A vibration-based system for identifying engine faults in armored vehicles

Abstract

This invention provides a system for identifying engine faults in armored vehicles based on vibration characteristics, relating to the field of electronic digital data processing. It includes a data acquisition module, a signal processing module, a feature analysis module, and a fault detection module. The data acquisition module collects vibration signals from the engine during operation. The signal processing module cleans the signals. The feature analysis module extracts and analyzes key features from the vibration signals. The fault detection module determines engine faults based on the feature analysis information. This system can identify existing faults based on engine vibration information and predict future faults and their corresponding timing, enabling reasonable planning for engine maintenance.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing, and more specifically to a system for identifying engine faults in armored vehicles based on vibration characteristics. Background Technology

[0002] With the development of modern armored vehicle engine technology, their complexity and operational performance have been greatly enhanced. However, this has also made engine maintenance and fault diagnosis more difficult. Once an engine malfunctions, it can not only lead to a decline in vehicle performance but also have a serious impact on mission execution and personnel safety. Traditional engine fault diagnosis methods mainly rely on manual experience and regular maintenance, which is not only inefficient but also makes it difficult to detect potential faults in a timely manner. Therefore, how to quickly and accurately diagnose engine faults and predict their development trends has become an important research direction in the field of armored vehicle maintenance.

[0003] Many fault detection systems have been developed. Extensive research and reference have revealed existing systems, such as the one disclosed in publication number CN117009791B. These systems typically involve: classifying flight conditions and constructing a granular condition set; extracting engine signal feature data, generating a basic feature data set, and constructing a smoothing identifier; obtaining an identification accuracy factor, generating requirement constraints, and simultaneously sending the requirement constraints and sub-network to an encryption unit for network optimization, thus completing the construction of the granular identification sub-network; extracting engine vibration, sound, and temperature feature signals, and constructing an anomaly identification database based on the extraction results; and performing engine fault identification through the granular identification sub-network and the anomaly identification database to generate fault identification results. However, this system only extracts features for fault identification, and can only detect some common faults. Furthermore, it cannot predict faults in advance. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings by proposing a system for identifying engine faults in armored vehicles based on vibration characteristics.

[0005] The present invention adopts the following technical solution:

[0006] A system for identifying engine faults in armored vehicles based on vibration characteristics includes a data acquisition module, a signal processing module, a feature analysis module, and a fault detection module.

[0007] The data acquisition module is used to acquire vibration signals of the transmitter during operation; the signal processing module is used to clean the signals; the feature analysis module is used to extract key features from the vibration signals and analyze them; and the fault detection module determines the faults in the engine based on the feature analysis information.

[0008] The data acquisition module includes a vibration sensing unit, a data transmission unit, and an environmental monitoring unit. The vibration sensing unit is used to collect vibration data of the engine, the data transmission unit is used to output the collected data information, and the environmental monitoring unit is used to monitor the environmental data of the armored vehicle.

[0009] The signal processing module includes a signal filtering unit, a data cleaning unit, and a frequency domain transformation unit. The signal filtering unit is used to filter the signal, the data cleaning unit is used to remove redundant and abnormal data, and the frequency domain transformation unit is used to convert the time domain signal into a frequency domain signal.

[0010] The feature analysis module includes a feature extraction unit, a feature selection unit, and a feature processing unit. The feature extraction unit is used to extract feature information from the signal, the feature selection unit is used to select feature information for analysis, and the feature processing unit is used to calculate and process the feature information to obtain feature parameters.

[0011] The fault detection module includes a fault model storage unit, a comparison and diagnosis unit, and a trend prediction unit. The fault model storage unit is used to store vibration characteristic model information of engine faults. The comparison and diagnosis unit is used to detect faults by comparing characteristic parameters with model information. The trend prediction unit is used to predict possible future fault development.

[0012] Furthermore, the comparison and diagnosis unit includes a fault retrieval processor, a deep diagnosis processor, and a fault output processor. The fault retrieval processor determines suspected faults based on parameter type retrieval. The deep diagnosis processor compares the standard values ​​of the characteristic parameters of the suspected faults with the characteristic parameters and determines the existing faults. The fault output processor outputs fault information and corresponding data information.

[0013] Furthermore, the process by which the deep diagnostic processor compares and judges faults includes the following steps:

[0014] S1. Select a suspected fault;

[0015] S2. Based on the environmental data, the characteristic parameters are corrected according to the following formula:

[0016] Cp(i)=Cp′(i)·(1+λ1·(T-T0)+λ2·(H-H0));

[0017] Where T represents the current temperature, H represents the current humidity, T0 represents the standard temperature, H0 represents the standard humidity, λ1 is the temperature influence coefficient of the suspected fault, λ2 is the humidity influence coefficient of the suspected fault, Cp'(i) represents the i-th characteristic parameter before correction, and Cp(i) represents the i-th characteristic parameter after correction.

[0018] S3. Calculate the occurrence index Q of the suspected fault according to the following formula:

[0019]

[0020] Where n is the number of the suspected fault characteristic parameters, and Sp(i) represents the standard value of the i-th characteristic parameter;

[0021] S4. When the index exceeds the judgment threshold, a suspected fault is judged.

[0022] S5. Repeat steps S1 to S4 until all suspected faults have been analyzed.

[0023] Furthermore, the trend prediction unit includes a historical parameter register, a change filtering processor, and a time prediction processor. The historical parameter register is used to store feature parameters from different periods. The change filtering processor is used to filter out feature parameters that conform to the change pattern and identify delayed faults. The time prediction processor is used to predict the time when the delayed fault will occur.

[0024] Furthermore, the time prediction processor calculates the occurrence time t of the delayed fault according to the following formula:

[0025]

[0026] Where Cg(i) represents the change exponent of the i-th characteristic parameter of the delay fault, t0 is the time interval between two adjacent storage points, and n1 is the number of characteristic parameters included in the delay fault.

[0027] The beneficial effects achieved by this invention are:

[0028] After extracting the features of the vibration signal, this system combines and processes these features to obtain feature parameters. Based on the comparison of these feature parameters, the fault type is determined. Compared with directly identifying faults through extracted features, this system can detect a wider range of faults. At the same time, by analyzing the feature parameters, the system can determine the types of faults that may occur in the future and predict when they will occur. This provides more targeted reference information for engine maintenance and reduces the probability of temporary faults.

[0029] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the overall structural framework of the present invention;

[0031] Figure 2This is a schematic diagram of the data acquisition module of the present invention;

[0032] Figure 3 This is a schematic diagram of the signal processing module of the present invention;

[0033] Figure 4 This is a schematic diagram of the feature analysis module of the present invention;

[0034] Figure 5 This is a schematic diagram of the fault detection module of the present invention;

[0035] Figure 6 This is a comparison chart of the application time of the fault prediction method in this invention and the actual time. Detailed Implementation

[0036] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated beforehand. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0037] Example 1.

[0038] This embodiment provides a system for identifying engine faults in armored vehicles based on vibration characteristics, combined with... Figure 1 It includes a data acquisition module, a signal processing module, a feature analysis module, and a fault detection module.

[0039] The data acquisition module is used to collect vibration signals of the transmitter during operation. The signal processing module is used to clean the signals. The feature analysis module is used to extract key features from the vibration signals and analyze them. The fault detection module determines the faults in the engine based on the feature analysis information.

[0040] The data acquisition module includes a vibration sensing unit, a data transmission unit, and an environmental monitoring unit. The vibration sensing unit is used to collect vibration data of the engine, the data transmission unit is used to output the collected data information, and the environmental monitoring unit is used to monitor the environmental data of the armored vehicle.

[0041] The signal processing module includes a signal filtering unit, a data cleaning unit, and a frequency domain transformation unit. The signal filtering unit is used to filter the signal, the data cleaning unit is used to remove redundant and abnormal data, and the frequency domain transformation unit is used to convert the time domain signal into a frequency domain signal.

[0042] The feature analysis module includes a feature extraction unit, a feature selection unit, and a feature processing unit. The feature extraction unit is used to extract feature information from the signal, the feature selection unit is used to select feature information for analysis, and the feature processing unit is used to calculate and process the feature information to obtain feature parameters.

[0043] The fault detection module includes a fault model storage unit, a comparison and diagnosis unit, and a trend prediction unit. The fault model storage unit is used to store vibration characteristic model information of engine faults. The comparison and diagnosis unit is used to detect faults by comparing characteristic parameters with model information. The trend prediction unit is used to predict possible future fault development.

[0044] The comparison and diagnosis unit includes a fault retrieval processor, a deep diagnosis processor, and a fault output processor. The fault retrieval processor determines suspected faults based on parameter type retrieval. The deep diagnosis processor compares the standard values ​​of the characteristic parameters of the suspected faults with the characteristic parameters and determines the existence of the faults. The fault output processor outputs fault information and corresponding data information.

[0045] The deep diagnostic processor's process of comparing and judging faults includes the following steps:

[0046] S1. Select a suspected fault.

[0047] S2. Based on the environmental data, the characteristic parameters are corrected according to the following formula:

[0048] Cp(i)=Cp'(i)·(1+λ1·(T-T0)+λ2·(H-H0)).

[0049] Where T represents the current temperature, H represents the current humidity, T0 represents the standard temperature, H0 represents the standard humidity, λ1 is the temperature influence coefficient of the suspected fault, λ2 is the humidity influence coefficient of the suspected fault, Cp'(i) represents the i-th characteristic parameter before correction, and Cp(i) represents the i-th characteristic parameter after correction.

[0050] S3. Calculate the occurrence index Q of the suspected fault according to the following formula:

[0051]

[0052] Where n is the number of suspected fault characteristic parameters, and Sp(i) represents the standard value of the i-th characteristic parameter.

[0053] S4. When the index exceeds the judgment threshold, a suspected fault is judged.

[0054] S5. Repeat steps S1 to S4 until all suspected faults have been analyzed.

[0055] The trend prediction unit includes a historical parameter register, a change filtering processor, and a time prediction processor. The historical parameter register is used to store characteristic parameters from different periods. The change filtering processor is used to filter out characteristic parameters that conform to the change pattern and identify delayed faults. The time prediction processor is used to predict the time when the delayed fault will occur.

[0056] The time prediction processor calculates the occurrence time t of the delayed fault according to the following formula:

[0057]

[0058] Where Cg(i) represents the change exponent of the i-th characteristic parameter of the delay fault, t0 is the time interval between two adjacent storage points, and n1 is the number of characteristic parameters included in the delay fault.

[0059] Example 2.

[0060] This embodiment includes all the contents of Embodiment 1, and provides a system for identifying engine faults in armored vehicles based on vibration characteristics, including a data acquisition module, a signal processing module, a feature analysis module, and a fault detection module.

[0061] The data acquisition module is used to collect vibration signals of the transmitter during operation. The signal processing module is used to clean the signals. The feature analysis module is used to extract key features from the vibration signals and analyze them. The fault detection module determines the faults in the engine based on the feature analysis information.

[0062] Combination Figure 2 The data acquisition module includes a vibration sensing unit, a data transmission unit, and an environmental monitoring unit. The vibration sensing unit is used to collect vibration data of the engine, the data transmission unit is used to output the collected data information, and the environmental monitoring unit is used to monitor the environmental data of the armored vehicle.

[0063] Combination Figure 3 The signal processing module includes a signal filtering unit, a data cleaning unit, and a frequency domain transformation unit. The signal filtering unit is used to filter the signal, the data cleaning unit is used to remove redundant and abnormal data, and the frequency domain transformation unit is used to convert the time domain signal into a frequency domain signal.

[0064] Combination Figure 4The feature analysis module includes a feature extraction unit, a feature selection unit, and a feature processing unit. The feature extraction unit is used to extract feature information from the signal, the feature selection unit is used to select feature information for analysis, and the feature processing unit is used to calculate and process the feature information to obtain feature parameters.

[0065] Combination Figure 5 The fault detection module includes a fault model storage unit, a comparison diagnosis unit, and a trend prediction unit. The fault model storage unit is used to store vibration characteristic model information of engine faults. The comparison diagnosis unit is used to detect faults by comparing characteristic parameters with model information. The trend prediction unit is used to predict possible future fault development.

[0066] The vibration sensing unit includes a vibration detection processor, a threshold analysis processor, and a reference information register. The vibration detection processor is used to collect vibration information, the threshold analysis processor is used to analyze the vibration information to determine whether the engine is working, and the reference information register is used to store vibration data when the engine is not working.

[0067] The data transmission processor unit includes a signal conversion processor, a vibration transmission processor, and an environmental transmission processor. The signal conversion processor is used to convert analog signals into digital signals, the vibration transmission processor is used to transmit the vibration digital signals to the signal processing module, and the environmental transmission processor is used to transmit the collected environmental data to the fault detection module.

[0068] The environmental monitoring unit includes a temperature detection processor, a humidity detection processor, and a detection control processor. The temperature detection processor is used to monitor temperature information, the humidity detection processor is used to detect humidity information, and the detection control processor is used to control the temperature and humidity detection switches.

[0069] The signal filtering unit includes a signal subtraction processor, a low-pass filter processor, and a high-pass filter processor. The signal subtraction processor is used to subtract the vibration signal when the engine is running from the vibration signal when the engine is not running to obtain a pure vibration signal. The low-pass filter processor is used to filter high-frequency signals in the pure vibration signal, and the high-pass filter processor is used to filter low-frequency signals in the pure vibration signal.

[0070] The data cleaning unit includes an anomaly detection processor, a data denoising processor, and a redundancy removal processor. The anomaly detection processor is used to detect and clean abnormal parts of the signal, the data denoising processor is used to remove noise from the signal, and the redundancy removal processor is used to remove redundant information from the signal.

[0071] The frequency domain transformation unit includes a fast transformation processor, a short-time transformation processor, and an envelope adjustment processor. The fast transformation processor is used to perform frequency domain transformation on the signal when the engine is operating stably to obtain spectral information. The short-time transformation processor is used to slice the signal when the engine is just started and then perform frequency domain transformation to obtain time-frequency change information. The envelope adjustment processor is used to obtain the envelope curve of the spectral information.

[0072] The feature extraction unit includes a feature item register, a parsing and extraction processor, and a feature data register. The feature item register is used to store annotation information of feature items. The parsing and extraction processor is used to parse the annotation information and read the corresponding feature data from the signal. The feature data register is used to save the feature data of each feature item.

[0073] The feature selection unit includes a feature combination processor, a regular comparison processor, and a combination output processor. The feature combination processor is used to combine different feature items to obtain feature groups. The regular comparison processor is used to compare the feature group data with regular data and filter out the matching feature groups. The combination output processor is used to send the remaining feature group data to the feature processing unit.

[0074] The feature processing unit includes a combined recognition processor, a parameter calculation processor, and a data guidance processor. The combined recognition processor is used to identify the feature item type in the feature group. The parameter calculation processor is used to process the data in the feature group to obtain feature parameters. The data guidance processor guides the data in the feature group to the corresponding parameter calculation processor based on the identified type.

[0075] Different parameter calculation processors have different data processing formulas.

[0076] The fault model storage unit includes a fault information register, a standard parameter register, and an environmental impact register. The fault information register is used to store fault type information, the standard parameter register is used to store the standard values ​​of characteristic parameters for each fault type, and the environmental impact register is used to store the influence relationship of environmental items on each fault type.

[0077] The comparison and diagnosis unit includes a fault retrieval processor, a deep diagnosis processor, and a fault output processor. The fault retrieval processor determines suspected faults based on parameter type retrieval. The deep diagnosis processor compares the standard values ​​of the characteristic parameters of the suspected faults with the characteristic parameters and determines the existence of the faults. The fault output processor outputs fault information and corresponding data information.

[0078] Let B be the set of feature parameter types output by the feature analysis module, and let A be the set of feature parameter types included in the fault type. When set B includes set A, the corresponding fault type is a suspected fault.

[0079] The deep diagnostic processor's process of comparing and judging faults includes the following steps:

[0080] S1. Select a suspected fault.

[0081] S2. Based on the environmental data, the characteristic parameters are corrected according to the following formula:

[0082] Cp(i)=Cp'(i)·(1+λ1·(T-T0)+λ2·(H-H0)).

[0083] Where T represents the current temperature, H represents the current humidity, T0 represents the standard temperature, H0 represents the standard humidity, λ1 is the temperature influence coefficient of the suspected fault, λ2 is the humidity influence coefficient of the suspected fault, Cp'(i) represents the i-th characteristic parameter before correction, and Cp(i) represents the i-th characteristic parameter after correction.

[0084] S3. Calculate the occurrence index Q of the suspected fault according to the following formula:

[0085]

[0086] Where n is the number of suspected fault characteristic parameters, and Sp(i) represents the standard value of the i-th characteristic parameter.

[0087] S4. When the index exceeds the judgment threshold, a suspected fault is judged.

[0088] S5. Repeat steps S1 to S4 until all suspected faults have been analyzed.

[0089] The trend prediction unit includes a historical parameter register, a change filtering processor, and a time prediction processor. The historical parameter register is used to store characteristic parameters from different periods. The change filtering processor is used to filter out characteristic parameters that conform to the change pattern and identify delayed faults. The time prediction processor is used to predict the time when the delayed fault will occur.

[0090] The historical parameter register is configured with storage points, each storage point corresponds to a period, the time interval between adjacent storage points is the same, and each type of feature parameter stores a value at each storage point.

[0091] The filtering process of the change filtering processor includes the following steps:

[0092] S21. Select a feature parameter and obtain the values ​​of the m nearest storage points.

[0093] S22. Calculate the change index Cg of the characteristic parameter according to the following formula:

[0094]

[0095] Where Vp(i) represents the characteristic parameter value of the i-th storage point.

[0096] S23. Mark the feature parameters whose absolute value of the change index is greater than the prediction threshold.

[0097] S24. Repeat steps S21 to S23 to count the characteristic parameters that exist.

[0098] S25. Set the faults whose characteristic parameters are all marked as delayed faults.

[0099] The time prediction processor calculates the occurrence time t of the delayed fault according to the following formula:

[0100]

[0101] Where Cg(i) represents the change exponent of the i-th characteristic parameter of the delay fault, t0 is the time interval between two adjacent storage points, and n1 is the number of characteristic parameters included in the delay fault.

[0102] The 'i' mentioned above refers to the ordinal number and has no actual meaning.

[0103] The following is a portion of the code for this system:

[0104]

[0105]

[0106]

[0107] This system predicts engine failures and, without requiring specific engine maintenance, compares the actual occurrence time of the failure with the predicted time to obtain... Figure 6 ...

[0108] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A system for identifying engine faults in armored vehicles based on vibration characteristics, characterized in that, It includes a data acquisition module, a signal processing module, a feature analysis module, and a fault detection module; The data acquisition module is used to acquire vibration signals of the transmitter during operation; the signal processing module is used to clean the signals; the feature analysis module is used to extract key features from the vibration signals and analyze them; and the fault detection module determines the faults in the engine based on the feature analysis information. The data acquisition module includes a vibration sensing unit, a data transmission unit, and an environmental monitoring unit. The vibration sensing unit is used to collect vibration data of the engine, the data transmission unit is used to output the collected data information, and the environmental monitoring unit is used to monitor the environmental data of the armored vehicle. The signal processing module includes a signal filtering unit, a data cleaning unit, and a frequency domain transformation unit. The signal filtering unit is used to filter the signal, the data cleaning unit is used to remove redundant and abnormal data, and the frequency domain transformation unit is used to convert the time domain signal into a frequency domain signal. The feature analysis module includes a feature extraction unit, a feature selection unit, and a feature processing unit. The feature extraction unit is used to extract feature information from the signal, the feature selection unit is used to select feature information for analysis, and the feature processing unit is used to calculate and process the feature information to obtain feature parameters. The fault detection module includes a fault model storage unit, a comparison and diagnosis unit, and a trend prediction unit. The fault model storage unit is used to store vibration characteristic model information of engine faults. The comparison and diagnosis unit is used to detect faults by comparing characteristic parameters with model information. The trend prediction unit is used to predict possible future fault development. The comparison and diagnosis unit includes a fault retrieval processor, a deep diagnosis processor, and a fault output processor. The fault retrieval processor determines suspected faults based on parameter type retrieval. The deep diagnosis processor compares the standard values ​​of the characteristic parameters of the suspected faults with the characteristic parameters and determines the existing faults. The fault output processor outputs fault information and corresponding data information. The deep diagnostic processor's process of comparing and judging faults includes the following steps: S1. Select a suspected fault; S2. Based on the environmental data, the characteristic parameters are corrected according to the following formula: ； Where T represents the current temperature, H represents the current humidity, T0 represents the standard temperature, and H0 represents the standard humidity. The temperature influence coefficient of the suspected fault. Let Cp'(i) be the humidity influence coefficient of the suspected fault, where Cp'(i) represents the i-th characteristic parameter before correction and Cp(i) represents the i-th characteristic parameter after correction. S3. Calculate the occurrence index Q of the suspected fault according to the following formula: ； Where n is the number of the suspected fault characteristic parameters, and Sp(i) represents the standard value of the i-th characteristic parameter; S4. When the index exceeds the judgment threshold, a suspected fault is judged. S5. Repeat steps S1 to S4 until all suspected faults have been analyzed.

2. The system for identifying engine faults in armored vehicles based on vibration characteristics as described in claim 1, characterized in that, The trend prediction unit includes a historical parameter register, a change filtering processor, and a time prediction processor. The historical parameter register is used to store characteristic parameters from different periods. The change filtering processor is used to filter out characteristic parameters that conform to the change pattern and identify delayed faults. The time prediction processor is used to predict the time when the delayed fault will occur.

3. The system for identifying engine faults in armored vehicles based on vibration characteristics as described in claim 2, characterized in that, The time prediction processor calculates the occurrence time t of the delayed fault according to the following formula: ； Where Cg(i) represents the change exponent of the i-th characteristic parameter of the delay fault, t0 is the time interval between two adjacent storage points, and n1 is the number of characteristic parameters included in the delay fault.

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