A diagnostic method for railway locomotive bearings based on VMD adaptive parameter selection

Abstract

This application relates to the field of locomotive diagnostic technology, and provides a railway locomotive bearing diagnostic method based on VMD adaptive parameter selection. The method includes: acquiring locomotive operating status data; calculating bearing speed information; obtaining basic bearing information and combining it with the speed information to obtain fault characteristic frequencies; acquiring locomotive bearing vibration signals and motor current signals, obtaining characteristic signals and performing VMD decomposition, using an iterative decomposition method to obtain optimal decomposition parameters, and further decomposing the characteristic signals to obtain multiple IMF components; then calculating and comparing the composite entropy to obtain the change characteristics of the composite entropy, and determining whether a bearing fault has occurred. This method can solve the problems of complexity and inaccurate parameter values ​​in VMD analysis of railway locomotive bearings, achieving accurate diagnosis of various types of locomotive bearing faults by simultaneously processing vibration signals and motor current signals.

Description

Technical Field

[0001] This application relates to the field of locomotive diagnostic technology, and in particular to a railway locomotive bearing diagnostic method based on VMD adaptive parameter selection. Background Technology

[0002] Rolling bearings are core components of locomotive transmission systems, operating under extremely harsh conditions. Operating at high speeds and under heavy loads, they are exposed to dust, rain, and snow, and are subject to drastic temperature fluctuations and frequent load changes. This makes them highly susceptible to micro-damage such as localized wear, pitting, and breakage, which can eventually lead to bearing failure. Failure to detect and address these faults early can result in abnormal locomotive operation, causing significant economic losses and even personal injury or death.

[0003] Currently, vibration signal-based analysis methods are commonly used for monitoring and diagnosing locomotive bearing conditions. Locomotive bearing vibration signals are complex and easily interfered with by vibrations from the car body, bogies, and wheel-rail systems. Early-stage faults produce weak fault information, requiring powerful signal analysis methods for feature extraction. Adaptive signal decomposition (VMD) is widely used in fault diagnosis because it effectively eliminates interference from other components. Many commonly used adaptive decomposition methods exist, among which EMD, EEMD, and LMD are empirical methods with several theoretical problems remaining. VMD, on the other hand, has a robust mathematical foundation. Essentially, it is an adaptive optimal Wiener waveform group that decomposes a complex multi-component signal into a series of AM-FM components in a non-recursive manner. VMD analysis can improve local spectral resolution and has promising applications in motor current signal analysis. The decomposition capability of VMD depends on the selected parameters, namely the modulus K and the penalty factor α. Since K and α are discrete values, inaccurate parameter settings will result in significantly different decomposition results. Furthermore, the optimization algorithm is complex and computationally expensive, limiting its practical application.

[0004] In summary, existing technologies suffer from problems such as the complexity of VMD analysis of railway locomotive bearings and inaccurate parameter values, which limit their practical application. Summary of the Invention

[0005] Therefore, it is necessary to provide a railway locomotive bearing diagnostic method based on VMD adaptive parameter selection to address the aforementioned technical problems.

[0006] A railway locomotive bearing diagnostic method based on VMD adaptive parameter selection includes: acquiring operating status data of the target locomotive, including wheel diameter and locomotive speed, and calculating bearing rotational speed information using the wheel diameter and locomotive speed; acquiring basic bearing information of the target locomotive, and calculating fault characteristic frequencies using the bearing rotational speed information, including outer ring fault frequency, inner ring fault frequency, rolling element fault frequency, and cage fault frequency; acquiring bearing vibration signals and motor current signals of the target locomotive during operation using a data acquisition device to obtain characteristic signals of the target locomotive; performing VMD decomposition on the characteristic signals, and using iterative methods... The decomposition method obtains the optimal decomposition parameters (K, α), where K represents the number of modes and α represents the penalty factor. The feature signal is then subjected to VMD decomposition using the optimal decomposition parameters (K, α) to obtain K IMF components, and K composite entropies corresponding to the K IMF components are calculated. The K composite entropies are compared to obtain their variation characteristics. Based on these characteristics, it is determined whether the bearing has failed. If so, the K IMF components are analyzed to obtain the target IMF component, and the envelope and envelope spectrum of the target IMF component are obtained. The fault feature frequencies are matched based on the envelope and envelope spectrum, and a warning message is generated and sent through the railway locomotive bearing diagnostic system.

[0007] In one embodiment, the feature signal undergoes VMD decomposition, and an iterative decomposition method is used to obtain the optimal decomposition parameters. This includes: setting initial values ​​for the VMD decomposition parameters (K, α) to obtain initial values ​​K0 and α0; performing VMD decomposition on the feature signal based on the initial values ​​K0 and α0 to obtain K0 IMF components; performing envelope analysis on each IMF component to calculate the initial envelope fuzzy entropy; calculating the initial scatter entropy of each IMF component and combining it with the initial envelope fuzzy entropy to obtain the composite entropy of the K0 IMF components; calculating the average value of the composite entropy of each IMF component as the total entropy of one VMD decomposition; adjusting the values ​​of K and α in the VMD decomposition parameters (K, α), performing iterative decomposition, searching for the optimal total entropy, and using the parameters (K, α) corresponding to the optimal total entropy as the optimal decomposition parameters for VMD decomposition.

[0008] In one embodiment, adjusting the values ​​of K and α in the VMD decomposition parameters (K, α) to perform iterative decomposition and search for the optimal total entropy includes: setting the maximum value K of parameter K based on the initial value K0 and the initial value α0. max and step size K i and the maximum value of parameter α α max and step size α i And configure an optimal total entropy S minInput K = K0, α = α0, calculate the corresponding total entropy S0, if S0 min Then S min =S0, and according to the step size K i and the step size α i Update parameters K and α, i.e., K = K0 + K i α=α0+α i Repeat this step until K = K max α=α0+α max Output the optimal values ​​of parameters K and α corresponding to the optimal total entropy as the optimal decomposition parameters.

[0009] In one embodiment, acquiring basic bearing information of the target locomotive and calculating fault characteristic frequencies by combining the bearing speed information includes: acquiring the basic bearing information of the target locomotive, wherein the basic bearing information includes rolling element diameter, raceway pitch diameter, bearing contact angle, and number of rolling elements; and using the rolling element diameter, raceway pitch diameter, bearing contact angle, number of rolling elements, and bearing speed information, calculating the outer ring fault frequency, the inner ring fault frequency, the rolling element fault frequency, and the cage fault frequency using the fault characteristic frequency calculation formula.

[0010] In one embodiment, the formula for calculating the fault characteristic frequency is as follows:

[0011]

[0012]

[0013]

[0014]

[0015] Among them, f o Indicates the outer ring fault frequency, f i Indicates the inner race fault frequency, f R f represents the rolling element failure frequency. c The value represents the cage failure frequency, d represents the rolling element diameter, D represents the raceway pitch diameter, β represents the bearing contact angle, z represents the number of rolling elements, and f represents the bearing speed.

[0016] ​In one embodiment, analyzing the K IMF components to obtain a target IMF component and acquiring the envelope and envelope spectrum of the target IMF component includes: analyzing the K IMF components to calculate K composite entropies corresponding to the K IMF components; comparing the frequency values ​​of the K composite entropies and obtaining the target IMF component based on the comparison results; and performing envelope analysis on the target IMF component to obtain the envelope and envelope spectrum of the target IMF component.

[0017] In one embodiment, matching the fault characteristic frequencies based on the envelope and the envelope spectrum, generating early warning information, and sending the early warning information through the railway locomotive bearing diagnostic system includes: obtaining the fundamental frequency of the target IMF component based on the envelope spectrum; comparing and matching the fundamental frequency with the outer ring fault frequency, inner ring fault frequency, rolling element fault frequency, and cage fault frequency among the fault characteristic frequencies to determine the matching fault frequency; generating the early warning information based on the matching fault frequency, and sending the early warning information through the railway locomotive bearing diagnostic system.

[0018] A diagnostic system for railway locomotive bearings includes: a status information acquisition module, used to collect operating status data of a target locomotive, including wheel diameter and locomotive speed, and calculate bearing rotational speed information using the wheel diameter and locomotive speed; a fault frequency calculation module, used to collect basic bearing information of the target locomotive and calculate fault characteristic frequencies based on the bearing rotational speed information, including outer ring fault frequency, inner ring fault frequency, rolling element fault frequency, and cage fault frequency; a bearing signal acquisition module, used to collect bearing vibration signals and motor current signals during the operation of the target locomotive using a data acquisition device to obtain characteristic signals of the target locomotive; and a parameter decomposition module, used to perform VMD decomposition on the characteristic signals. The system employs an iterative decomposition method to obtain the optimal decomposition parameters (K, α), where K represents the number of modes and α represents the penalty factor. A composite entropy acquisition module is used to perform VMD decomposition on the feature signal using the optimal decomposition parameters (K, α) to obtain K IMF components and calculate the K composite entropies corresponding to the K IMF components. A target component adjustment module compares the K composite entropies to obtain their variation characteristics, determines whether the bearing has failed based on these characteristics, and if so, analyzes the K IMF components to obtain the target IMF component and acquires its envelope and envelope spectrum. A result matching and sending module matches the fault feature frequencies based on the envelope and envelope spectrum, generates early warning information, and sends the warning information through the railway locomotive bearing diagnostic system.

[0019] The aforementioned railway locomotive bearing diagnostic method and system based on VMD adaptive parameter selection can solve the problems of complexity and inaccurate parameter values ​​in VMD analysis of railway locomotive bearings, which limit its practical application. It calculates bearing speed information based on collected target locomotive operating status data, obtains fault characteristic frequencies by collecting basic bearing information and combining it with bearing speed information, collects bearing vibration signals during locomotive operation using vibration sensors, performs VMD decomposition, and then uses an iterative decomposition method to obtain optimal decomposition parameters, resulting in K IMF components. The composite entropy is calculated and its variation characteristics are compared. By analyzing the IMF components, the target IMF component is obtained, along with its envelope and envelope spectrum. Fault characteristic frequencies are then matched, and early warning information is generated and sent through the railway locomotive bearing diagnostic system. This reduces the difficulty of use and improves the efficiency and accuracy of VMD analysis.

[0020] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating a railway locomotive bearing diagnostic method based on VMD adaptive parameter selection in one embodiment.

[0022] Figure 2 This is a flowchart illustrating the process of obtaining the optimal decomposition parameters using an iterative decomposition method in a railway locomotive bearing diagnostic method based on VMD adaptive parameter selection in one embodiment.

[0023] Figure 3 This application provides a structural schematic diagram for a railway locomotive bearing diagnostic system. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0025] like Figure 1 As shown, this application provides a railway locomotive bearing diagnostic method based on VMD adaptive parameter selection, the method comprising:

[0026] Step S100: Collect and acquire the operating status data of the target locomotive, including wheel diameter and locomotive speed, and calculate the bearing rotation speed information using the wheel diameter and locomotive speed;

[0027] Specifically, the railway locomotive bearing diagnostic system is a platform for intelligent diagnosis of locomotive bearings. The system communicates with vibration sensors, and the data acquisition device includes various sensors to collect bearing operating characteristic data of the target locomotive, such as vibration signals and motor current signals. The operating status data consists of parameters of the locomotive during operation. The bearing speed is calculated using mathematical formulas based on the wheel diameter and locomotive speed. Since the failure frequency of the locomotive varies at different speeds, calculating the bearing speed of the running locomotive is crucial for collecting the failure frequency at different speeds.

[0028] Step S200: Collect and obtain the basic bearing information of the target locomotive, and calculate the fault characteristic frequency by combining the bearing speed information. The fault characteristic frequency includes the outer ring fault frequency, inner ring fault frequency, rolling element fault frequency, and cage fault frequency.

[0029] Specifically, the outer ring failure frequency is the calculated failure frequency of the locomotive bearing's outer ring, the inner ring failure frequency is the calculated failure frequency of the locomotive bearing's inner ring, the rolling element failure frequency is the calculated failure frequency of the rolling elements contained in the locomotive bearing, and the cage failure frequency is the calculated failure frequency of the cage contained in the locomotive bearing. The failure characteristic frequencies are calculated using formulas based on the bearing's basic information and bearing speed information in order to diagnose bearing failures by analyzing and extracting these failure frequencies.

[0030] In one embodiment, step S200 of this application further includes:

[0031] Step S210: Collect and obtain the basic bearing information of the target locomotive, wherein the basic bearing information includes rolling element diameter, raceway pitch diameter, bearing contact angle, and number of rolling elements;

[0032] Step S220: Using the information on the rolling element diameter, raceway pitch diameter, bearing contact angle, number of rolling elements, and bearing speed, calculate the outer ring fault frequency, the inner ring fault frequency, the rolling element fault frequency, and the cage fault frequency using the fault characteristic frequency calculation formula;

[0033] Specifically, the rolling element diameter is the diameter of the rolling element in the bearing of the locomotive in operation, the raceway pitch diameter is the raceway pitch diameter in the bearing of the locomotive in operation, the bearing contact angle is the bearing contact angle in the bearing of the locomotive in operation, and the number of rolling elements is the number of rolling elements contained in the bearing of the locomotive in operation; the fault characteristic frequency can be calculated by formula using the bearing speed obtained by collecting the basic bearing information of the target locomotive and calculating it first.

[0034] In one embodiment, step S220 of this application further includes:

[0035] Step S221: The formula for calculating the fault characteristic frequency is as follows:

[0036]

[0037]

[0038]

[0039]

[0040] Among them, f o Indicates the outer ring fault frequency, f i Indicates the inner race fault frequency, f R f represents the rolling element failure frequency. cThe value represents the cage failure frequency, d represents the rolling element diameter, D represents the raceway pitch diameter, β represents the bearing contact angle, z represents the number of rolling elements, and f represents the bearing speed.

[0041] Step S300: Collect bearing vibration signals and motor current signals of the target locomotive during operation using a data acquisition device to obtain characteristic signals of the target locomotive;

[0042] Step S400: The feature signal is decomposed using VMD, and the optimal decomposition parameters (K, α) are obtained using an iterative decomposition method, where K represents the number of modes and α represents the penalty factor.

[0043] Specifically, locomotive bearing vibration signals are complex and easily affected by vibrations from the car body, bogies, and wheel-rail systems. VMD decomposition allows for the selection of fault-sensitive components for further analysis, enabling accurate extraction of fault characteristics and effective elimination of interference from other components. By collecting bearing vibration signals and motor current signals, and then performing VMD decomposition on the obtained characteristic signals, the system can process both vibration and motor current signals, accurately diagnosing various types of locomotive bearing faults. The main steps of the iterative decomposition method include: performing VMD decomposition on the signal to obtain multiple IMF components; performing envelope analysis on each IMF component to calculate the envelope fuzzy entropy; calculating the initial scatter entropy of each IMF component and combining it with the envelope composite entropy to form a composite entropy; calculating the average of the composite entropies of all IMF components as the total entropy of one VMD decomposition; repeatedly adjusting the VMD decomposition parameters (K, α) and recalculating the total entropy to search for the minimum total entropy; repeatedly calculating and finding the minimum total entropy, recording the parameters (K, α) corresponding to this entropy value as the optimal parameters for VMD decomposition, where K and α are both positive integers greater than 1. Using iterative decomposition methods to obtain the optimal decomposition parameters (K, α) can reduce the computational cost of obtaining parameters K and α, thereby improving the efficiency of extracting fault feature frequencies.

[0044] In one embodiment, such as Figure 2 As shown, step S400 of this application further includes:

[0045] Step S410: Set initial values ​​for VMD decomposition parameters (K, α) to obtain initial values ​​K0 and α0. Perform VMD decomposition on the feature signal based on the initial values ​​K0 and α0 to obtain K0 IMF components.

[0046] Step S420: Perform envelope analysis on each IMF component and calculate the initial envelope fuzzy entropy;

[0047] Step S430: Calculate the initial spread entropy of each IMF component and combine it with the initial envelope fuzzy entropy to obtain the composite entropy of K0 IMF components;

[0048] Step S440: Calculate the average value of the composite entropy of each IMF component as the total entropy of one VMD decomposition;

[0049] Step S450: Adjust the values ​​of K and α in the VMD decomposition parameters (K, α), perform iterative decomposition, search for the optimal total entropy, and use the parameter (K, α) corresponding to the optimal total entropy as the optimal decomposition parameter for VMD decomposition.

[0050] Specifically, the initial values ​​are determined based on practical experience to be the minimum values. K0 is the initial value of the number of modes, typically set to 3, and α0 is set to 500. The composite entropy is obtained by adding the initial scatter entropy and the initial envelope fuzzy entropy. The composite entropy comprehensively considers the fuzziness and randomness of the signal, fully describing the uncertainty of the signal, and is therefore more suitable for VMD parameter optimization. This application, targeting the characteristics of mechanical signals, combines the envelope fuzzy entropy with the scatter entropy to obtain the composite entropy. That is, first, envelope analysis is performed on the signal to highlight the signal change trend with a clear envelope line, and then the fuzzy entropy of the envelope signal is calculated. Since the number of data points is reduced by half when calculating the fuzzy entropy, the time required is greatly reduced, improving computational efficiency. Moreover, the envelope fuzzy entropy takes advantage of the low sensitivity of fuzzy entropy to sequence length, its robustness to noise, and the good continuity of the measurement value.

[0051] In one embodiment, step S450 of this application further includes:

[0052] Step S451: Based on the initial value K0 and the initial value α0, set the maximum value K of parameter K to K. max and step size K i and the maximum value of parameter α α max and step size α i And configure an optimal total entropy S min ;

[0053] Step S452: Input K = K0, α = α0, calculate the corresponding total entropy S0, if S0 min Then S min =S0, and according to the step size K i and the step size α i Update parameters K and α, i.e., K = K0 + K i α=α0+α i Repeat this step until K = K max α=α0+α max ;

[0054] Step S453: Output the optimal values ​​of parameters K and α corresponding to the optimal total entropy as the optimal decomposition parameters.

[0055] Specifically, generally speaking, K i ​This is the step size used in the algorithm, with a value of 1, and the step size α. i Take 500; K max The maximum value of the model is generally no greater than 10, and the maximum value is α. max We set it to 4000; since the mixing entropy is always less than 6, we set it to an initial value of 6, i.e., S. min =6. K0 and α0 are obtained by taking values, and the total entropy S0 is calculated, then compared with S... min Compare, if S0≥S min Then, re-evaluate K and α according to the step size K. i and step size α i Update until S0 min Then we can get S min =S0, thus obtaining the optimal values ​​of parameters K and α corresponding to the optimal total entropy. The principle of using entropy for VMD parameter selection is as follows: During the VMD parameter setting process, a smaller number of modes K and a penalty factor α may lead to mode aliasing; a smaller number of modes K and a larger penalty factor α will discard some modes as noise; a larger number of modes K and a smaller penalty factor α will add additional noise to the decomposed components; a larger number of modes K and a penalty factor α may lead to mode aliasing. Therefore, inappropriate parameter selection will increase the total entropy value of each component after decomposition. By calculating the entropy values ​​of the decomposed components under different parameter combinations (K, α), the (K, α) corresponding to the minimum value is the optimal VMD parameter.

[0056] Step S500: Perform VMD decomposition on the feature signal using the optimal decomposition parameters (K, α) to obtain K IMF components, and calculate the K composite entropies corresponding to the K IMF components;

[0057] Step S600: Compare the K composite entropies, obtain the change characteristics of the K composite entropies, determine whether the bearing has failed based on the change characteristics, and if so, analyze the K IMF components to obtain the target IMF component, and obtain the envelope and envelope spectrum of the target IMF component.

[0058] In one embodiment, step S600 of this application further includes:

[0059] Step S610: Analyze the K IMF components and calculate the K composite entropies corresponding to the K IMF components;

[0060] Step S620: Compare the frequency values ​​of the K composite entropies, and obtain the target IMF component based on the comparison results;

[0061] Step S630: Perform envelope analysis on the target IMF component to obtain the envelope and envelope spectrum of the target IMF component. ​

[0062] Specifically, by calculating the corresponding composite entropy using IMF components, the accuracy of VMD parameter selection can be improved. Then, comparative analysis is performed to obtain the envelope and envelope spectrum of the target IMF component. The K IMF components represent the fault frequency components in the original signal, ordered from lowest to highest frequency, including outer race fault frequency, inner race fault frequency, rolling element fault frequency, and cage fault frequency. The target IMF component refers to the IMF component among the K IMF components that exceeds a certain range of normal frequency. Analyzing the target IMF component facilitates rapid fault frequency analysis. The vibration signal of railway locomotive bearings is very complex. For example, the HXD3C locomotive has a maximum speed of 120 km / h and an operating speed of 100 km / h. Since the wheel diameter is 1250 mm, the calculated axle speed is 424 r / min. At this speed, the fault characteristic frequencies of the bearing housing were obtained as follows: outer ring fault frequency 54.8Hz, inner ring fault frequency 72.5Hz, rolling element fault frequency 24.9Hz, and cage fault frequency 3.0Hz. Applying VMD to the vibration signal and automatically determining parameters yielded seven Intrinsic Mode Functions (IMFs). The mixed entropy showed good performance in detecting frequency changes, periodic amplitude changes with frequency, and dynamic changes in the mechanical system. The increase in mixed entropy indicates the emergence of new frequencies within the corresponding frequency range. Calculating the mixed entropy of each IMF, the value of IMF4 increased by 21% compared to normal conditions, indicating that bearing failure led to the emergence of new frequencies. Therefore, IMF4 was selected, and its envelope and envelope spectrum were analyzed. Using the fault frequencies obtained above and sorting the IMF components to obtain the target IMF components, when the outer ring fault frequency of the bearing was 56Hz, its second and third harmonics were quite significant. The presence of these significant peaks at the characteristic fault frequencies of the envelope spectrum indicates a significant fault in the outer raceway. In summary, VMD analysis can effectively extract the fault characteristics of locomotive bearings.

[0063] Step S700: Match the fault characteristic frequencies according to the envelope and the envelope spectrum, generate early warning information, and send the early warning information through the railway locomotive bearing diagnostic system;

[0064] In one embodiment, step S700 of this application further includes:

[0065] Step S710: Obtain the fundamental frequency of the target IMF component based on the envelope spectrum;

[0066] Step S720: Compare and match the fundamental frequency with the outer ring fault frequency, inner ring fault frequency, rolling element fault frequency, and cage fault frequency among the fault characteristic frequencies to determine the matching fault frequency;

[0067] Step S730: Generate the warning information according to the matching fault frequency, and send the warning information through the railway locomotive bearing diagnostic system.

[0068] Specifically, the fundamental frequency of the target IMF component can be obtained by filtering out higher harmonics through low-pass filtering to obtain the frequency of the required fault signal. By highlighting the low-frequency changes of the signal, it is beneficial to select VMD parameters. Then, by matching the fundamental frequency with the fault frequency, the specific location of the fault can be obtained, and then an early warning information can be issued. This achieves the technical effect of quickly selecting VMD parameters and accurately extracting fault features.

[0069] In one embodiment, such as Figure 3 The diagram shows a diagnostic system for railway locomotive bearings, comprising:

[0070] Status information acquisition module 1 is used to collect and acquire the operating status data of the target locomotive. The operating status data includes wheel diameter and locomotive operating speed, and the bearing rotation speed information is calculated by using the wheel diameter and locomotive operating speed.

[0071] Fault frequency calculation module 2 is used to collect and obtain the basic bearing information of the target locomotive, and calculate the fault characteristic frequency by combining the bearing speed information. The fault characteristic frequency includes the outer ring fault frequency, inner ring fault frequency, rolling element fault frequency, and cage fault frequency.

[0072] The bearing signal acquisition module 3 is used to acquire the bearing vibration signal and motor current signal of the target locomotive during operation through a data acquisition device, so as to obtain the characteristic signal of the target locomotive.

[0073] The parameter decomposition acquisition module 4 is used to perform VMD decomposition on the feature signal and use an iterative decomposition method to obtain the optimal decomposition parameters (K, α), where K represents the number of modes and α represents the penalty factor.

[0074] The composite entropy acquisition module 5 is used to perform VMD decomposition on the feature signal using the optimal decomposition parameters (K, α) to obtain K IMF components and calculate the K composite entropies corresponding to the K IMF components.

[0075] The target component adjustment module 6 is used to compare the K composite entropies, obtain the change characteristics of the K composite entropies, determine whether the bearing has failed based on the change characteristics, and if so, analyze the K IMF components to obtain the target IMF component, and obtain the envelope and envelope spectrum of the target IMF component.

[0076] Result matching and sending module 7 is used to match the fault characteristic frequencies according to the envelope and the envelope spectrum, generate early warning information, and send the early warning information through the railway locomotive bearing diagnostic system.

[0077] Furthermore, the fault frequency calculation module 2 is also used for:

[0078] The basic information of the bearings of the target locomotive is collected and acquired, wherein the basic information of the bearings includes the rolling element diameter, raceway pitch diameter, bearing contact angle, and number of rolling elements;

[0079] Furthermore, the fault frequency calculation module 2 is also used for:

[0080] The formula for calculating the fault characteristic frequency is as follows:

[0081]

[0082]

[0083]

[0084]

[0085] Among them, f o Indicates the outer ring fault frequency, f i Indicates the inner race fault frequency, f R f represents the rolling element failure frequency. c The value represents the cage failure frequency, d represents the rolling element diameter, D represents the raceway pitch diameter, β represents the bearing contact angle, z represents the number of rolling elements, and f represents the bearing speed.

[0086] Furthermore, the parameter decomposition obtaining module 4 is also used for:

[0087] The VMD decomposition parameters (K, α) are initialized to obtain initial values ​​K0 and α0. The feature signal is then decomposed using the initial values ​​K0 and α0 to obtain K0 IMF components.

[0088] Envelope analysis is performed on each IMF component to calculate the initial envelope fuzzy entropy;

[0089] Calculate the initial scattering entropy of each IMF component and combine it with the initial envelope fuzzy entropy to obtain the composite entropy of K0 IMF components;

[0090] Calculate the average value of the composite entropy of each IMF component as the total entropy of one VMD decomposition;

[0091] Adjust the values ​​of K and α in the VMD decomposition parameters (K, α), perform iterative decomposition, search for the optimal total entropy, and use the parameters (K, α) corresponding to the optimal total entropy as the optimal decomposition parameters for VMD decomposition.

[0092] Furthermore, the parameter decomposition obtaining module 4 is also used for:

[0093] Based on the initial value K0 and the initial value α0, set the maximum value K of parameter K to K0. max and step size K i and the maximum value of parameter α α max and step size α i And configure an optimal total entropy S min ;

[0094] Input K = K0, α = α0, calculate the corresponding total entropy S0, if S0 min Then S min =S0, and according to the step size K i and the step size α i Update parameters K and α, i.e., K = K0 + K i α=α0+α i Repeat this step until K = K max α=α0+α max ;

[0095] The optimal values ​​of parameters K and α corresponding to the optimal total entropy are output as the optimal decomposition parameters.

[0096] Furthermore, the target component adjustment module 6 is also used for:

[0097] The K IMF components are analyzed to calculate the K composite entropies corresponding to the K IMF components;

[0098] The frequency values ​​of the K composite entropies are compared, and the target IMF component is obtained based on the comparison results.

[0099] Envelope analysis is performed on the target IMF component to obtain the envelope and envelope spectrum of the target IMF component.

[0100] Furthermore, the result matching and sending module 7 is also used for:

[0101] The fundamental frequency of the target IMF component is obtained based on the envelope spectrum;​

[0102] The fundamental frequency is compared and matched with the outer ring fault frequency, inner ring fault frequency, rolling element fault frequency, and cage fault frequency among the fault characteristic frequencies to determine the matching fault frequency.

[0103] The warning information is generated based on the matched fault frequency and sent through the railway locomotive bearing diagnostic system.

[0104] In summary, this application provides a synchronization control method and system for automated equipment, which has the following technical advantages:

[0105] 1. Based on the characteristics of locomotive bearing radiation signals, this application first performs VMD decomposition on the vibration signals emitted by the locomotive bearings and the motor current signals, and then uses the same mixed entropy to find the IMF components that are sensitive to faults. Further analysis is performed to extract fault characteristic frequencies and diagnose locomotive bearing faults. This achieves the technical effect of being able to process both vibration signals and motor current signals and accurately diagnose various types of locomotive bearing faults.

[0106] 2. By first performing envelope analysis on the signal to highlight the signal's changing trend with a clear envelope line, and then calculating the fuzzy entropy of the envelope signal, the computational efficiency is further improved and the VMD analysis time is reduced by half because the number of data points is reduced when calculating the fuzzy entropy.

[0107] 3. Obtaining the optimal decomposition parameters (K, α) through iterative decomposition can reduce the computational cost of obtaining parameters K and α, thereby improving the efficiency of extracting fault feature frequencies.

[0108] 4. Using the fundamental frequency of the obtained target IMF component, higher harmonics can be filtered out by low-pass filtering to obtain the frequency of the required fault signal. By highlighting the low-frequency changes of the signal, it is beneficial to select VMD parameters, reduce interference from surrounding signals, and greatly improve the accuracy of VMD analysis.

[0109] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0110] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A railway locomotive bearing diagnostic method based on VMD adaptive parameter selection, characterized in that, The method is applied to a railway locomotive bearing diagnostic system, the system being communicatively connected to a data acquisition device, and the method includes: The operating status data of the target locomotive is collected and acquired. The operating status data includes wheel diameter and locomotive speed. The bearing rotation speed information is calculated by using the wheel diameter and locomotive speed. The basic bearing information of the target locomotive is collected and obtained, and the fault characteristic frequency is calculated by combining the bearing speed information. The fault characteristic frequency includes the outer ring fault frequency, inner ring fault frequency, rolling element fault frequency, and cage fault frequency. The bearing vibration signal and motor current signal of the target locomotive are collected by a data acquisition device during operation to obtain the characteristic signal of the target locomotive. The feature signal is decomposed using VMD, and the optimal decomposition parameters (K, α) are obtained by using an iterative decomposition method, where K represents the number of modes and α represents the penalty factor. The feature signal is decomposed using the optimal decomposition parameters (K, α) to obtain K IMF components, and the K composite entropies corresponding to the K IMF components are calculated. By comparing the K composite entropies, the change characteristics of the K composite entropies are obtained. Based on the change characteristics, it is determined whether the bearing has failed. If so, the K IMF components are analyzed to obtain the target IMF component, and the envelope and envelope spectrum of the target IMF component are obtained. The fault characteristic frequencies are matched based on the envelope and the envelope spectrum, and early warning information is generated and sent through the railway locomotive bearing diagnostic system.

2. The method as described in claim 1, characterized in that, The feature signal is subjected to VMD decomposition, and the optimal decomposition parameters are obtained using an iterative decomposition method, including: The VMD decomposition parameters (K, α) are initialized to obtain initial values ​​K0 and α0. The feature signal is then decomposed using the initial values ​​K0 and α0 to obtain K0 IMF components. Envelope analysis is performed on each IMF component to calculate the initial envelope fuzzy entropy; Calculate the initial discrete entropy of each IMF component and combine it with the initial envelope fuzzy entropy to obtain the composite entropy of K0 IMF components; Calculate the average value of the composite entropy of each IMF component as the total entropy of one VMD decomposition; Adjust the values ​​of K and α in the VMD decomposition parameters (K, α), perform iterative decomposition, search for the optimal total entropy, and use the parameters (K, α) corresponding to the optimal total entropy as the optimal decomposition parameters for VMD decomposition.

3. The method as described in claim 2, characterized in that, Adjust the values ​​of K and α in the VMD decomposition parameters (K, α), perform iterative decomposition, and search for the optimal total entropy, including: Based on the initial value K0 and the initial value α0, set the maximum value K of parameter K to K0. max and step size K i and the maximum value of parameter α α max and step size α i And configure an optimal total entropy S min ; Input K = K0, α = α0, calculate the corresponding total entropy S0, if S0 < S min Then S min =S0, and according to the step size K i and the step size α i Update parameters K and α, i.e., K = K0 + K i α=α0+α i Repeat this step until K = K max α=α0+α max ; The optimal values ​​of parameters K and α corresponding to the optimal total entropy are output as the optimal decomposition parameters.

4. The method as described in claim 1, characterized in that, The basic bearing information of the target locomotive is collected, and the fault characteristic frequency is calculated by combining the bearing speed information, including: The basic information of the bearings of the target locomotive is collected and acquired, wherein the basic information of the bearings includes the rolling element diameter, raceway pitch diameter, bearing contact angle, and number of rolling elements; Using the information on the rolling element diameter, raceway pitch diameter, bearing contact angle, number of rolling elements, and bearing speed, the failure frequencies of the outer ring, inner ring, rolling elements, and cage are calculated using the formula for calculating the failure characteristic frequencies.

5. The method as described in claim 4, characterized in that, The formula for calculating the fault characteristic frequency is as follows: Among them, f o Indicates the outer ring fault frequency, f i Indicates the inner race fault frequency, f R f represents the rolling element failure frequency. c The value represents the cage failure frequency, d represents the rolling element diameter, D represents the raceway pitch diameter, β represents the bearing contact angle, z represents the number of rolling elements, and f represents the bearing speed.

6. The method as described in claim 5, characterized in that, Analyze the K IMF components to obtain the target IMF component, and acquire the envelope and envelope spectrum of the target IMF component, including: The K IMF components are analyzed to calculate the K composite entropies corresponding to the K IMF components; The frequency values ​​of the K composite entropies are compared, and the target IMF component is obtained based on the comparison results. Envelope analysis is performed on the target IMF component to obtain the envelope and envelope spectrum of the target IMF component.

7. The method as described in claim 1, characterized in that, The fault characteristic frequencies are matched based on the envelope and the envelope spectrum, and a warning message is generated and sent through the railway locomotive bearing diagnostic system, including: The fundamental frequency of the target IMF component is obtained based on the envelope spectrum; The fundamental frequency is compared and matched with the outer ring fault frequency, inner ring fault frequency, rolling element fault frequency, and cage fault frequency among the fault characteristic frequencies to determine the matching fault frequency. The warning information is generated based on the matched fault frequency and sent through the railway locomotive bearing diagnostic system.

8. A diagnostic system for railway locomotive bearings, characterized in that, The system includes: The status information acquisition module is used to collect and acquire the operating status data of the target locomotive, including wheel diameter and locomotive operating speed, and to calculate the bearing rotation speed information through the wheel diameter and locomotive operating speed. The fault frequency calculation module is used to collect and obtain the basic bearing information of the target locomotive, and calculate the fault characteristic frequency by combining the bearing speed information. The fault characteristic frequency includes the outer ring fault frequency, inner ring fault frequency, rolling element fault frequency, and cage fault frequency. The bearing signal acquisition module is used to acquire bearing vibration signals and motor current signals of the target locomotive during operation through a data acquisition device, thereby obtaining characteristic signals of the target locomotive. The parameter decomposition acquisition module is used to perform VMD decomposition on the feature signal and use an iterative decomposition method to obtain the optimal decomposition parameters (K, α), where K represents the number of modes and α represents the penalty factor. The composite entropy acquisition module is used to perform VMD decomposition on the feature signal using the optimal decomposition parameters (K, α) to obtain K IMF components, and calculate the K composite entropies corresponding to the K IMF components. The target component adjustment module is used to compare the K composite entropies, obtain the change characteristics of the K composite entropies, determine whether the bearing has failed based on the change characteristics, and if so, analyze the K IMF components to obtain the target IMF component, and obtain the envelope and envelope spectrum of the target IMF component. The result matching and sending module is used to match the fault characteristic frequencies based on the envelope and the envelope spectrum, generate early warning information, and send the early warning information through the railway locomotive bearing diagnostic system.

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