Bogie health status monitoring method, system, equipment and medium integrating radar speed measurement and multi-physical quantities

Abstract

The present invention discloses a bogie health status monitoring method, system, device, and medium that integrates radar speed measurement with multiple physical quantities. The method comprises: collecting physical signals during normal train operation, wherein the physical signals include vehicle speed, vibration, noise, and temperature signals at k measuring points on the bogie; pre-processing the vibration and noise signals in combination with the vehicle speed signals, performing time domain analysis and frequency domain analysis, and extracting signal features; and performing statistical analysis on the temperature signal to obtain the statistical features of the signal. The bogie health status monitoring method, system, device, and medium that integrates radar speed measurement with multiple physical quantities perform weighted processing on the collected vehicle speed signal and physical quantity signals such as vibration, noise, and temperature to obtain a fusion index, and classify vehicle speeds into different levels. The thresholds of the health status fusion index at different vehicle speeds are used for comparison and judgment, thereby enabling bogie health status assessment at different vehicle speeds.

Description

Technical Field

[0001] The present invention relates to a bogie health status monitoring method, system, equipment and medium that integrate radar speed measurement and multiple physical quantities, and belongs to the technical field of bogie health monitoring. Background Art

[0002] Bogies are key components in train operation. Effective health status monitoring can ensure safe operation of the train and timely equipment maintenance.

[0003] Traditional health monitoring systems often evaluate multiple physical parameters individually to determine the health of the assessed object. Because a single physical signal contains relatively little information and fails to reflect the correlations between different physical quantities, it is impossible to comprehensively evaluate the overall condition of the train, which can easily lead to inaccurate assessments of equipment health. Furthermore, the smoothness of train operation varies at different speeds, and the corresponding bogie health indicators also vary. Therefore, a single indicator system cannot be used to assess the train's operational health.

[0004] Therefore, in order to conduct a comprehensive evaluation of the train bogie operating status in real time and accurately, and adjust the evaluation indicators according to the actual vehicle speed to meet actual monitoring needs, there is an urgent need for a bogie health status monitoring method, system, equipment and medium that integrates radar speed measurement and multiple physical quantities. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a bogie health status monitoring method, system, equipment and medium that integrates radar speed measurement and multiple physical quantities. The collected vehicle speed signal is weightedly processed with physical quantity signals such as vibration, noise, and temperature to obtain a fusion index, and the vehicle speed is divided into different levels. The threshold value of the health status fusion index at different vehicle speeds is compared and judged, thereby realizing the health status assessment of the bogie at different vehicle speeds.

[0006] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0007] In a first aspect, the present invention provides a bogie health status monitoring method integrating radar speed measurement and multiple physical quantities, comprising:

[0008] Collecting physical signals of the train in normal operation, the physical signals including speed, vibration, noise and temperature signals at k measuring points on the train bogie;

[0009] Combined with the vehicle speed signal, the vibration signal and noise signal are pre-processed and analyzed in the time domain and frequency domain to extract the signal features; the temperature signal is statistically analyzed to obtain the statistical features of the signal;

[0010] The obtained signal features and statistical features are normalized, and weighted according to the influence of different signals on the bogie operating status to obtain a fusion index S including speed information;

[0011] The fusion index S obtained at different vehicle speed levels is compared with the threshold β of the healthy state at the corresponding speed to determine the health status of the bogie.

[0012] Furthermore, the physical signal acquisition method includes:

[0013] The vehicle speed is obtained by radar speed measurement, and the vibration, noise and temperature signals are collected by sensors.

[0014] Furthermore, the vibration signal and noise signal preprocessing method includes:

[0015] The vibration signal and the noise signal are filtered to obtain the time domain signal;

[0016] The vehicle speed signal is converted into a rotation speed signal, and the phase is solved based on the rotation speed signal to obtain the interpolation time point corresponding to the time domain signal;

[0017] Based on the obtained interpolation time points, the time domain signal is converted into an angular domain signal by equal-angle resampling using the Lagrange interpolation method.

[0018] Furthermore, the Lagrange interpolation method is performed as follows:

[0019]

[0020] Where, represents the angular domain signal after equiangular interpolation, L represents the interpolation by Lagrange interpolation, X(t) represents the time domain signal, Indicates the interpolation time point corresponding to the time domain signal;

[0021] in, Obtained by the following steps:

[0022] The train speed V(t) is measured by a radar speed meter. Under the condition of known wheel radius parameter R, the original speed is converted into the real-time rotation angle of the wheel. In the extremely short time of high-speed sampling, the wheel speed and angle are considered to be constant. Therefore, the time corresponding to the intermediate angle is solved by the proportional formula:

[0023]

[0024] Where, t i The real-time corner of the moment, [t i ] represents the time ti Round down, according to the above formula, combined with Interpolate the corresponding time in equal angle increments

[0025] Furthermore, the method for obtaining the fusion index S includes:

[0026] Perform time domain analysis on the time domain signal X(t) and extract the time domain features: effective value Q1

[0027]

[0028] Among them, X rms represents the RMS value of the time domain signal X(t), and N is the number of sample points in the signal;

[0029] Diagonal signal Perform Fourier transform and extract frequency domain features: vibration acceleration level Q2

[0030]

[0031] Where a e is the effective value of acceleration, a0 is the reference acceleration;

[0032] Perform statistical analysis on the temperature signal, calculate the mean and variance of the temperature measurement value, and add the two to obtain the statistical feature: temperature Q3;

[0033] For all signal characteristics Q i Perform normalization to obtain the evaluation index q i , removing the influence of different physical quantity dimensions on signal fusion, namely:

[0034]

[0035] Where Q max is the maximum value of the signal characteristic when the bogie is in a healthy state, Q min is the minimum value, q i Between 0 and 1, q i is the evaluation index obtained after normalization, i represents different physical quantities;

[0036] The normalized evaluation indicators are fused to obtain the fusion index s of multi-physical signals:

[0037]

[0038] Where q n is the evaluation index obtained after normalization, ω n is the evaluation index q n The corresponding weight.

[0039] Furthermore, the method for constructing the fusion index threshold under the bogie health state is as follows:

[0040] The wheels are divided into different levels according to their rotation speed, and multiple groups of signals in healthy state are collected for different physical signals, such as rotation speed. At the same level, k groups of signals (X1, X2, X3, ..., X j ; j = 1, 2, 3, ..., k), obtain the evaluation index of each group of signals The evaluation index q Xi Perform statistical analysis, calculate the mean, variance and sum to obtain the evaluation index under the health state Finally, the weighted fusion index threshold is obtained

[0041]

[0042] Where, Indicates speed The threshold of the fusion indicator under the bogie health status.

[0043] Furthermore, the method for determining the bogie health status includes:

[0044] Compare the fusion index S obtained during actual operation of the bogie with the threshold β of the healthy state at the corresponding speed to determine whether the fusion index S is within the threshold β range:

[0045]

[0046] When the fusion index S is within the threshold β, it can be judged that the bogie is in a healthy state;

[0047] When the fusion index S is outside the threshold β range, it is necessary to further determine the proportion of the part exceeding the threshold β. If the proportion of the exceeding part is less than 20%, it is judged as a minor fault of the bogie; otherwise, it is judged as a major fault of the bogie.

[0048] In a second aspect, the present invention provides a bogie health status monitoring device that integrates radar speed measurement and multiple physical quantities, comprising:

[0049] Signal acquisition module: used to collect physical signals of the train in normal operation, including speed, vibration, noise and temperature signals at k measuring points on the train bogie;

[0050] Signal feature acquisition module: used to perform time domain analysis and frequency domain analysis on vibration and noise signals after pre-processing based on vehicle speed signals, and extract signal features; perform statistical analysis on temperature signals to obtain statistical features of the signals;

[0051] Fusion index acquisition module: used to normalize the obtained signal features and statistical features, and weight the influence of different signals on the bogie operation status according to the size of the influence, to obtain the fusion index S including the speed information;

[0052] Judgment module: used to compare the fusion index S obtained at different vehicle speed levels with the threshold β of the healthy state at the corresponding speed to judge the health state of the bogie.

[0053] In a third aspect, the present invention provides a computer device including a processor and a storage medium;

[0054] The storage medium is used to store instructions;

[0055] The processor is configured to operate according to the instructions to execute the steps of the method according to the first aspect.

[0056] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the program implements the steps of the method described in the first aspect when executed by a processor.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] By fusing the train speed signal under actual operating conditions with other physical signals reflecting the bogie operating status, a comprehensive assessment system for the train bogie health status is formed, in which a set of evaluation indicators can be adjusted according to changes in train speed. This not only avoids misjudgment due to failure of a single signal, but also correlates various physical signals to make the monitoring results more reliable. At the same time, the collected speed signal is weighted with physical quantity signals such as vibration, noise, and temperature to obtain a fusion index. According to the speed level of the train, the corresponding threshold of the health status fusion indicator is selected for judgment, thereby realizing the health status assessment of the bogie at different speeds. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a flow chart of the bogie health status monitoring method that integrates radar speed measurement and multiple physical quantities provided in Example 1;

[0060] Figure 2 yes Figure 1 Schematic diagram of the specific process of judging the health status of the bogie;

[0061] Figure 3 Schematic diagram of a health status assessment system constructed based on the bogie health status monitoring method provided in Example 1;

[0062] Figure 4 It is a schematic diagram of the preprocessing process of vibration signals and noise signals. DETAILED DESCRIPTION

[0063] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Unless there is a conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0064] The term "and / or" in this document simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " in this document generally indicates an "or" relationship between the related objects.

[0065] Example 1:

[0066] Figure 1 This is a flow chart of a bogie health monitoring method that integrates radar speed measurement and multiple physical quantities in the first embodiment of the present invention. The bogie health monitoring method that integrates radar speed measurement and multiple physical quantities provided in this embodiment can be applied to a terminal and can be executed by a bogie health monitoring device that integrates radar speed measurement and multiple physical quantities. The device can be implemented in software and / or hardware and can be integrated into a terminal, such as any smartphone, tablet computer or computer device with communication capabilities. Figure 1 , the method of this implementation specifically includes the following steps:

[0067] Step A: collecting physical signals of the train in normal operation, wherein the physical signals include speed, vibration, noise and temperature signals at k measuring points of the train bogie;

[0068] Step B: Pre-process the vibration signal and noise signal in combination with the vehicle speed signal, perform time domain analysis and frequency domain analysis, and extract signal features; perform statistical analysis on the temperature signal to obtain the statistical features of the signal;

[0069] Step C: normalize the obtained signal features and statistical features, and weight the influence of different signals on the bogie operating state according to the weight, to obtain a fusion index S including speed information;

[0070] Step D: Compare the fusion index S obtained at different vehicle speed levels with the threshold β of the healthy state at the corresponding speed to determine the health state of the bogie.

[0071] Step Aa: The physical signal acquisition method includes:

[0072] The vehicle speed is obtained by radar speed measurement, and the vibration, noise and temperature signals are obtained by sensors. Specifically, the radar speed meter, vibration sensor, sound sensor, etc. are fixed on the train bogie according to predetermined measurement points, and various physical signals during operation are obtained through synchronous collection.

[0073] Step 8: If Figure 4 As shown, the vibration signal and noise signal preprocessing method includes:

[0074] The vibration signal and the noise signal are filtered to obtain the time domain signal;

[0075] The vehicle speed signal is converted into a rotation speed signal, and the phase is solved based on the rotation speed signal to obtain the interpolation time point corresponding to the time domain signal;

[0076] Based on the obtained interpolation time points, the time domain signal is converted into a quasi-static angular domain signal by performing equal-angle resampling through Lagrange interpolation method;

[0077] It is worth further explaining that the actual running speed of the train is obtained through radar speed measurement and converted into a speed signal. The various physical signals for evaluating the health status of the train are collected through sensors placed on the carriages or body. Since the vibration and noise signals fluctuate with the speed, they need to be pre-processed before feature analysis. Specifically, the vibration and noise signals can be determined by phase solution to determine the equal-angle resampling moment, and then the signal is resampled in the angular domain using the Lagrange interpolation method. The interpolated time axis coordinate point is the equal-angle resampling moment. The resampled signal obtained at this time is a quasi-static signal in the angular domain.

[0078] Since the temperature signal is collected into data points at a fixed sampling frequency and is not affected by speed fluctuations, there is no need to perform angular domain resampling processing. Only statistical analysis, such as calculating the mean and variance, is required for the collected temperature data.

[0079] Step Bb: Since the amplitude of the time domain signal X(t) changes suddenly in real time, the Lagrange interpolation method is used to perform equiangular interpolation on the original time domain signal in the time domain coordinates, and simulate resampling to obtain a quasi-static signal in the angular domain coordinates:

[0080]

[0081] Where, represents the angular domain signal after equiangular interpolation, L represents the interpolation by Lagrange interpolation, X(t) represents the time domain signal, Indicates the interpolation time point corresponding to the time domain signal;

[0082] in, Obtained by the following steps:

[0083] The train speed V(t) is measured by a radar speed meter. Under the condition of known wheel radius parameter R, the original speed is converted into the real-time rotation angle of the wheel. In the extremely short time of high-speed sampling, the wheel speed and angle are considered to be constant. Therefore, the time corresponding to the intermediate angle is solved by the proportional formula:

[0084]

[0085] Where, t i The real-time corner of the moment, [t i ] represents the time t i Round down, according to the above formula, combined with Interpolate the corresponding time in equal angle increments

[0086] Step Ca: The method for obtaining the fusion index S includes:

[0087] (1) Perform time domain analysis on the time domain signal X(t) and extract the time domain features: effective value Q1

[0088]

[0089] Among them, X rms represents the RMS value of the time-domain signal X(t), where N is the number of sample points in the signal. This feature reflects the amplitude and energy intensity of the bogie vibration and can objectively assess the bogie's stability.

[0090] (2) Diagonal signal Perform Fourier transform and extract frequency domain features: vibration acceleration level Q2

[0091]

[0092] Where a e is the effective value of acceleration, and a0 is the reference acceleration.

[0093] It is worth mentioning that since speed fluctuations will cause the signal to produce spectrum blurring, it is impossible to perform Fourier transform analysis to extract frequency domain features, so equal angle resampling is required. However, it has no effect on the extraction of time domain features, so time domain features can be directly extracted from time domain signals; frequency domain features can be extracted after Fourier transform of frequency domain signals;

[0094] The premise of Fourier transform is that the signal must be a non-sudden and stable signal, so the interpolation time point is obtained by phase solution, and then the interpolation method is used to resample to obtain a quasi-static signal in the angular domain.

[0095] (3) Perform statistical analysis on the temperature signal, calculate the mean and variance of the temperature measurement value, and add the two to obtain the statistical feature: temperature Q3;

[0096] (4) For all signal features Q i Perform normalization to obtain the evaluation index q i , removing the influence of different physical quantity dimensions on signal fusion, namely:

[0097]

[0098] Where Q max is the maximum value of the signal characteristic when the bogie is in a healthy state, Q min is the minimum value, q i Between 0 and 1, q i is the evaluation index obtained after normalization, i represents different physical quantities;

[0099] (5) Since the influence of various physical quantities on the bogie health status is different, the normalized evaluation indicators are fused to obtain the fusion index S of the multi-physical signal:

[0100]

[0101] Where q n is the evaluation index obtained after normalization, ω n is the evaluation index q n The corresponding weight.

[0102] Step Da: The method for constructing the fusion index threshold under the bogie health state is:

[0103] The wheels are divided into different levels according to their rotation speed, and multiple groups of signals in healthy state are collected for different physical signals, such as rotation speed. At the same level, k groups of signals (X1, X2, X3, ..., X j ; j = 1, 2, 3, ..., k), the evaluation index of each group of signals is obtained through step Ca The evaluation index q Xi Perform statistical analysis, calculate the mean, variance and sum to obtain the evaluation index under the health state Finally, the weighted fusion index threshold is obtained

[0104]

[0105] Where, Indicates speed The threshold of the fusion index under the healthy state of the bogie, ω n Evaluation indicators for health status The corresponding weight.

[0106] Step Db: Figure 2 As shown, the method for determining the health status of the bogie includes:

[0107] Compare the fusion index S obtained during actual operation of the bogie with the threshold β of the healthy state at the corresponding speed to determine whether the fusion index S is within the threshold β range:

[0108]

[0109] When the fusion index S is within the threshold β, it can be judged that the bogie is in a healthy state;

[0110] When the fusion index S is outside the threshold β range, it is necessary to further determine the proportion of the part exceeding the threshold β. If the proportion of the exceeding part is less than 20%, it is judged as a minor fault of the bogie; otherwise, it is judged as a major fault of the bogie, thereby achieving the purpose of the bogie health status monitoring system.

[0111] The health status assessment system constructed based on this health status monitoring method is as follows: Figure 3 As shown in the figure, before starting the train bogie health status monitoring, it is necessary to first divide it into different levels according to the wheel speed, collect different physical signals under normal operating conditions, and construct a fusion index threshold β for subsequent analysis and comparison.

[0112] The various physical signals of the bogie during actual operation are collected, the signal features are extracted and normalized and weighted to obtain the index S used to evaluate the health status of the bogie.

[0113] The fusion index S is compared with the threshold β for the healthy state at the corresponding speed to determine whether the fusion index S is within the threshold β range. If the fusion index S is within the threshold β range, it can be determined that the bogie is in a healthy state. If the fusion index S is outside the threshold β range, it is necessary to further determine the proportion of the portion exceeding the threshold β.

[0114] Calculate the proportion of the fusion index S that exceeds the threshold β. Then determine whether the correlation reaches the threshold β. If the correlation reaches the threshold, the component is judged to be healthy. If the proportion of the excess is less than 20%, it is judged to be a minor fault in the bogie; otherwise, it is judged to be a major fault in the bogie.

[0115] In summary, the bogie health status monitoring method provided in this embodiment, which integrates radar speed measurement and multiple physical quantities, fuses the train speed signal under actual operating conditions with other physical signals reflecting the bogie operating status to form a comprehensive train bogie health status assessment system with a set of evaluation indicators that can be adjusted with changes in train speed. This not only avoids the situation where incorrect judgments are caused by the failure of a single signal, but also associates various physical signals to make the monitoring results more reliable. At the same time, the collected speed signal is weighted with physical quantity signals such as vibration, noise, and temperature to obtain a fusion index. Based on the speed level of the train, the corresponding threshold of the health status fusion index is selected for judgment, thereby realizing the health status assessment of the bogie at different speeds.

[0116] Example 2:

[0117] A bogie health status monitoring device integrating radar speed measurement and multiple physical quantities, comprising:

[0118] Signal acquisition module: used to collect physical signals of the train in normal operation, including speed, vibration, noise and temperature signals at k measuring points on the train bogie;

[0119] Signal feature acquisition module: used to perform time domain analysis and frequency domain analysis on vibration and noise signals after pre-processing based on vehicle speed signals, and extract signal features; perform statistical analysis on temperature signals to obtain statistical features of the signals;

[0120] Fusion index acquisition module: used to normalize the obtained signal features and statistical features, and weight the influence of different signals on the bogie operation status according to the size of the influence, to obtain the fusion index S including the speed information;

[0121] Judgment module: used to compare the fusion index S obtained at different vehicle speed levels with the threshold β of the healthy state at the corresponding speed to judge the health state of the bogie.

[0122] The bogie health status monitoring device that integrates radar speed measurement and multiple physical quantities provided in an embodiment of the present invention can execute the bogie health status monitoring method that integrates radar speed measurement and multiple physical quantities provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0123] Example 3:

[0124] An embodiment of the present invention further provides a computer device, including a processor and a storage medium;

[0125] The storage medium is used to store instructions;

[0126] The processor is configured to operate according to the instructions to execute the steps of the following method:

[0127] Collecting physical signals of the train in normal operation, the physical signals including speed, vibration, noise and temperature signals at k measuring points on the train bogie;

[0128] Combined with the vehicle speed signal, the vibration signal and noise signal are pre-processed and analyzed in the time domain and frequency domain to extract the signal features; the temperature signal is statistically analyzed to obtain the statistical features of the signal;

[0129] The obtained signal features and statistical features are normalized, and weighted according to the influence of different signals on the bogie operating status to obtain a fusion index S including speed information;

[0130] The fusion index S obtained at different vehicle speed levels is compared with the threshold β of the healthy state at the corresponding speed to determine the health status of the bogie.

[0131] For details of each step in this embodiment, please refer to the first embodiment, which will not be described in detail here. Since this embodiment and the first embodiment adopt the same technical concept, they also have the technical effects as described in the first embodiment.

[0132] Example 4:

[0133] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the following method:

[0134] Collecting physical signals of the train in normal operation, the physical signals including speed, vibration, noise and temperature signals at k measuring points on the train bogie;

[0135] Combined with the vehicle speed signal, the vibration signal and noise signal are pre-processed and analyzed in the time domain and frequency domain to extract the signal features; the temperature signal is statistically analyzed to obtain the statistical features of the signal;

[0136] The obtained signal features and statistical features are normalized, and weighted according to the influence of different signals on the bogie operating status to obtain a fusion index S including speed information;

[0137] The fusion index S obtained at different vehicle speed levels is compared with the threshold β of the healthy state at the corresponding speed to determine the health status of the bogie.

[0138] For details of each step in this embodiment, please refer to the first embodiment, which will not be described in detail here. Since this embodiment and the first embodiment adopt the same technical concept, they also have the technical effects as described in the first embodiment.

[0139] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0140] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0141] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0143] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A bogie health status monitoring method integrating radar speed measurement and multiple physical quantities, characterized by: include: Collecting physical signals of the train in normal operation, the physical signals including speed, vibration, noise and temperature signals at k measuring points on the train bogie; Combined with the vehicle speed signal, the vibration signal and noise signal are pre-processed and analyzed in the time domain and frequency domain, and the signal features are extracted; Perform statistical analysis on the temperature signal to obtain the statistical characteristics of the signal; The obtained signal features and statistical features are normalized, and weighted according to the influence of different signals on the bogie operating status to obtain a fusion index S including speed information; The fusion index S obtained at different vehicle speed levels is compared with the threshold value β of the healthy state at the corresponding speed to determine the health state of the bogie; The vibration signal and noise signal preprocessing method comprises: The vibration signal and the noise signal are filtered to obtain the time domain signal; The vehicle speed signal is converted into a rotation speed signal, and the phase is solved based on the rotation speed signal to obtain the interpolation time point corresponding to the time domain signal; Based on the obtained interpolation time points, the time domain signal is converted into an angular domain signal by equal-angle resampling using the Lagrange interpolation method.

2. The bogie health status monitoring method integrating radar speed measurement and multiple physical quantities according to claim 1 is characterized in that: The physical signal acquisition method includes: The vehicle speed is obtained by radar speed measurement, and the vibration, noise and temperature signals are collected by sensors.

3. The bogie health status monitoring method integrating radar speed measurement and multiple physical quantities according to claim 1 is characterized in that: The Lagrange interpolation method is performed as follows: Where, represents the angular domain signal after equiangular interpolation, L represents the interpolation by Lagrange interpolation, X(t) represents the time domain signal, Indicates the interpolation time point corresponding to the time domain signal; in, Obtained by the following steps: The train speed V(t) is measured by a radar speed meter. Under the condition of known wheel radius parameter R, the original speed is converted into the real-time rotation angle of the wheel. In the extremely short time of high-speed sampling, the wheel speed and angle are considered to be constant. Therefore, the time corresponding to the intermediate angle is solved by the proportional formula: Where, t i The real-time corner of the moment, [t i ] represents the time t i Round down, according to the above formula, combined with Interpolate the corresponding time in equal angle increments 4. The bogie health status monitoring method integrating radar speed measurement and multiple physical quantities according to claim 3 is characterized in that: The method for obtaining the fusion index S includes: Perform time domain analysis on the time domain signal X(t) and extract the time domain features: effective value Q1 Among them, X rms represents the RMS value of the time domain signal X(t), and N is the number of sample points in the signal; Diagonal signal Perform Fourier transform and extract frequency domain features: vibration acceleration level Q2 Where a e is the effective value of acceleration, a0 is the reference acceleration; Perform statistical analysis on the temperature signal, calculate the mean and variance of the temperature measurement value, and add the two to obtain the statistical feature: temperature Q3; For all signal characteristics Q i Perform normalization to obtain the evaluation index q i , removing the influence of different physical quantity dimensions on signal fusion, namely: Where Q max is the maximum value of the signal characteristic when the bogie is in a healthy state, Q min is the minimum value, q i Between 0 and 1, q i is the evaluation index obtained after normalization, i represents different physical quantities; The normalized evaluation indicators are fused to obtain the fusion index S of multi-physical signals: Where q n is the evaluation index obtained after normalization, ω n is the evaluation index q n The corresponding weight.

5. The bogie health status monitoring method integrating radar speed measurement and multi-physical quantities according to claim 4 is characterized in that the bogie The construction method of the fusion indicator threshold in the healthy state is: The wheels are divided into different levels according to their rotation speed, and multiple groups of signals in healthy state are collected for different physical signals, such as rotation speed. At the same level, k groups of signals (X1, X2, X3, ..., X j ; j=1,2,3,…,k), obtain the evaluation index of each group of signals The evaluation index q Xj Perform statistical analysis, calculate the mean, variance and sum to obtain the evaluation index under the health state Finally, the weighted fusion index threshold is obtained Where, Indicates speed The threshold of the fusion indicator under the bogie health status.

6. The bogie health status monitoring method integrating radar speed measurement and multiple physical quantities according to claim 1 is characterized in that: The method for determining the bogie health status includes: Compare the fusion index S obtained during actual operation of the bogie with the threshold β of the healthy state at the corresponding speed to determine whether the fusion index S is within the threshold β range: When the fusion index S is within the threshold β, it can be judged that the bogie is in a healthy state; When the fusion index S is outside the threshold β range, it is necessary to further determine the proportion of the part exceeding the threshold β. If the proportion of the exceeding part is less than 20%, it is judged as a minor fault of the bogie; otherwise, it is judged as a major fault of the bogie.

7. A bogie health status monitoring device integrating radar speed measurement and multiple physical quantities, characterized in that: The device comprises: Signal acquisition module: used to collect physical signals of the train in normal operation, including speed, vibration, noise and temperature signals at k measuring points on the train bogie; Signal feature acquisition module: used to perform time domain analysis and frequency domain analysis on vibration and noise signals after pre-processing based on vehicle speed signals, and extract signal features; perform statistical analysis on temperature signals to obtain statistical features of the signals; Fusion index acquisition module: used to normalize the obtained signal features and statistical features, and weight the influence of different signals on the bogie operation status according to the size of the influence, to obtain the fusion index S including the speed information; Judgment module: used to compare the fusion index S obtained at different vehicle speed levels with the threshold value β of the healthy state at the corresponding speed to judge the health state of the bogie; The vibration signal and noise signal preprocessing method comprises: The vibration signal and the noise signal are filtered to obtain the time domain signal; The vehicle speed signal is converted into a rotation speed signal, and the phase is solved based on the rotation speed signal to obtain the interpolation time point corresponding to the time domain signal; Based on the obtained interpolation time points, the time domain signal is converted into an angular domain signal by equal-angle resampling using the Lagrange interpolation method.

8. A computer device, characterized in that: including processors and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images