time fault diagnosis of the traction converter, the diagnosability of the target traction converter must first be analyzed. Based on a typical traction converter circuit structure, this invention employs structural analysis to conduct a detailed analysis of the detectability and isolation of system sensor and component faults, and proposes a sensor layout scheme with maximum isolation as the objective, providing a foundation for subsequent fault diagnosis.

[0075] In some embodiments, the structural analysis of the target traction converter to obtain a second structured model of the target traction converter includes:

[0076] Based on the structure of the target traction converter, a second mathematical model of the target traction converter is obtained;

[0077] Based on the second mathematical model, the second structured model of the target traction converter is obtained.

[0078] Specifically, the target traction converter mainly consists of a four-quadrant rectifier, an intermediate circuit, an inverter, and corresponding sensors. Its second mathematical model is shown in the equation. As shown:

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098] In the formula: Equations 1 through 19 are represented.

[0099] Based on the second mathematical model, the second structured model of the traction converter is obtained as follows: Figure 3 As shown, the second structured model categorizes the variables in the second mathematical model into three types: unknown variables { }、 Fault Variables { } and known variables { },from Figure 3 The relationship between the equation and the variables can be seen in the figure. In the figure, D represents the differential variable relationship, and I represents the integral variable relationship. From the second mathematical model, it can be seen that the known variables are derived from the equation... The remaining variables in the equations that result in the measurement output are unknown variables.

[0100] In some embodiments, the structural analysis of the pre-optimized target traction converter to obtain a first structured model of the pre-optimized target traction converter includes:

[0101] Based on the structure of the pre-optimized target traction converter, a first mathematical model of the pre-optimized target traction converter is obtained.

[0102] Based on the first mathematical model, the first structured model of the target traction converter that has undergone pre-optimized structure is obtained.

[0103] It should be noted that the difference between the optimized first mathematical model of the traction converter and the unoptimized second mathematical model is the addition of equations. The other e1~e19 are the same as the second mathematical model.

[0104] The first structured model is based on the second structured model, adding [something] to the fault variables. The transformation process from the first mathematical model to the first structured model is the same as the transformation process from the second mathematical model to the second structured model.

[0105] In some embodiments, determining whether all faults in the target traction converter can be isolated and detected based on the DM decomposition results includes:

[0106] Based on the structural overdetermined portion of the DM decomposition results, determine whether various faults are detectable;

[0107] If it is determined that various faults are detectable, then according to the following formula:

[0108]

[0109] Determine whether the various faults can be isolated from each other;

[0110] In the formula, and Each contains a fault and The equation, To eliminate equations The overdetermined portion of the subsequent structure.

[0111] It should be noted that the redundancy relationships in the model can be derived using the mathematical tool DM decomposition table. DM decomposition is a mathematical tool that rearranges the rows and columns of a sparse matrix, similar to an upper triangular matrix, and can divide the system model into three distinct regions, such as... Figure 4 As shown.

[0112] The three areas are as follows:

[0113] The structurally indeterminate part M- means that the number of unknown variables exceeds the number of equations;

[0114] The positive definite part of the structure is M0, which means that the number of unknown variables is equal to the number of equations;

[0115] The overdetermined part of the structure is M+, meaning the number of unknown variables is less than the number of equations;

[0116] The structurally overdetermined part M+ is of most interest in fault diagnosis because it possesses analytical redundancy, which can be used for residual generation in fault diagnosis. If the fault in the equation is located in the structurally overdetermined part, it indicates that the fault is detectable.

[0117] Specifically, for Figure 2 The second structural model of the traction converter in the traction system is subjected to DM decomposition, and the normalized decomposition of its overdetermined structural part is as follows: Figure 5 As shown. From Figure 5 As can be seen, all the defined faults occur in the structural overdetermined part. Therefore, all sensor and component faults of the traction system listed in Table 1 are detectable.

[0118] Fault isolation refers to the ability to distinguish and isolate a fault from other faults when it occurs. Based on the definition of fault isolation, the fault isolation matrix can be obtained as follows: Figure 6 As shown.

[0119] from Figure 6 It can be seen that the fault variables , , , It is only related to itself, therefore it is an isolable fault, while the fault variable , Although it can be isolated from other faults, it cannot be isolated from each other, therefore it is a fault that cannot be completely isolated.

[0120] In order to achieve maximum isolation of various faults, that is, to ensure that all kinds of faults can be isolated, this application adds a current sensor for the W phase of the motor, which can increase the fault isolation matrix after the corresponding sensor fails, thereby improving the redundancy of the traction system and thus achieving the isolation of various faults.

[0121] Depend on Figure 7 It can be seen that when a motor W-phase current sensor is added... At this time, all faults in the system can be isolated from each other, achieving maximum isolation, and achieving maximum system isolation with the minimum number of sensors added.

[0122] Therefore, the structured model after optimizing the sensor layout is based on the formula with the following additional equation:

[0123]

[0124] In the formula, For the fault variable of the motor W-phase current sensor;

[0125] The optimized sensor layout scheme in the traction converter is as follows: Figure 2 As shown, a W-phase current sensor is added to the original scheme, i.e. Figure 2 LH4.

[0126] In some embodiments, determining at least one set of structurally minimal overdetermined equations for the first structured model includes:

[0127] Calculate all minimal overdetermined equations of the first structured model based on its mathematical model.

[0128] The minimum collision set method is used to process all minimal overdetermined equation sets, resulting in at least one structural minimal overdetermined equation set.

[0129] It should be noted that, in order to generate residuals for fault diagnosis, the structural minimal overdetermined equation set (MSOs) must first be determined, which is the minimum number of equations that achieves maximum isolation. MSOs are subsets of the set of equations with analytical redundancy. This invention calculates all 24 MSOs for the first structured model. Then, based on the minimum collision set method, six structural minimal overdetermined equation sets (MSOs) are obtained, as shown in Table 2, which lists each structural minimal overdetermined equation set and the equations contained within each set.

[0130]

[0131] Table 2

[0132] Because each MSO contains different equations, the faults that each MSO can detect will be different. Table 3 is a table of fault variables that MSOs can detect.

[0133]

[0134] Table 3

[0135] In Table 3, the symbol "X" indicates that the fault is detectable, and blank spaces indicate that the fault is not detectable. For example, MOS1 can detect two faults. and However, it cannot detect other faults.

[0136] It should be further explained that after obtaining the above six sets of structural minimal overdetermined equations, the residuals of each set of structural minimal overdetermined equations are determined by combining the redundancy relationships between components and the observer of the traction system. The specific process is as follows:

[0137] The equation set MOS1 consists of two equations used to generate residuals. The fault of the two intermediate voltage sensors is detected by the hardware redundancy relationship between them. The residual can be designed as follows:

[0138]

[0139] The equation set MOS2 consists of four equations used to generate residuals. The fault of the three-phase motor current sensor is detected by analyzing the redundancy relationship between the three-phase motor currents. The residual can be designed as follows:

[0140]

[0141] The equation set MOS3 consists of 14 equations used to generate residuals. Because of the equations in equation set MOS3 ~ Contains differential terms, therefore, based on ~ The observer method is used to design residuals. .

[0142] Specifically, take the state variable The residuals can be derived from the residuals. State-space representation format:

[0143]

[0144] In the formula, ; ; ; ; ; This is the observer feedback gain matrix.

[0145] The equation set MOS4 consists of 14 equations used to generate residuals. Because of the equations in the equation set MOS4 ~ Contains differential terms, therefore, based on ~ The observer method is used to design residuals. .

[0146] The equation set MOS5 consists of 6 equations Composition, through residuals To detect faults in the secondary voltage sensor, four-quadrant input current sensor, and intermediate voltage sensor. This is due to the equations in the MOS5 equation set. It contains differential terms, therefore, based on and combined The parsing redundancy relation produces a parsing redundancy relation:

[0147]

[0148] make

[0149]

[0150] Since the formula contains differential relationships, the residuals need to be filtered to obtain the residuals:

[0151]

[0152] In the formula, For differential operators, >0.

[0153] Then let

[0154]

[0155] Then we get:

[0156]

[0157]

[0158] As shown in Table 2, the equation set MOS6 consists of 17 equations. (The last part, "because the equations in equation set MOS6...", appears to be a fragment and doesn't translate directly.) ~ Contains differential terms, therefore, based on ~ The observer method is used to design residuals. .

[0159] Take state variables The residuals can be derived from the residuals. State-space representation:

[0160]

[0161] In the formula, ; ; ; ; ; This is the observer feedback gain matrix.

[0162] Thus, we obtain the six residuals R1~R6 corresponding to the six equation sets.

[0163] In some embodiments, calculating the detection value corresponding to each residual includes:

[0164] According to the formula:

[0165]

[0166] Calculate the detection value T corresponding to each residual R. 2 ,in, Satisfying the condition having N-1 degrees of freedom Standard distribution, and , Indicates the fault-free assumption Detection quantity under conditions Greater than The probability, Indicates the confidence level. It is related to the measurement noise and harmonics of the residual R.

[0167] It should be noted that after completing the residual design, the next step is to construct suitable detection quantities for fault detection. During normal operation, if the system is fault-free, the residual... satisfy ,in, , Related to measurement noise and harmonics in residuals, let... for The periodic sampled values.

[0168] Define the formula for calculating the detection value. ,

[0169] but Satisfying the condition having N-1 degrees of freedom Standard distribution

[0170] and In the formula, Indicates the fault-free assumption Detection quantity under conditions Greater than The probability of.

[0171] This paper uses this method for fault detection, and the threshold is obtained through an approximate chi-square distribution, that is:

[0172]

[0173] In the formula, Indicates the detection threshold. This represents a chi-square distribution with n degrees of freedom. This represents the confidence level, typically understood as the acceptable probability of false detection. The corresponding fault detection decision logic can be expressed as:

[0174]

[0175] It should be noted that, as shown above, for different detection values ​​T... 2 There will be corresponding detection thresholds. Let the detection values ​​corresponding to the 6 equation sets be respectively The detection thresholds are as follows: The fault flag bits for various faults are as follows: , , , , , and Based on the above analysis and the fault feature matrix in Table 4, fault diagnosis rules can be used to effectively detect each fault and obtain diagnostic results.

[0176]

[0177] Table 4

[0178] In Table 4, a fault flag of 1 indicates that a corresponding fault has occurred.

[0179] In summary, this application provides a converter fault diagnosis method, comprising: performing structural analysis on a target traction converter that has undergone pre-optimized structure to obtain a first structured model of the pre-optimized target traction converter; determining at least one set of minimum overdetermined structural equations for the first structured model; determining the residual of each set of minimum overdetermined structural equations; calculating the detection value corresponding to each residual; and comparing the detection value with a corresponding detection threshold to obtain a diagnosis result. This application enables the isolation and detection of various faults in the traction converter through structural optimization, and achieves accurate online fault diagnosis through real-time determined detection values.

[0180] Example 2

[0181] Based on the converter fault diagnosis method disclosed in the above embodiments of the present invention, Figure 8 Specifically, a converter fault diagnosis device that applies this converter fault diagnosis method is disclosed.

[0182] like Figure 8 As shown in the figure, an embodiment of the present invention discloses a converter fault diagnosis device, which includes:

[0183] Analysis unit 801 is used to perform structural analysis on the target traction converter that has undergone pre-optimized structure, and obtain the first structured model of the target traction converter that has undergone pre-optimized structure.

[0184] The determining unit 802 is used to determine at least one set of structural minimal overdetermined equations for the first structured model;

[0185] Design unit 803 is used to determine the residuals of each of the minimum overdetermined equation sets of the structure;

[0186] Calculation unit 804 is used to calculate the detection value corresponding to each residual;

[0187] The comparison unit 805 is used to compare the detected value with the corresponding detection threshold to obtain a diagnostic result.

[0188] The specific working processes of the analysis unit 801, determination unit 802, design unit 803, calculation unit 804, and comparison unit 805 in the converter fault diagnosis device disclosed in the above embodiments of the present invention can be found in the corresponding content of the converter fault diagnosis method disclosed in the above embodiments of the present invention, and will not be repeated here.

[0189] In summary, this application provides a converter fault diagnosis device, comprising: performing structural analysis on a target traction converter that has undergone pre-optimized structure to obtain a first structured model of the target traction converter; determining at least one set of minimum overdetermined structural equations for the first structured model; determining the residual of each set of minimum overdetermined structural equations; calculating the detection value corresponding to each residual; and comparing the detection value with a corresponding detection threshold to obtain a diagnosis result. This application enables the isolation and detection of various faults in the traction converter through structural optimization, and achieves accurate online fault diagnosis through real-time determined detection values.

[0190] Example 3

[0191] This embodiment also provides a computer-readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, App application store, etc., which stores a computer program. When the computer program is executed by a processor, it can implement the method steps as described in Embodiment 1. This embodiment will not repeat the description here.

[0192] Example 4

[0193] Figure 9 A connection block diagram of an electronic device 900 provided in an embodiment of this application is shown below. Figure 9 As shown, the electronic device 900 may include: a processor 901, a memory 902, a multimedia component 903, an input / output (I / O) interface 904, and a communication component 905.

[0194] The processor 901 is used to execute all or part of the steps in the converter fault diagnosis method as described in Embodiment 1. The memory 902 is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.

[0195] The processor 901 may be implemented as an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field-programmable gate array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is used to execute the converter fault diagnosis method in Embodiment 1 above.

[0196] The memory 902 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0197] Multimedia component 903 may include a screen, which may be a touchscreen, and an audio component for outputting and / or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory or transmitted via a communication component. The audio component also includes at least one speaker for outputting audio signals.

[0198] I / O interface 904 provides an interface between processor 901 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical buttons.

[0199] Communication component 905 is used for wired or wireless communication between the electronic device 900 and other devices. Wireless communication includes, for example, Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or one or more combinations thereof. Therefore, the corresponding communication component 905 may include: a Wi-Fi module, a Bluetooth module, or an NFC module.

[0200] In summary, this application provides a converter fault diagnosis method, apparatus, storage medium, and electronic device. The method includes: performing structural analysis on a target traction converter that has undergone pre-optimized structure to obtain a first structured model of the pre-optimized target traction converter; determining at least one set of minimum overdetermined structural equations for the first structured model; determining the residual of each set of minimum overdetermined structural equations; calculating the detection value corresponding to each residual; and comparing the detection value with a corresponding detection threshold to obtain a diagnosis result. This application enables the isolation and detection of various faults in the traction converter through structural optimization, and achieves accurate online fault diagnosis through real-time determined detection values.

[0201] In the several embodiments provided in this application, it should be understood that the disclosed methods can also be implemented in other ways. The method embodiments described above are merely illustrative.

[0202] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0203] Although the embodiments disclosed in this application are as described above, the above content is merely for the purpose of facilitating understanding of this application and is not intended to limit this application. Any person skilled in the art to which this application pertains may make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in this application; however, the scope of patent protection of this application shall still be determined by the scope defined in the appended claims.

Claims

1. A converter fault diagnostic method characterized by, The method comprises: performing structural analysis on a target traction converter with completed structural optimization to obtain a first structured model of the target traction converter with completed structural optimization; determining at least one set of structural minimal overdetermined equations for the first structured model; determining a residual error of each set of structural minimal overdetermined equations; calculating a detection value corresponding to each residual error; comparing the detection value with a corresponding detection threshold to obtain a diagnosis result; wherein the process of performing structural optimization on the target traction converter comprises: performing structural analysis on the target traction converter to obtain a second structured model of the target traction converter; performing DM decomposition on the second structured model to obtain a DM decomposition result; judging whether various faults in the target traction converter can be isolated and detected according to the DM decomposition result; if yes, the target traction converter does not need structural optimization; if no, performing structural optimization on the target traction converter, adding a current sensor of a motor W phase, and performing sensor layout scheme with maximum isolability as a target, so that various faults in the target traction converter can be isolated.

2. The method of claim 1, wherein, The determination of at least one set of structural minimal overdetermined equations for the first structured model comprises: calculating all sets of minimal overdetermined equations for the first structured model according to a mathematical model of the first structured model; processing all sets of minimal overdetermined equations by using a minimal hitting set method to obtain at least one set of structural minimal overdetermined equations.

3. The method of claim 1, wherein, The structural analysis on the target traction converter with completed structural optimization to obtain a first structured model of the target traction converter with completed structural optimization comprises: obtaining a first mathematical model of the target traction converter with completed structural optimization according to a structure of the target traction converter with completed structural optimization; obtaining the first structured model of the target traction converter with completed structural optimization according to the first mathematical model.

4. The method of claim 1, wherein, The structural analysis on the target traction converter to obtain a second structured model of the target traction converter comprises: obtaining a second mathematical model of the target traction converter according to a structure of the target traction converter; obtaining the second structured model of the target traction converter according to the second mathematical model.

5. The method of claim 1, wherein, The judgment of whether various faults in the target traction converter can be isolated and detected according to the DM decomposition result comprises: determining whether various faults can be detected according to a structural overdetermined part of the DM decomposition result; if it is determined that various faults can be detected, determining whether various faults can be isolated from each other according to a relationship formula: The calculation of a detection value corresponding to each residual error comprises: wherein and are equations containing faults and respectively, is the structure overdetermined part after eliminating the equation .

6. The method of claim 1, wherein, according to a formula: The device comprises: a detection value T2 corresponding to each of the residuals R is calculated, wherein satisfies a standard distribution with a degree of freedom of N-1 standard distribution, and , represents a no-fault assumption detection quantity under the condition greater than the probability that represents a confidence level, and the measurement noise of the residual R and the harmonic.

7. A converter fault diagnostic device characterized by comprising: an analysis unit configured to perform structural analysis on a target traction converter with completed structural optimization to obtain a first structured model of the target traction converter with completed structural optimization; a determination unit configured to determine at least one set of structural minimal overdetermined equations for the first structured model; a design unit configured to determine a residual error of each set of structural minimal overdetermined equations; ​ A computing unit is configured to calculate a detection value corresponding to each of the residual errors; A comparing unit is configured to compare the detection value with a corresponding detection threshold to obtain a diagnosis result; The process of performing structural optimization on the target traction converter includes: performing structural analysis on the target traction converter to obtain a second structural model of the target traction converter; performing DM decomposition on the second structural model to obtain a DM decomposition result; determining whether various faults of sensors and components in the target traction converter can be isolated and detected according to the DM decomposition result; if yes, the target traction converter does not need to be structurally optimized; if no, performing structural optimization on the target traction converter, adding a current sensor of a motor W phase, and performing sensor layout scheme with the maximum isolability as a target, so that various faults in the target traction converter can be isolated.

8. A storage medium, characterized by The computer program stored in the storage medium can be executed by one or more processors, and can be used to implement the traction converter fault diagnosis method according to any one of claims 1-6.

9. An electronic device, comprising: The device includes a memory and a processor, the memory stores a computer program, and the memory and the processor are communicatively connected, when the computer program is executed by the processor, the traction converter fault diagnosis method according to any one of claims 1-6 is executed.

Images