Axle box fault identification method and device, electronic equipment and storage medium

Abstract

This application provides a method and electronic device for identifying axle box faults, as well as a computer-readable storage medium. The method includes: acquiring vibration data and sound data generated by the axle box to be identified; wherein the axle box to be identified is the main roller axle box of a multi-wire cutting machine; determining the proportion of normal vibration time-domain signals based on the amplitude of each vibration time-domain signal in the vibration data and a first amplitude threshold; determining the proportion of normal sound time-domain signals based on the amplitude of each sound time-domain signal in the sound data and a second amplitude threshold; and determining whether the axle box to be identified is in a faulty state based on the proportion of normal vibration time-domain signals and the proportion of normal sound time-domain signals. This solution can achieve automated fault identification based on the vibration and sound data generated during the operation of the axle box, thereby improving the accuracy and timeliness of fault detection.

Description

Technical Field

[0001] This application relates to the field of equipment fault detection technology, and in particular to a method and device for identifying axle box faults, electronic equipment, and computer-readable storage medium. Background Technology

[0002] In multi-wire EDM, cutting wires are often spirally arranged at a certain interval on multiple main rollers. The multi-wire operation is achieved through grinding between the wire mesh and the workpiece. In current industrial applications, the two ends of the main rollers are usually connected to two front and rear axle boxes. Adjusting the distance between the two main rollers is equivalent to adjusting the distance between the two pairs of front and rear axle boxes. During the rotation of the main rollers by a motor, factors such as excessive temperature or excessive load may cause axle box malfunctions.

[0003] In related technologies, axle box faults are diagnosed by operators based on their own work experience. However, manual fault detection has low accuracy and may fail to detect faults in a timely manner. Summary of the Invention

[0004] The purpose of this application is to provide a method and electronic device for axle box fault identification, as well as a computer-readable storage medium, for automatically identifying faults based on vibration and sound data generated during axle box operation, thereby improving the accuracy and timeliness of fault detection.

[0005] On the one hand, this application provides a method for identifying axle box faults, including:

[0006] The vibration and sound data generated by the shaft box to be identified are acquired; wherein, the shaft box to be identified is the main roller shaft box in a multi-wire cutting machine.

[0007] The proportion of normal vibration time-domain signals is determined based on the amplitude of each vibration time-domain signal in the vibration data and a first amplitude threshold; wherein, the first amplitude threshold is obtained based on the vibration time-domain signal generated by the sample axle box under non-fault conditions;

[0008] The proportion of normal sound time-domain signals is determined based on the amplitude of each sound time-domain signal in the sound data and a second amplitude threshold; wherein, the second amplitude threshold is obtained based on the sound time-domain signal generated by the sample shaft box under non-fault conditions;

[0009] Based on the proportion of normal vibration time-domain signal and the proportion of normal sound time-domain signal, it is determined whether the axle box to be identified is in a fault state.

[0010] In one embodiment, determining whether the axle box to be identified is in a fault state based on the proportion of normal vibration time-domain signal and the proportion of normal sound time-domain signal includes:

[0011] The proportion of the normal vibration time-domain signal is compared with the normal vibration proportion threshold to obtain a first comparison result; wherein, the normal vibration proportion threshold is obtained based on the vibration time-domain signal generated by the sample axle box under fault conditions;

[0012] The proportion of the normal sound time-domain signal is compared with the normal sound proportion threshold to obtain a second comparison result; wherein, the normal sound proportion threshold is obtained based on the sound time-domain signal generated by the sample shaft box under fault conditions;

[0013] Based on the first comparison result and the second comparison result, it is determined whether the axle box to be identified is in a faulty state.

[0014] In one embodiment, determining whether the axle box to be identified is in a fault state based on the first comparison result and the second comparison result includes:

[0015] If the proportion of the normal vibration time domain signal of the axle box to be identified is not greater than the normal vibration proportion threshold, and the proportion of the normal sound time domain signal of the axle box to be identified is not greater than the normal sound proportion threshold, then the axle box to be identified is determined to be in a fault state.

[0016] In one embodiment, determining whether the axle box to be identified is in a fault state based on the proportion of normal vibration time-domain signal and the proportion of normal sound time-domain signal includes:

[0017] If the proportion of the normal vibration time domain signal is not greater than the normal vibration proportion threshold, and the proportion of the normal sound time domain signal is greater than the normal sound proportion threshold, then it is determined whether the duration of the normal vibration time domain signal proportion not being greater than the normal vibration proportion threshold exceeds the first preset duration.

[0018] If the value exceeds the limit, the axle box to be identified is determined to be in a faulty state.

[0019] In one embodiment, determining whether the axle box to be identified is in a fault state based on the proportion of normal vibration time-domain signal and the proportion of normal sound time-domain signal includes:

[0020] If the proportion of the normal vibration time domain signal is greater than the normal vibration proportion threshold, and the proportion of the normal sound time domain signal is not greater than the normal sound proportion threshold, then it is determined whether the duration of the normal sound time domain signal proportion not being greater than the normal sound proportion threshold exceeds the second preset duration.

[0021] If the value exceeds the limit, the axle box to be identified is determined to be in a faulty state.

[0022] In one embodiment, the step of obtaining the first amplitude threshold includes:

[0023] Acquire sample vibration data when the sample shaft box is in a non-faulty state, wherein the sample vibration data includes multiple sample vibration time-domain signals;

[0024] Calculate the mean amplitude of the time-domain signal of the sample vibration in the sample vibration data, and use it as the vibration reference value;

[0025] The first amplitude threshold is determined based on the vibration reference value.

[0026] In one embodiment, the step of obtaining the second amplitude threshold includes:

[0027] Acquire sample sound data when the sample shaft box is in a non-faulty state, wherein the sample sound data includes multiple sample sound time-domain signals;

[0028] Calculate the mean amplitude of the time-domain signal of the sample sound in the sample sound data, and use it as the sound reference value;

[0029] The second amplitude threshold is determined based on the sound reference value.

[0030] In one embodiment, the step of obtaining the normal vibration percentage threshold includes:

[0031] Obtain N sets of fault vibration data for the sample axle box under fault conditions; where N is an integer greater than 1.

[0032] Obtain the proportion of N normal vibration time-domain signals from the N sets of fault vibration data;

[0033] The threshold for the proportion of normal vibrations is determined based on the proportion of the N normal vibration time-domain signals.

[0034] In one embodiment, the step of obtaining the normal sound proportion threshold includes:

[0035] Obtain M sets of fault sound data when the sample axle box is in a fault state; where M is an integer greater than 1;

[0036] Obtain the percentage of the M normal sound time-domain signals from the M sets of fault sound data;

[0037] The normal sound proportion threshold is determined based on the proportion of the M normal sound time-domain signals.

[0038] On the other hand, this application provides an axle box fault identification device, comprising:

[0039] The acquisition module is used to acquire vibration and sound data generated by the shaft box to be identified; wherein, the shaft box to be identified is the main roller shaft box in a multi-wire cutting machine;

[0040] The first determining module is used to determine the proportion of normal vibration time-domain signals based on the amplitude of each vibration time-domain signal in the vibration data and a first amplitude threshold; wherein, the first amplitude threshold is obtained based on the vibration time-domain signals generated by the sample shaft box under non-fault conditions;

[0041] The second determining module is used to determine the proportion of normal sound time-domain signals based on the amplitude of each sound time-domain signal in the sound data and a second amplitude threshold; wherein, the second amplitude threshold is obtained based on the sound time-domain signals generated by the sample shaft box under non-fault conditions;

[0042] The identification module is used to determine whether the axle box to be identified is in a fault state based on the proportion of normal vibration time-domain signal and the proportion of normal sound time-domain signal.

[0043] Furthermore, this application provides an electronic device, the electronic device comprising:

[0044] processor;

[0045] Memory used to store processor-executable instructions;

[0046] The processor is configured to execute the aforementioned axle box fault identification method.

[0047] In addition, this application provides a computer-readable storage medium storing a computer program that can be executed by a processor to perform the above-described axle box fault identification method.

[0048] In this application, during the operation of the axle box to be identified, the proportion of normal vibration time-domain signal and the proportion of normal sound time-domain signal can be calculated based on the collected vibration and sound data. Since the proportion of normal vibration time-domain signal can characterize the overall normality of the vibration dimension during the operation of the axle box, and the proportion of normal sound time-domain signal can characterize the overall normality of the sound dimension during the operation of the axle box, the axle box to be identified can be automatically identified by combining the two parameters of vibration dimension and sound dimension, thus solving the problem of insufficient accuracy and timeliness that may exist in manual fault identification. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly described below.

[0050] Figure 1 A flowchart illustrating a method for identifying axle box faults according to an embodiment of this application;

[0051] Figure 2 A schematic diagram of vibration data provided in an embodiment of this application;

[0052] Figure 3 A schematic diagram of vibration data provided in another embodiment of this application;

[0053] Figure 4 A schematic diagram of sound data provided in an embodiment of this application;

[0054] Figure 5 A schematic diagram illustrating the specific process of a fault identification method provided in an embodiment of this application;

[0055] Figure 6 A flowchart illustrating a method for determining a first amplitude threshold provided in an embodiment of this application;

[0056] Figure 7 A flowchart illustrating a method for determining a second amplitude threshold provided in an embodiment of this application;

[0057] Figure 8 A flowchart illustrating a method for determining the normal vibration percentage threshold provided in an embodiment of this application;

[0058] Figure 9 A flowchart illustrating a method for determining the normal sound percentage threshold provided in an embodiment of this application;

[0059] Figure 10 A block diagram of an axle box fault identification device provided in an embodiment of this application;

[0060] Figure 11 This is a schematic diagram illustrating an application scenario of the axle box fault identification method provided in an embodiment of this application;

[0061] Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0062] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0063] Similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0064] See Figure 1 This is a flowchart illustrating a method for identifying axle box faults according to an embodiment of this application. Figure 1 As shown, the method may include steps 110-140.

[0065] Step 110: Obtain vibration and sound data generated by the shaft box to be identified within the same time period; wherein, the shaft box to be identified is the main roller shaft box in the multi-wire cutting machine.

[0066] In practice, vibration data includes multiple vibration time-domain signals, and similarly, sound data includes multiple sound time-domain signals.

[0067] This solution is used for fault identification of the spindle boxes on a multi-wire cutting machine. For each spindle box to be identified on the multi-wire cutting machine, corresponding vibration and sound acquisition devices can be installed. The vibration acquisition device is used to collect vibration data, and the sound acquisition device is used to collect sound data. For example, the vibration acquisition device can be a vibration acceleration sensor or an eddy current sensor; the sound acquisition device can be a sound sensor.

[0068] The vibration frequency of the axle box is approximately below 1000 Hz. During sampling, the sampling frequency must adhere to Shannon's sampling theorem. The sampling theorem states that the sampling frequency fs must be at least twice the highest frequency of the signal of interest. Half of the sampling frequency is called the Nyquist frequency, also known as the analysis bandwidth, or simply bandwidth. When the sampling frequency setting does not satisfy the sampling theorem (i.e., the sampling frequency is less than twice the signal frequency), the originally high-frequency signal will be sampled as a low-frequency signal, resulting in lower data accuracy. Conversely, using a frequency higher than twice the signal frequency ensures a higher degree of matching between the acquired data and the frequency, leading to higher data accuracy.

[0069] See Figure 2 This is a schematic diagram of vibration data provided in an embodiment of this application. With the sampling frequency set to 5000 Hz, the vibration data consists of multiple vibration time-domain signals collected, as shown below. Figure 2 As shown, the amplitude of the vibration time-domain signal includes both positive and negative directions.

[0070] See Figure 3 This is a schematic diagram of vibration data provided in another embodiment of this application. With the sampling frequency set to 12000 Hz, the vibration data composed of multiple vibration time-domain signals collected is as follows: Figure 3 As shown.

[0071] During the current cutting process, the multi-wire dicing machine can continuously collect vibration time-domain signals through the vibration acquisition equipment corresponding to the shaft box to be identified. When the multi-wire dicing machine first starts, the amplitude of the vibration time-domain signal fluctuates significantly. Once the cutting time reaches a preset threshold, the vibration time-domain signals collected during that period can be combined to form vibration data, and the sound time-domain signals collected during that period can be combined to form sound data. Here, "one cut" refers to the cutting process of the multi-wire dicing machine cutting a silicon rod into a silicon wafer. The time threshold can be configured based on experience; for example, the time threshold can be between 1 and 5 minutes, such as 3 minutes, 4 minutes, or 5 minutes.

[0072] See Figure 4 This is a schematic diagram of sound data provided in an embodiment of this application, as shown below. Figure 4 As shown, multiple sound time-domain signals collected within the same time period constitute sound data, and the amplitude of the sound time-domain signals includes both positive and negative directions.

[0073] In some embodiments of this application, when acquiring vibration and sound data within the same time period, different values ​​can be set for the time period based on different actual situations. For example, the duration of the vibration time-domain signal can be set according to the working accuracy of the axle box to be identified. For axle boxes with high working accuracy, which are more sensitive to axle box failures, the time period can be set shorter, such as 3 seconds to 5 minutes, to acquire shorter duration vibration and sound time-domain signals as vibration and sound data. Conversely, for axle boxes with lower working accuracy, the time period can be set longer, such as 4 minutes to 20 minutes, to acquire longer duration vibration and sound time-domain signals as vibration and sound data. Specifically, the duration of the time period can be flexibly set according to the actual situation.

[0074] During the current cutting process, the multi-wire cutting machine can continuously collect sound time-domain signals through the sound acquisition equipment corresponding to the shaft box to be identified. When the multi-wire cutting machine first starts, the amplitude of the sound time-domain signal fluctuates significantly. Once the cutting time reaches a preset time threshold, the sound time-domain signals collected during this period can be used to construct sound data for the initial fault identification. Here, the time threshold is the same as the previously mentioned time threshold.

[0075] In one embodiment, due to the complex composition of the multi-wire cutting machine, and the influence of factors such as load changes and component interactions during operation, the acquired raw vibration and sound data contain a large amount of noise. Directly using the acquired raw vibration and sound data for fault identification may result in low identification accuracy. To solve this problem, denoising algorithms can be used to denoise the vibration and sound data separately, thereby using the denoised vibration and sound data for subsequent fault identification, effectively improving identification accuracy. Here, denoising algorithms may include, but are not limited to, Singular Value Decomposition (SVD) and wavelet thresholding methods.

[0076] Step 120: Determine the proportion of normal vibration time domain signals based on the amplitude of each vibration time domain signal in the vibration data and the first amplitude threshold; wherein, the first amplitude threshold is obtained based on the vibration time domain signal generated by the sample axle box under non-fault conditions.

[0077] In step 120, the absolute amplitude of each vibration time-domain signal in the vibration data can be compared with the first amplitude threshold to determine the proportion of normal vibration time-domain signals; alternatively, each vibration time-domain signal in the vibration data can be normalized, and the amplitude of each normalized vibration time-domain signal can be compared with the first amplitude threshold to determine the proportion of normal vibration time-domain signals.

[0078] The first amplitude threshold is used to filter normal vibration time-domain signals. For each axle box to be identified, a corresponding first amplitude threshold can be pre-configured. In one embodiment, the first amplitude threshold can be obtained based on the vibration time-domain signal generated by the sample axle box under non-fault conditions, as detailed in the following description.

[0079] In one embodiment, the amplitude of each vibration time-domain signal is compared with a first amplitude threshold one by one, and the amplitude signals with amplitudes less than the first amplitude threshold are identified as normal vibration time-domain signals. After determining all normal vibration time-domain signals in the vibration data, the number of all normal vibration time-domain signals can be counted, and then the proportion of normal vibration time-domain signals can be determined by using this number and the total number of signals in the vibration data.

[0080] For example, the proportion of normal vibration time-domain signal can be represented by the following formula (1):

[0081] L=T1 / S (1)

[0082] Where L is the proportion of normal vibration time-domain signals; T1 is the number of normal vibration time-domain signals; and S is the total amount of vibration time-domain signals in the vibration data.

[0083] In one embodiment, the amplitude of each vibration time-domain signal is compared with a first amplitude threshold one by one, and the amplitude signals with amplitudes not less than the first amplitude threshold are identified as abnormal vibration time-domain signals. After identifying all abnormal vibration time-domain signals in the vibration data, the number of all abnormal vibration time-domain signals can be counted, and then the proportion of normal vibration time-domain signals can be determined by using this number and the total number of signals in the vibration data.

[0084] For example, the proportion of normal vibration time-domain signal can be represented by the following formula (2):

[0085] L=(S-T2) / S (2)

[0086] Where L is the proportion of normal vibration time-domain signals; T2 is the number of abnormal vibration time-domain signals; and S is the total amount of vibration time-domain signals in the vibration data.

[0087] Step 130: Determine the proportion of normal sound time-domain signals based on the amplitude of each sound time-domain signal in the sound data and the second amplitude threshold; wherein, the second amplitude threshold is obtained based on the sound time-domain signal generated by the axle box to be identified under non-fault conditions.

[0088] In step 130, the absolute amplitude of each sound time-domain signal in the sound data can be compared with the second amplitude threshold to determine the proportion of normal sound time-domain signals; alternatively, each sound time-domain signal in the sound data can be normalized, and the amplitude of each normalized sound time-domain signal can be compared with the second amplitude threshold to determine the proportion of normal sound time-domain signals.

[0089] The second amplitude threshold is used to filter normal sound time-domain signals. For each axle box to be identified, a corresponding second amplitude threshold can be pre-configured. In one embodiment, the second amplitude threshold can be obtained based on the sound time-domain signal generated by a sample axle box under non-fault conditions, as detailed in the following description.

[0090] In one embodiment, the amplitude of each audio time-domain signal is compared with a second amplitude threshold one by one, and audio time-domain signals with amplitudes less than the second amplitude threshold are identified as normal audio time-domain signals. After identifying all normal audio time-domain signals in the audio data, the number of all normal audio time-domain signals can be counted, and then the proportion of normal audio time-domain signals can be determined by using this number and the total number of signals in the audio data.

[0091] For example, the proportion of normal sound time-domain signal can be represented by the following formula (3):

[0092] H = F1 / W (2)

[0093] Where H represents the proportion of normal sound time-domain signals; F1 represents the number of normal sound time-domain signals; and W represents the total amount of sound time-domain signals in the sound data.

[0094] In one embodiment, the amplitude of each audio time-domain signal is compared with a second amplitude threshold one by one, and audio time-domain signals with an amplitude not less than the second amplitude threshold are identified as abnormal audio time-domain signals. After identifying all abnormal audio time-domain signals in the audio data, the number of all abnormal audio time-domain signals can be counted, and then the proportion of normal audio time-domain signals can be determined by using this number and the total number of signals in the audio data.

[0095] For example, the proportion of normal sound time-domain signal can be represented by the following formula (4):

[0096] H=(W-F2) / W (4)

[0097] Where H represents the proportion of normal sound time-domain signals; F2 represents the number of abnormal sound time-domain signals; and W represents the total amount of sound time-domain signals in the sound data.

[0098] Step 140: Determine whether the axle box to be identified is in a fault state based on the proportion of normal vibration time domain signal and the proportion of normal sound time domain signal.

[0099] After obtaining the proportion of normal vibration time-domain signal and the proportion of normal sound time-domain signal, the operating status of the axle box to be identified can be determined based on the proportion of normal vibration time-domain signal and the proportion of normal sound time-domain signal, thus determining whether there is a fault.

[0100] Through the above measures, during the operation of the axle box to be identified, the proportion of normal vibration time-domain signal and the proportion of normal sound time-domain signal can be calculated based on the collected vibration and sound data. Since the proportion of normal vibration time-domain signal can characterize the overall normality of the vibration dimension during the operation of the axle box, and the proportion of normal sound time-domain signal can characterize the overall normality of the sound dimension during the operation of the axle box, the axle box to be identified can be automatically identified by combining the two parameters of vibration and sound dimensions, thus solving the problem of insufficient accuracy and timeliness that may exist in manual fault identification.

[0101] In one embodiment, see Figure 5 The diagram below illustrates the specific process of a fault identification method provided in an embodiment of this application. Figure 5 As shown, the method may include steps 510 to 530.

[0102] Step 510: Compare the proportion of normal vibration time domain signal with the normal vibration proportion threshold to obtain the first comparison result; wherein, the normal vibration proportion threshold is obtained based on the vibration time domain signal generated by the sample axle box under fault conditions.

[0103] The normal vibration percentage threshold can be configured as needed. For example, the normal vibration percentage threshold can be between 20% and 30%. For instance, the normal vibration percentage threshold could be 28%. In one embodiment, the normal vibration percentage threshold can be obtained based on the vibration time-domain signal generated by the sample shaft box under fault conditions, as detailed in the following description.

[0104] After obtaining the proportion of normal vibration time-domain signal, the proportion of normal vibration time-domain signal is compared with a normal vibration proportion threshold to obtain a first comparison result. The first comparison result is either that the proportion of normal vibration time-domain signal is greater than the normal vibration proportion threshold, or that the proportion of normal vibration time-domain signal is not greater than the normal vibration proportion threshold.

[0105] Step 520: Compare the proportion of normal sound time domain signal with the normal sound proportion threshold to obtain a second comparison result; wherein, the normal sound proportion threshold is obtained based on the sound time domain signal generated by the sample shaft box under fault conditions.

[0106] The normal sound percentage threshold can be configured as needed. For example, the normal sound percentage threshold can be between 18% and 32%. For instance, the normal sound percentage threshold could be 25%. In one embodiment, the normal sound percentage threshold can be obtained based on the time-domain sound signal generated by the sample shaft box under fault conditions, as detailed in the following description.

[0107] After obtaining the proportion of normal sound time-domain signal, this proportion is compared with a normal sound proportion threshold to obtain a second comparison result. The second comparison result is either that the proportion of normal sound time-domain signal is greater than the normal sound proportion threshold, or that the proportion of normal sound time-domain signal is not greater than the normal sound proportion threshold.

[0108] Step 530: Based on the first comparison result and the second comparison result, determine whether the axle box to be identified is in a faulty state.

[0109] After obtaining the first comparison result and the second comparison result, it can be determined whether the axle box to be identified has a fault.

[0110] By comparing the normal vibration percentage threshold with the normal vibration time domain signal percentage, and comparing the normal sound percentage threshold with the normal sound time domain signal percentage, a first comparison result and a second comparison result indicating whether the vibration condition is qualified and whether the sound condition is qualified can be obtained. Thus, the operating status of the axle box to be identified can be determined based on the first comparison result and the second comparison result.

[0111] In one embodiment, if the proportion of normal vibration time-domain signal of the axle box to be identified is not greater than the normal vibration proportion threshold, and the proportion of normal sound time-domain signal of the axle box to be identified is not greater than the normal sound proportion threshold, it indicates that the axle box to be identified is abnormal from both the vibration and sound dimensions, and it can be determined that the axle box to be identified is in a faulty state. At this time, a prompt message can be output for the axle box, which is used to characterize the axle box fault and prompt the staff to carry out maintenance.

[0112] If the proportion of normal vibration time-domain signal of the axle box to be identified is greater than the normal vibration proportion threshold, and the proportion of normal sound time-domain signal of the axle box to be identified is greater than the normal sound proportion threshold, it indicates that there are no abnormalities in the axle box to be identified from both vibration and sound dimensions, and it can be determined that the axle box to be identified is in a non-faulty state. At this time, the operating status of the axle box to be identified can continue to be monitored.

[0113] By taking the above measures, when both the proportion of normal vibration time-domain signal and the proportion of normal sound time-domain signal are greater than the threshold, or neither are greater than the threshold, the axle box to be identified can be accurately determined to be in a non-faulty state or a faulty state based on the two dimensions.

[0114] In one embodiment, if the proportion of normal vibration time-domain signal of the axle box to be identified is not greater than the normal vibration proportion threshold, and the proportion of normal sound time-domain signal of the axle box to be identified is greater than the normal sound proportion threshold, it indicates that from the perspective of vibration, the axle box to be identified may have an abnormality, while from the perspective of sound, the axle box to be identified does not have an abnormality. In other words, the axle box to be identified may have a fault, but its operating status cannot be directly determined based on the currently obtained first comparison result and second comparison result.

[0115] In another embodiment, the operating status of the axle box to be identified can continue to be monitored, and all first comparison results and all second comparison results within a cutting cycle (one cut) can be obtained. Of course, real-time monitoring is also possible. After initially obtaining the first and second comparison results, at set acquisition cycles, the proportion of normal vibration time-domain signal can be compared with a normal vibration proportion threshold, and the proportion of normal sound time-domain signal can be compared to a normal sound proportion threshold, thereby obtaining multiple first comparison results and multiple second comparison results. This specification does not impose specific limitations. The following example uses all first comparison results and all second comparison results within one cut.

[0116] Based on all the first comparison results obtained within a single cut, it can be determined whether the duration for which the proportion of normal vibration time-domain signal is no greater than the normal vibration proportion threshold exceeds a first preset duration. Here, the first preset duration can be configured as needed. For example, the first preset duration can be 5 seconds.

[0117] On the one hand, if the value exceeds the limit, it indicates an abnormality in the axle box from a vibration perspective, confirming a fault and a faulty state. In this case, a prompt message for that axle box can be output. On the other hand, if the value does not exceed the limit, it indicates that an abnormality in the axle box cannot be accurately determined from a vibration perspective, confirming a non-faulty state. In this case, the operating status of the axle box can continue to be monitored.

[0118] Through the above measures, if an anomaly is determined to exist in the axle box to be identified based on a single vibration dimension, the operating status of the axle box to be identified can continue to be monitored in the vibration dimension. Therefore, if the duration for which the proportion of normal vibration time-domain signal does not exceed a normal vibration proportion threshold exceeds a first preset duration, it can be determined that the axle box to be identified is faulty from a vibration perspective. In one embodiment, if the proportion of normal vibration time-domain signal of the axle box to be identified is greater than the normal vibration proportion threshold, and the proportion of normal sound time-domain signal of the axle box to be identified is not greater than the normal sound proportion threshold, it indicates that the axle box to be identified may be abnormal from a sound perspective, but not from a vibration perspective. In other words, the axle box to be identified may be faulty, but its operating status cannot be directly determined based on the currently obtained first and second comparison results.

[0119] In another embodiment, the operating status of the axle box to be identified can continue to be monitored, and all first comparison results and all second comparison results within a cutting cycle (one cut) can be obtained. Of course, real-time monitoring is also possible. After initially obtaining the first and second comparison results, at set acquisition cycles, the proportion of normal vibration time-domain signal can be compared with a normal vibration proportion threshold, and the proportion of normal sound time-domain signal can be compared to a normal sound proportion threshold, thereby obtaining multiple first comparison results and multiple second comparison results. This specification does not impose specific limitations. The following example uses all first comparison results and all second comparison results within one cut.

[0120] Based on all the second comparison results obtained within a single cut, it can be determined whether the duration for which the proportion of normal sound time-domain signal is no greater than the normal sound proportion threshold exceeds a second preset duration. Here, the second preset duration can be configured as needed. For example, the second preset duration could be 10 seconds.

[0121] On one hand, if the sound level exceeds a certain threshold, it indicates an anomaly in the axle box to be identified, confirming a fault and a faulty state. In this case, a prompt message for that axle box can be output. On the other hand, if the sound level does not exceed a certain threshold, it indicates that an anomaly cannot be accurately determined from the sound level, confirming a non-faulty state. In this case, the operating status of the axle box to be identified can continue to be monitored.

[0122] Through the above measures, if it is determined that the axle box to be identified may be abnormal in a single sound dimension, the operating status of the axle box to be identified can continue to be monitored in the sound dimension. Thus, if the duration of the normal sound time domain signal not exceeding the normal sound proportion threshold exceeds the second preset duration, it can be determined that the axle box to be identified is faulty from the sound dimension.

[0123] In one embodiment, a first amplitude threshold can be determined for each axle box to be identified before performing step 120. Figure 6 A flowchart illustrating a method for determining a first amplitude threshold provided in an embodiment of this application is shown below. Figure 6 As shown, the method may include steps 610 to 630.

[0124] Step 610: Collect sample vibration data when the sample shaft box is in a non-faulty state. The sample vibration data includes multiple vibration time-domain signals.

[0125] When the sample shaft box is in a non-faulty state, multiple vibration time-domain signals of the sample shaft box can be collected by vibration acquisition equipment during multiple runs of the multi-wire cutting machine, forming sample vibration data. Here, the sample vibration data can include vibration time-domain signals from one or more cuts.

[0126] Step 620: Calculate the mean value of the amplitude of the time-domain signal of the sample vibration in the sample vibration data, and use it as the vibration reference value.

[0127] Step 630: Determine the first amplitude threshold of the axle box to be identified based on the vibration reference value.

[0128] After obtaining multiple sets of sample vibration data, the average amplitude of the vibration time-domain signals of multiple samples in the multiple sets of sample vibration data can be calculated and used as the vibration reference value. After obtaining the vibration reference value, a first amplitude threshold can be calculated based on the vibration reference value. For example, the vibration reference value can be added to a first set constant to obtain the first amplitude threshold. Here, the first set constant can be configured empirically to characterize the offset between the maximum amplitude of the vibration time-domain signal allowed by the axle box under non-fault conditions and the average amplitude.

[0129] In one embodiment, the vibration reference value can be multiplied by a first adjustment coefficient to obtain a first amplitude threshold. Here, the first adjustment coefficient can be configured as needed. For example, the first adjustment coefficient is a value between 1.2 and 1.5, such as 1.2, 1.3, 1.4, or 1.5.

[0130] In one embodiment, the first amplitude threshold can be calculated by using the vibration reference value as the base and the first adjustment coefficient as the exponent. Preferably, the first amplitude threshold is the product of the vibration reference value and the first adjustment coefficient.

[0131] Through the above measures, after calculating the mean amplitude of the sample vibration time-domain signal of the sample axle box under non-fault conditions, the mean value can be amplified to obtain the first amplitude threshold. The first amplitude threshold includes the vibration reference value and the offset between the maximum amplitude allowed by the axle box under non-fault conditions and the vibration reference value. Thus, normal vibration time-domain signals can be screened with the help of the first amplitude threshold in the future.

[0132] In one embodiment, if the vibration time-domain signal of any axle box to be identified fluctuates greatly, a corresponding first amplitude threshold can be configured for the axle box to be identified based on experience.

[0133] In one embodiment, before performing step 130, a corresponding second amplitude threshold can be determined for each axle box to be identified. See also Figure 7 The above is a flowchart illustrating a method for determining a second amplitude threshold according to an embodiment of this application. Figure 7As shown, the method may include steps 710 to 730.

[0134] Step 710: Collect sample sound data when the sample shaft box is in a non-faulty state, wherein the sample sound data includes multiple sound time-domain signals.

[0135] When the sample spindle box is in a non-faulty state, during multiple runs of the multi-wire cutting machine, the sound time-domain signal of the sample spindle box can be collected by the sound acquisition equipment to form sample sound data. Here, the sample sound data can include the sound time-domain signal within one or more cuts.

[0136] Step 720: Calculate the mean amplitude of the time-domain signal of the sample sound in the sample sound data, and use it as the sound reference value.

[0137] Step 730: Determine the second amplitude threshold of the axle box to be identified based on the sound reference value.

[0138] After obtaining multiple sets of sample sound data, the average amplitude of the time-domain signals of multiple samples in the sample sound data can be calculated and used as the sound reference value. After obtaining the sound reference value, a second amplitude threshold can be calculated based on the sound reference value. For example, a second set constant can be added to the sound reference value to obtain the second amplitude threshold. Here, the second set constant can be configured empirically, representing the offset between the maximum amplitude of the sound time-domain signal allowed by the axle box under non-fault conditions and the average amplitude.

[0139] In one embodiment, the sound reference value can be multiplied by a second adjustment coefficient to obtain a second amplitude threshold. Here, the second adjustment coefficient can be configured as needed. For example, the second adjustment coefficient is a value between 1.2 and 1.5, such as 1.2, 1.3, 1.4, or 1.5.

[0140] In one embodiment, the second amplitude threshold can be calculated by using the sound reference value as the base and the second adjustment coefficient as the exponent. Preferably, the second amplitude threshold is the product of the sound reference value and the second adjustment coefficient.

[0141] By taking the above measures, after calculating the mean amplitude of the sample sound time-domain signal of the sample axle box under non-fault conditions, the mean value can be amplified to obtain a second amplitude threshold. The second amplitude threshold includes the sound reference value and the offset between the maximum amplitude allowed by the axle box under non-fault conditions and the sound reference value. The normal sound time-domain signal can then be filtered using the second amplitude threshold.

[0142] In one embodiment, if the sound time-domain signal of any axle box to be identified fluctuates greatly, a corresponding second amplitude threshold can be configured for the axle box to be identified based on experience.

[0143] In one embodiment, a normal vibration percentage threshold may be generated before performing step 510. See also Figure 8 This is a flowchart illustrating a method for determining the normal vibration percentage threshold according to an embodiment of this application. Figure 8 As shown, the method may include steps 810 to 830.

[0144] Step 810: When the sample shaft box is in a faulty state, collect N sets of fault vibration data; where N is an integer greater than 1.

[0145] When the sample shaft box is in a faulty state, during N runs of the multi-wire cutting machine, the vibration time-domain signal of the sample shaft box can be collected by vibration acquisition equipment to form fault vibration data, thereby obtaining N sets of fault vibration data. Here, each time the multi-wire cutting machine performs a cut, one set of fault vibration data can be collected.

[0146] Step 820: Obtain the percentage of N normal vibration time-domain signals from N sets of fault vibration data.

[0147] Step 830: Determine the threshold for the proportion of normal vibration based on the proportion of N normal vibration time-domain signals.

[0148] For each set of fault vibration data, the amplitude of each vibration time-domain signal in the fault vibration data can be compared with the first amplitude threshold to filter out normal vibration time-domain signals. The proportion of normal vibration time-domain signals can be obtained by dividing the number of normal vibration time-domain signals by the total number of vibration time-domain signals in the set of fault vibration data.

[0149] By calculation, the proportion of N normal vibration time-domain signals corresponding to N sets of fault vibration data can be obtained. After obtaining the proportion of N normal vibration time-domain signals, the mean of the proportion of N normal vibration time-domain signals can be calculated and used as the threshold of the normal vibration proportion of the axle box to be identified.

[0150] Through the above measures, since the N sets of fault vibration data generated by the sample axle box under fault conditions can characterize the vibration situation under fault conditions, after obtaining the N sets of fault vibration data, the proportion of normal vibration time domain signal under fault conditions can be determined by converting the proportion of normal vibration time domain signal corresponding to each set of fault vibration data. Using the average of the proportions of N normal vibration time domain signals as the normal vibration proportion threshold can reduce errors and select a more reliable normal vibration proportion threshold.

[0151] In one embodiment, a normal sound percentage threshold may be generated before performing step 520. See also Figure 9 This is a flowchart illustrating a method for determining the normal sound percentage threshold according to an embodiment of this application. Figure 9 As shown, the method may include steps 910 to 930.

[0152] Step 910: When the sample shaft box is in a faulty state, collect M sets of fault sound data; where M is an integer greater than 1.

[0153] When the sample shaft box is in a faulty state, during N runs of the multi-wire cutting machine, the sound time-domain signal of the sample shaft box can be collected by the sound acquisition equipment to form fault sound data, thereby obtaining M sets of fault sound data. Here, each time the multi-wire cutting machine performs a cut, one set of fault sound data can be collected.

[0154] Step 920: Obtain the percentage of normal sound time-domain signals in the M groups of fault sound data.

[0155] Step 930: Determine the threshold for the proportion of normal sounds based on the proportion of the M normal sound time-domain signals.

[0156] For each set of fault sound data, the amplitude of each sound time-domain signal and the second amplitude threshold can be compared to filter out normal sound time-domain signals. The proportion of normal sound time-domain signals can be obtained by dividing the number of normal sound time-domain signals by the total number of sound time-domain signals in the set of fault sound data.

[0157] By calculation, the proportion of M normal sound time-domain signals corresponding to M sets of fault sound data can be obtained. After obtaining the proportion of M normal sound time-domain signals, the mean of the proportion of M normal sound time-domain signals can be calculated and used as the threshold of the normal sound proportion of the axle box to be identified.

[0158] Through the above measures, since the M sets of fault sound data generated by the sample axle box under fault conditions can characterize the sound generation under fault conditions, after obtaining the M sets of fault sound data, the proportion of normal sound time domain signal under fault conditions can be determined by converting the proportion of normal sound time domain signal corresponding to each set of fault sound data; using the average of the proportions of the M normal sound time domain signals as the normal sound proportion threshold can reduce errors and select a more reliable normal sound proportion threshold.

[0159] Figure 10 This is a block diagram of a shaft box fault identification device according to an embodiment of the present invention, as shown below. Figure 10 As shown, the device may include:

[0160] The acquisition module 1010 is used to acquire vibration data and sound data generated by the shaft box to be identified; wherein, the shaft box to be identified is the main roller shaft box in a multi-wire cutting machine;

[0161] The first determining module 1020 is used to determine the proportion of normal vibration time-domain signals based on the amplitude of each vibration time-domain signal in the vibration data and a first amplitude threshold; wherein, the first amplitude threshold is obtained based on the vibration time-domain signals generated by the sample shaft box under non-fault conditions;

[0162] The second determining module 1030 is used to determine the proportion of normal sound time-domain signals based on the amplitude of each sound time-domain signal in the sound data and a second amplitude threshold; wherein, the second amplitude threshold is obtained based on the sound time-domain signals generated by the sample shaft box under non-fault conditions;

[0163] The identification module 1040 is used to determine whether the axle box to be identified is in a fault state based on the proportion of normal vibration time domain signal and the proportion of normal sound time domain signal.

[0164] In one embodiment, the identification module 1040 is further configured to:

[0165] The proportion of the normal vibration time-domain signal is compared with the normal vibration proportion threshold to obtain a first comparison result; wherein, the normal vibration proportion threshold is obtained based on the vibration time-domain signal generated by the sample axle box under fault conditions;

[0166] The proportion of the normal sound time-domain signal is compared with the normal sound proportion threshold to obtain a second comparison result; wherein, the normal sound proportion threshold is obtained based on the sound time-domain signal generated by the sample shaft box under fault conditions;

[0167] Based on the first comparison result and the second comparison result, it is determined whether the axle box to be identified is in a faulty state.

[0168] In one embodiment, the identification module 1040 is further configured to:

[0169] If the proportion of the normal vibration time domain signal of the axle box to be identified is not greater than the normal vibration proportion threshold, and the proportion of the normal sound time domain signal of the axle box to be identified is not greater than the normal sound proportion threshold, then the axle box to be identified is determined to be in a fault state.

[0170] In one embodiment, the identification module 1040 is further configured to:

[0171] If the proportion of the normal vibration time domain signal is not greater than the normal vibration proportion threshold, and the proportion of the normal sound time domain signal is greater than the normal sound proportion threshold, then it is determined whether the duration of the normal vibration time domain signal proportion not being greater than the normal vibration proportion threshold exceeds the first preset duration.

[0172] If the value exceeds the limit, the axle box to be identified is determined to be in a faulty state.

[0173] In one embodiment, the identification module 1040 is further configured to:

[0174] If the proportion of the normal vibration time domain signal is greater than the normal vibration proportion threshold, and the proportion of the normal sound time domain signal is not greater than the normal sound proportion threshold, then it is determined whether the duration of the normal sound time domain signal proportion not being greater than the normal sound proportion threshold exceeds the second preset duration.

[0175] If the value exceeds the limit, the axle box to be identified is determined to be in a faulty state.

[0176] In one embodiment, the device further includes:

[0177] The threshold acquisition module is used to acquire sample vibration data of the sample shaft box in a non-faulty state, wherein the sample vibration data includes multiple sample vibration time-domain signals; calculate the mean value of the amplitude of the sample vibration time-domain signals in the sample vibration data as a vibration reference value; and determine the first amplitude threshold based on the vibration reference value.

[0178] In one embodiment, the threshold acquisition module is used to acquire sample sound data of the sample shaft box in a non-faulty state, wherein the sample sound data includes multiple sample sound time-domain signals; calculate the mean value of the amplitude of the sample sound time-domain signals in the sample sound data as a sound reference value; and determine the second amplitude threshold based on the sound reference value.

[0179] In one embodiment, the threshold acquisition module is used to acquire N sets of fault vibration data when the sample axle box is in a fault state; wherein N is an integer greater than 1; acquire the proportion of N normal vibration time-domain signals of the N sets of fault vibration data; and determine the normal vibration proportion threshold based on the proportion of the N normal vibration time-domain signals.

[0180] In one embodiment, the threshold acquisition module is used to acquire M sets of fault sound data when the sample axle box is in a fault state; wherein M is an integer greater than 1; acquire the proportion of M normal sound time-domain signals of the M sets of fault sound data; and determine the normal sound proportion threshold based on the proportion of the M normal sound time-domain signals.

[0181] The specific implementation process of the functions and roles of each module in the above-mentioned device can be found in the implementation process of the corresponding steps in the above-mentioned axle box fault identification method, and will not be repeated here.

[0182] Figure 11 This is a schematic diagram illustrating an application scenario of the axle box fault identification method provided in this application embodiment. For example... Figure 11As shown, the application scenario includes client 20, client 30, and server 40. Client 20 can be a vibration acquisition device installed on the axle box to be identified, used to collect vibration data of the axle box. Client 30 can be a sound acquisition device installed on the axle box to be identified, used to collect sound data of the axle box. Server 40 can be a host, server, server cluster, or cloud computing center, which can obtain the vibration data collected by client 20, obtain the sound data collected by client 30, and identify whether the axle box has malfunctioned based on the vibration data and sound data.

[0183] like Figure 12 As shown, this embodiment provides an electronic device 1, including: at least one processor 11 and a memory 12. Figure 12 Taking a processor 11 as an example, the processor 11 and the memory 12 are connected via a bus 10. The memory 12 stores instructions that can be executed by the processor 11. The instructions are executed by the processor 11 to enable the electronic device 1 to perform all or part of the process of the method in the embodiments described below. In one embodiment, the electronic device 1 may be the aforementioned server 40, used to execute the axle box fault identification method.

[0184] The memory 12 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0185] This application also provides a computer-readable storage medium storing a computer program that can be executed by a processor 11 to perform the axle box fault identification method provided in this application.

[0186] The apparatuses and methods disclosed in the several embodiments provided in this application can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatuses, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0187] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0188] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

Claims

1. A shaft box fault identification method characterized by, include: The vibration and sound data generated by the shaft box to be identified within the same time period are acquired; wherein, the shaft box to be identified is the main roller shaft box in a multi-wire cutting machine. The proportion of normal vibration time-domain signals is determined based on the amplitude of each vibration time-domain signal in the vibration data and a first amplitude threshold; wherein, the first amplitude threshold is obtained based on the vibration time-domain signal generated by the sample axle box under non-fault conditions; The proportion of normal sound time-domain signals is determined based on the amplitude of each sound time-domain signal in the sound data and a second amplitude threshold; wherein, the second amplitude threshold is obtained based on the sound time-domain signal generated by the sample shaft box under non-fault conditions; Based on the proportion of normal vibration time-domain signal and the proportion of normal sound time-domain signal, determine whether the axle box to be identified is in a fault state; The step of determining whether the axle box to be identified is in a fault state based on the proportion of normal vibration time-domain signal and the proportion of normal sound time-domain signal includes: The proportion of the normal vibration time-domain signal is compared with a normal vibration proportion threshold to obtain a first comparison result; wherein, the normal vibration proportion threshold is obtained based on the vibration time-domain signal generated by the sample axle box under fault conditions; the proportion of the normal sound time-domain signal is compared with a normal sound proportion threshold to obtain a second comparison result; wherein, the normal sound proportion threshold is obtained based on the sound time-domain signal generated by the sample axle box under fault conditions; based on the first comparison result and the second comparison result, it is determined whether the axle box to be identified is in a fault state; If the proportion of the normal vibration time domain signal is not greater than the normal vibration proportion threshold, and the proportion of the normal sound time domain signal is greater than the normal sound proportion threshold, then it is determined whether the duration of the normal vibration time domain signal proportion not being greater than the normal vibration proportion threshold exceeds a first preset duration; if it exceeds, it is determined that the axle box to be identified is in a fault state. If the proportion of the normal vibration time domain signal is greater than the normal vibration proportion threshold, and the proportion of the normal sound time domain signal is not greater than the normal sound proportion threshold, then it is determined whether the duration of the normal sound time domain signal proportion not being greater than the normal sound proportion threshold exceeds the second preset duration; if it does, it is determined that the axle box to be identified is in a fault state.

2. The method according to claim 1, characterized in that, The step of determining whether the axle box to be identified is in a fault state based on the first comparison result and the second comparison result includes: If the proportion of the normal vibration time domain signal of the axle box to be identified is not greater than the normal vibration proportion threshold, and the proportion of the normal sound time domain signal of the axle box to be identified is not greater than the normal sound proportion threshold, then the axle box to be identified is determined to be in a fault state.

3. The method according to claim 1 or 2, characterized in that, The step of obtaining the first amplitude threshold includes: Acquire sample vibration data when the sample shaft box is in a non-faulty state, wherein the sample vibration data includes multiple sample vibration time-domain signals; Calculate the mean amplitude of the time-domain signal of the sample vibration in the sample vibration data, and use it as the vibration reference value; The first amplitude threshold is determined based on the vibration reference value.

4. The method according to claim 1 or 2, characterized in that, The steps for obtaining the second amplitude threshold include: Acquire sample sound data when the sample shaft box is in a non-faulty state, wherein the sample sound data includes multiple sample sound time-domain signals; Calculate the mean amplitude of the time-domain signal of the sample sound in the sample sound data, and use it as the sound reference value; The second amplitude threshold is determined based on the sound reference value.

5. The method according to claim 1 or 2, characterized in that, The steps for obtaining the normal vibration percentage threshold include: Obtain N sets of fault vibration data for the sample axle box under fault conditions; where N is an integer greater than 1. Obtain the proportion of N normal vibration time-domain signals from the N sets of fault vibration data; The threshold for the proportion of normal vibrations is determined based on the proportion of the N normal vibration time-domain signals.

6. The method according to claim 1 or 2, characterized in that, The steps for obtaining the normal sound percentage threshold include: Obtain M sets of fault sound data when the sample axle box is in a fault state; where M is an integer greater than 1; Obtain the percentage of the M normal sound time-domain signals from the M sets of fault sound data; The normal sound proportion threshold is determined based on the proportion of the M normal sound time-domain signals.

7. A fault identification device for axle boxes, characterized in that, include: The acquisition module is used to acquire vibration and sound data generated by the shaft box to be identified; wherein, the shaft box to be identified is the main roller shaft box in a multi-wire cutting machine; The first determining module is used to determine the proportion of normal vibration time-domain signals based on the amplitude of each vibration time-domain signal in the vibration data and a first amplitude threshold; wherein, the first amplitude threshold is obtained based on the vibration time-domain signals generated by the sample shaft box under non-fault conditions; The second determining module is used to determine the proportion of normal sound time-domain signals based on the amplitude of each sound time-domain signal in the sound data and a second amplitude threshold; wherein, the second amplitude threshold is obtained based on the sound time-domain signals generated by the sample shaft box under non-fault conditions; The identification module is used to determine whether the axle box to be identified is in a fault state based on the proportion of normal vibration time-domain signal and the proportion of normal sound time-domain signal. The identification module is specifically used to compare the proportion of the normal vibration time-domain signal with a normal vibration proportion threshold to obtain a first comparison result; wherein the normal vibration proportion threshold is obtained based on the vibration time-domain signal generated by the sample axle box under fault conditions; to compare the proportion of the normal sound time-domain signal with a normal sound proportion threshold to obtain a second comparison result; wherein the normal sound proportion threshold is obtained based on the sound time-domain signal generated by the sample axle box under fault conditions; and to determine whether the axle box to be identified is in a fault state based on the first comparison result and the second comparison result. If the proportion of the normal vibration time domain signal is not greater than the normal vibration proportion threshold, and the proportion of the normal sound time domain signal is greater than the normal sound proportion threshold, then it is determined whether the duration of the normal vibration time domain signal proportion not being greater than the normal vibration proportion threshold exceeds a first preset duration; if it exceeds, it is determined that the axle box to be identified is in a fault state. If the proportion of the normal vibration time domain signal is greater than the normal vibration proportion threshold, and the proportion of the normal sound time domain signal is not greater than the normal sound proportion threshold, then it is determined whether the duration of the normal sound time domain signal proportion not being greater than the normal sound proportion threshold exceeds the second preset duration; if it does, it is determined that the axle box to be identified is in a fault state.

8. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store processor-executable instructions; The processor is configured to execute the axle box fault identification method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that can be executed by a processor to perform the axle box fault identification method according to any one of claims 1-6.

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