Gear breakage identification method, device, equipment and medium

Abstract

The present application discloses a gear breakage identification method, device, equipment and medium, which relates to the field of fault diagnosis technology, including: performing single-sample fault diagnosis on the impact single-sample data of each gear measuring point of the current gear to obtain single-sample fault diagnosis results; performing trend analysis on gear impact and meshing spectrum impact based on each impact single-sample data to obtain gear impact trend results and meshing spectrum impact trend results, respectively, and obtaining gear impact continuity statistics for each impact single-sample data; determining whether the current gear has a gear breakage fault and whether a gear breakage warning is required based on the current health level of the current gear, single-sample fault diagnosis results, gear impact trend results, meshing spectrum impact trend results and gear impact continuity statistics. Extract multi-angle features through single-sample analysis and trend analysis. Combine health level and multi-angle features to perform comprehensive gear breakage fault identification to avoid misjudgment of a single indicator.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis, and in particular to a gear breakage identification method, device, equipment and medium. Background Art

[0002] Gears often withstand high torque and radial loads during operation and are susceptible to a variety of factors, including manufacturing errors, improper assembly, poor lubrication, overloading, and poor operation and maintenance. This leads to a relatively high failure rate. Gear failure not only impacts the normal operation of equipment and reduces production efficiency, but can also cause major safety incidents and result in severe economic losses.

[0003] In the early stages of gear failure, the fault characteristics are often not obvious, and the signal changes are relatively weak, making it difficult for conventional diagnostic methods to detect them in a timely manner. For example, in the early stages of tooth fatigue crack development, the crack may just be a microscopic crack in a very small local area of ​​the tooth surface. At this time, the impact on the overall vibration and temperature of the gear is very small.

[0004] At present, the fault detection of gears utilizes machine learning and artificial intelligence algorithms, such as artificial neural networks, support vector machines, decision trees, etc., to study a large amount of gear fault sample data, explore hidden patterns and rules, and realize automatic identification of fault types. In order to achieve early fault diagnosis, advanced signal processing algorithms, such as wavelet analysis, are often used to reduce noise and extract weak features from the collected signals. By continuously optimizing the analysis parameters, the early fault signal characteristics are amplified and their identifiability is enhanced. However. In actual complex working conditions, the complex working environment and operating conditions will introduce a large number of interference signals. Therefore, the gear signal characteristics are difficult to extract accurately, and in the diagnosis process, a single diagnostic method can often only reflect the gear fault condition from a specific angle. When facing complex gear system failures.

[0005] In summary, how to achieve real-time and comprehensive collection and analysis of various data information of gears during operation, accurately capture abnormal impact signals, and then achieve early warning and accurate diagnosis of gear failures is a technical problem to be solved in this field. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a gear fracture identification method, device, equipment, and medium that can comprehensively collect and analyze various data information of gears during operation in real time, accurately capture abnormal impact signals, and thus achieve early warning and accurate diagnosis of gear failures. The specific scheme is as follows:

[0007] In a first aspect, the present application discloses a gear breakage identification method, comprising:

[0008] Perform single-sample fault diagnosis on the impact single-sample data of each gear measuring point of the current gear to obtain a single-sample fault diagnosis result;

[0009] Performing trend analysis on the gear impact and meshing spectrum impact based on each of the impact single sample data to obtain a gear impact trend result and a meshing spectrum impact trend result, respectively, and obtaining a gear impact continuity statistic for each of the impact single sample data;

[0010] Determining whether the current gear has a gear breakage fault based on the current health level of the current gear, the single sample fault diagnosis result, the gear impact trend result, the meshing spectrum impact trend result, and the gear impact continuity statistic to obtain a corresponding gear breakage identification result;

[0011] If the gear breakage identification result is that there is no gear breakage fault, a gear breakage warning judgment process is performed to obtain a corresponding gear breakage warning result.

[0012] Optionally, performing single-sample fault diagnosis on the impact single-sample data of each gear measuring point of the current gear to obtain a single-sample fault diagnosis result includes:

[0013] Performing spectrum analysis on the impact single sample data of each gear measuring point of the current gear, obtaining a preset number of target frequency components after spectrum analysis in descending order of amplitude, and determining the maximum amplitude, so as to set an amplitude threshold based on the maximum amplitude;

[0014] The number of frequency components in the target frequency components that are greater than the amplitude threshold is counted, and a single-sample fault diagnosis result is determined based on the number of frequency components and a fault judgment condition.

[0015] Optionally, determining a single-sample fault diagnosis result based on the number of frequency components and a fault determination condition includes:

[0016] Determining whether the number of frequency components meets a fault determination condition established based on a preset fault feature number threshold;

[0017] If the number of the frequency components is greater than or equal to the preset fault feature number threshold, it is determined that the impact single sample data has a single tooth broken tooth diagnosis feature or a tooth root crack feature, so as to determine the corresponding single sample fault diagnosis result;

[0018] If the number of the frequency components is less than the preset fault feature number threshold, it is determined that the impact single sample data has no fault diagnosis feature to determine the corresponding single sample fault diagnosis result.

[0019] Optionally, the trend analysis of gear impact and meshing spectrum impact is performed based on each of the impact single sample data to obtain a gear impact trend result and a meshing spectrum impact trend result, respectively, including:

[0020] Calculating the strength index of the gear impact signal strength within a first single time period and the strength index of the meshing spectrum impact signal strength within a second single time period of each of the impact single sample data, respectively, to obtain first time series trend data of the gear impact signal strength and second time series trend data of the meshing spectrum impact signal strength;

[0021] By using a multi-time-scale dynamic smoothing method, and based on the first time series trend data and the second time series trend data, a trend evolution analysis is performed on the impact trend data within a preset time period to obtain gear impact trend results and meshing spectrum impact trend results, respectively.

[0022] Optionally, the multi-time-scale dynamic smoothing method is used to perform trend evolution analysis on the impact trend data within a preset time period based on the first time series trend data and the second time series trend data to obtain gear impact trend results and meshing spectrum impact trend results, respectively, including:

[0023] Performing sliding average processing on the first time series trend data within the preset time period using a first preset time window to obtain a first short-term moving average;

[0024] Performing sliding average processing on the first time series trend data within the preset time period using a second preset time window to obtain a first long-term moving average;

[0025] Performing sliding average processing on the second time series trend data within the preset time period using the first preset time window to obtain a second short-term moving average;

[0026] Performing sliding average processing on the second time series trend data within the preset time period using a second preset time window to obtain a second long-term moving average;

[0027] extracting the corresponding data mean points on the first short-term moving average and the first long-term moving average in the same single time period, respectively, and performing difference processing to obtain a first short-term variation;

[0028] respectively extracting corresponding data mean points on the second short-term moving average and the second long-term moving average in the same single time period, and performing difference processing to obtain a second short-term variation;

[0029] performing a difference processing on two data mean points located in adjacent single time periods on the first long-term moving average to obtain a first long-term variation;

[0030] performing a difference processing on two data mean points located in adjacent single time periods on the second long-term moving average to obtain a second long-term variation;

[0031] If the first short-term variation satisfies a short-term variation threshold condition, and the first long-term variation satisfies a long-term variation threshold condition, determining that the gear impact trend result is an upward trend result;

[0032] If the second short-time variation satisfies a short-time variation threshold condition, and the second long-time variation satisfies a long-time variation threshold condition, it is determined that the meshing spectrum impact trend result is a trend rising result.

[0033] Optionally, the multi-time-scale dynamic smoothing method is used to perform trend evolution analysis on the impact single sample data within a preset time period based on the first time series trend data and the second time series trend data to obtain gear impact trend results and meshing spectrum impact trend results, respectively, including:

[0034] Performing sliding average processing on the first time series trend data within the preset time period using a first preset time window to obtain a first short-term moving average;

[0035] Performing sliding average processing on the first time series trend data within the preset time period using a second preset time window to obtain a first long-term moving average;

[0036] Performing sliding average processing on the second time series trend data within the preset time period using the first preset time window to obtain a second short-term moving average;

[0037] Performing sliding average processing on the second time series trend data within the preset time period using a second preset time window to obtain a second long-term moving average;

[0038] Performing a trend evolution analysis on the gear impact signal strength of the impact trend data based on the relative position and slope of the first short-term moving average and the first long-term moving average to obtain a gear impact trend result;

[0039] Based on the relative position and slope of the second short-term moving average and the second long-term moving average, a trend evolution analysis is performed on the meshing spectrum impact signal intensity of the impact trend data to obtain a meshing spectrum impact trend result.

[0040] Optionally, the obtaining of gear impact continuity statistics of each impact single sample data includes:

[0041] Based on the first time series trend data of each impact single sample data and the actual operating mileage information of the equipment where the current gear is located, the gear impact continuity statistics of the impact trend data within every 100 kilometers are calculated.

[0042] Optionally, the calculating of the gear impact continuity statistics of the impact trend data within every 100 kilometers based on the first time series trend data of each impact single sample data and the actual operating mileage information of the equipment where the current gear is located includes:

[0043] Counting the target frequency of the intensity index in the first single time period of the first time series trend data of each of the impact single sample data being greater than the preset intensity index threshold value in the single time period;

[0044] Calculate the gear impact continuity statistics of the impact trend data within every 100 kilometers according to the target frequency and the actual operating mileage of the equipment where the current gear is located using a preset gear impact continuity statistics equation;

[0045] The preset gear impact continuity statistics equation is:

[0046] ;

[0047] in, represents the target frequency, Indicates the actual operating mileage, Represents the gear impact continuity statistic.

[0048] Optionally, determining whether the current gear has a gear breakage fault according to the current health level of the current gear, the single sample fault diagnosis result, the gear impact trend result, the meshing spectrum impact trend result, and the gear impact continuity statistic includes:

[0049] If the current health level of the current gear is at any level among the unhealthy levels, the feature ratio of the number of single-tooth broken tooth diagnostic features or tooth root crack diagnostic features in the single-sample fault diagnosis result to the total number of results of the single-sample fault diagnosis result is greater than a first preset fault feature ratio, and the gear impact trend result is an upward trend result, then it is determined that the current gear has a gear breakage fault, and a gear breakage fault alarm is issued;

[0050] If the current health level of the current gear is at any level among the unhealthy levels, the feature ratio of the number of single-tooth broken tooth diagnostic features or tooth root crack diagnostic features in the single-sample fault diagnosis result to the total number of results of the single-sample fault diagnosis result is greater than a first preset fault feature ratio, and the meshing spectrum impact trend result is a downward trend result and / or the gear impact continuity statistic is greater than a preset statistic threshold, then it is determined that the current gear has a gear breakage fault, and a gear breakage fault alarm is issued;

[0051] Optionally, if the current health level of the current gear is higher than any level of the unhealthy levels, the method further includes:

[0052] According to the health assessment of key train components and the intelligent operation and maintenance model, the current health level of the current gear is output, and it is determined whether the current health level is at any unhealthy level to obtain a corresponding level judgment result; wherein, the unhealthy level includes a preset sub-health level, a preset minor fault level, a preset medium fault level, and a preset severe fault level.

[0053] Optionally, the gear breakage warning judgment process to obtain a corresponding gear breakage warning result includes:

[0054] If an external alarm message is received and the number of single-tooth broken tooth diagnostic features or tooth root crack diagnostic features in the single-sample fault diagnosis result accounts for a feature ratio greater than a second preset fault feature ratio in the total number of results of the single-sample fault diagnosis result, a gear breakage warning is issued; wherein the second preset fault feature ratio is less than the first preset fault feature ratio;

[0055] Alternatively, if no external alarm information is received, and if it is detected that the meshing spectrum impact trend result is an upward trend result and the gear impact continuity statistic is greater than a preset statistic threshold, a gear breakage warning is issued.

[0056] In a second aspect, the present application discloses a gear breakage identification device, comprising:

[0057] The single sample diagnosis module is used to perform single sample fault diagnosis on the impact single sample data of each gear measuring point of the current gear to obtain a single sample fault diagnosis result;

[0058] a trend analysis module for performing trend analysis of gear impact and meshing spectrum impact based on each of the impact single sample data, so as to obtain a gear impact trend result and a meshing spectrum impact trend result, respectively, and to obtain a gear impact continuity statistic of each of the impact single sample data;

[0059] a fault identification module, configured to determine whether the current gear has a gear breakage fault based on the current health level of the current gear, the single sample fault diagnosis result, the gear impact trend result, the meshing spectrum impact trend result, and the gear impact continuity statistic, so as to obtain a corresponding gear breakage identification result;

[0060] The fault warning module is used to perform a gear breakage warning judgment process to obtain a corresponding gear breakage warning result if the gear breakage identification result is that there is no gear breakage fault.

[0061] In a third aspect, the present application discloses an electronic device, comprising:

[0062] Memory, used to store computer programs;

[0063] The processor is used to execute the computer program to implement the steps of the gear breakage identification method disclosed above.

[0064] In a fourth aspect, the present application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the gear breakage identification method disclosed above are implemented.

[0065] It can be seen that the present application discloses a gear breakage identification method, including: performing single-sample fault diagnosis on the impact single-sample data of each gear measuring point of the current gear to obtain a single-sample fault diagnosis result; performing trend analysis of gear impact and meshing spectrum impact based on each of the impact single-sample data to obtain a gear impact trend result and a meshing spectrum impact trend result, and obtaining the gear impact continuity statistics of each of the impact single-sample data; determining whether the current gear has a gear breakage fault based on the current health level of the current gear, the single-sample fault diagnosis result, the gear impact trend result, the meshing spectrum impact trend result, and the gear impact continuity statistics to obtain a corresponding gear breakage identification result; if the gear breakage identification result is that there is no gear breakage fault, then performing a gear breakage warning judgment process to obtain a corresponding gear breakage warning result. It can be seen that multi-angle features are extracted through single-sample analysis and trend analysis (gear impact, meshing spectrum trend). Then, comprehensive gear breakage fault identification is performed in combination with health level and multi-angle characteristics to avoid misjudgment of a single indicator. In addition, the false alarm rate is reduced through triple verification of single sample characteristics, trends, and statistics. In addition, early warning of gear breakage faults (including early gear crack faults) is achieved through single sample analysis and trend analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0067] Figure 1 This is a flow chart of a gear breakage identification method disclosed in this application;

[0068] Figure 2 This is a flow chart of a gear fracture trend analysis method disclosed in this application;

[0069] Figure 3 This is a flow chart of a specific gear breakage identification method disclosed in this application;

[0070] Figure 4 This is a schematic structural diagram of a gear breakage identification device disclosed in this application;

[0071] Figure 5 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0072] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0073] Gears, as key transmission components in numerous mechanical devices, are widely used in transportation, industrial machinery, aerospace, medical devices, and other fields. For example, they play an indispensable role in automotive transmissions, machine tool spindle drives, and aircraft engine accessory drives. However, because gears often withstand high torque and radial loads during operation and are susceptible to various factors such as manufacturing errors, improper assembly, poor lubrication, overloading, and poor operation and maintenance, they have a relatively high failure rate.

[0074] Once a gear fails, it will not only affect the normal operation of the equipment and reduce production efficiency, but may also cause major safety accidents and bring serious economic losses. Gear failures manifest in various ways, and different types of failures may have similarities in certain characteristic manifestations. This makes it difficult to accurately distinguish which specific type of failure belongs to by relying on a single diagnostic method or partial diagnostic means. When a gear failure is in the early stages, since the fault characteristics are often not obvious and the signal changes are relatively weak, it is difficult to be captured in time by conventional diagnostic methods. For example, in the early stage of tooth surface fatigue crack development, the cracks have just begun to germinate, and there may only be microscopic cracks in a very small local area of ​​the tooth surface. At this time, the impact on the overall vibration, temperature, etc. of the gear is very small.

[0075] Currently, machine learning and artificial intelligence algorithms, such as artificial neural networks, support vector machines, and decision trees, are being used to analyze large amounts of gear fault sample data, uncovering hidden patterns and regularities, and automatically identifying fault types. To achieve early fault diagnosis, advanced signal processing algorithms, such as wavelet analysis, are often employed to reduce noise and extract weak features from the collected signals. By continuously optimizing analysis parameters, early fault signal characteristics are amplified, enhancing their identifiability. However, under normal conditions, gear impact signals exhibit specific patterns and characteristics. However, once a fault occurs, such as tooth wear or cracks, the impact signals change accordingly. In real-world operating conditions, complex working environments and operating conditions introduce a significant amount of interfering signals. For example, in large-scale industrial production sites, numerous machines operate simultaneously, generating vibrations and noise of varying frequencies. For signal acquisition during gear fault diagnosis, this is like trying to discern a specific "melody" amidst a cacophony of background noise. Take a multi-stage gear reducer as an example. The gears interact with each other, and there is also vibration interference from surrounding components such as motors and couplings. When a gear fails, it is very difficult to separate the key characteristic signal that can accurately reflect the gear failure from these numerous interference signals. Traditional technical methods have the following shortcomings:

[0076] Strong data dependence: A large amount of representative fault sample data is required for training. Otherwise, the model's generalization ability is poor, and it is difficult to accurately identify fault types that have not appeared in the training data or faults under different working conditions.

[0077] Poor model interpretability: The internal decision-making process and feature extraction mechanism of some complex intelligent algorithms, such as deep neural networks, are difficult to intuitively understand, which is not conducive to trust in diagnostic results and further fault analysis.

[0078] Poor adaptability to working conditions: In practical applications, gears often need to operate under a variety of working conditions, but the adaptability of existing diagnostic methods under different working conditions is obviously limited.

[0079] Limited comprehensive diagnostic capabilities: A single diagnostic method can often only reflect the gear failure from a specific perspective. When facing complex gear system failures, it is necessary to combine multiple diagnostic methods for collaborative diagnosis, but there are currently many limitations in this regard.

[0080] To this end, the present invention provides a gear breakage identification solution that can realize real-time and comprehensive collection and analysis of various data information of the gear during operation, accurately capture abnormal impact signals, and thus achieve early warning and accurate diagnosis of gear failures.

[0081] Reference Figure 1 As shown, an embodiment of the present invention discloses a gear breakage identification method, comprising:

[0082] Step S11: performing single-sample fault diagnosis on the impact single-sample data of each gear measuring point of the current gear to obtain a single-sample fault diagnosis result.

[0083] In this embodiment, before performing single-sample fault diagnosis on the impact single-sample data from each gear measuring point of the current gear, the method further includes collecting impact signals from each gear measuring point of the current gear during each preset time period to obtain impact signals as single-sample impact data. It is understood that sensors are first used to collect impact signals from each gear measuring point during operation of the current gear. A gear measuring point is a sensor location installed on a gear or transmission device, used to monitor vibration, noise, and other signals at a specific location in real time. Different gear measuring points focus on the status of different areas of the gear (e.g., the tooth root, tooth addendum, and meshing point) to accurately locate the fault. By fusing impact data from multiple gear measuring points, the limitations of a single position signal are mitigated. It is important to note that the preset time period is 24 hours. That is, impact signals are collected daily for a preset duration as single-sample impact data. The impact signals contain impact components. The preset duration can be 10 seconds, or other specific durations, and is not specifically limited to this.

[0084] In this embodiment, spectral analysis is performed on the impact single-sample data of each gear measurement point of the current gear. A preset number of target frequency components after spectral analysis are obtained in descending order of amplitude, and the maximum amplitude is determined. An amplitude threshold is then set based on the maximum amplitude. The number of frequency components within the target frequency components that are greater than the amplitude threshold is counted, and a single-sample fault diagnosis result is determined based on the number of frequency components and the fault determination criteria. It will be understood that spectral analysis is performed on the acquired single-sample impact data to obtain the top 20 target frequency components sorted by amplitude. The target frequency components are the top 20 frequency points with the highest amplitudes in the spectral analysis results within a preset time period. These 20 target frequency components are selected to cover the key fault characteristic frequencies. Gear faults (such as broken teeth and cracks) can significantly change the amplitude of the meshing frequency and its harmonics or sidebands. During normal gear operation, the meshing frequency and its harmonics are the primary vibration sources. A broken tooth or crack can increase the amplitude of the meshing frequency or generate new sidebands. The top 20 frequencies cover these key frequency bands, ensuring that fault signals are not missed.

[0085] Specifically, search for the first 20 order spectrum lines in the spectrum analysis results of the current gear, and obtain the maximum amplitude of the first 20 order fault spectrum lines ; Set the amplitude threshold to 50% of the maximum amplitude, so the amplitude threshold is ; Count the top 20 fault spectra and the "top 20 maximum values ​​and amplitudes reaching or exceeding "The order n (number of frequency components) of the

[0086] Furthermore, the determination of a single-sample fault diagnosis result based on the number of frequency components and the fault judgment condition includes: determining whether the number of frequency components satisfies the fault judgment condition established based on a preset fault feature number threshold; if the number of frequency components is greater than or equal to the preset fault feature number threshold, determining that the impact single-sample data has a single-tooth broken tooth diagnostic feature or a tooth root crack feature, so as to determine the corresponding single-sample fault diagnosis result; if the number of frequency components is less than the preset fault feature number threshold, determining that the impact single-sample data has no fault diagnostic feature, so as to determine the corresponding single-sample fault diagnosis result. It is understandable that the fault judgment condition is set as the size relationship between the number of frequency components and the number of 8 fault features. When the number of frequency components is greater than or equal to 8, it is determined that a single-tooth broken tooth or tooth root crack fault exists. Therefore, if n≥8, it is considered that a single-tooth broken tooth diagnostic feature or a tooth root crack diagnostic feature exists.

[0087] For example: when the amplitudes of the meshing frequency and its first few harmonics of the 20 target frequency components are high, and the amplitudes of other frequency components are low, the impact single sample data is judged to have no fault diagnosis results; when the amplitudes of the meshing frequency of the 20 target frequency components increase significantly and new sidebands appear, the impact single sample data is judged to have the diagnostic characteristics of a single gear tooth breakage; when the amplitudes of the high-order harmonics of the meshing frequency of the 20 target frequency components increase abnormally, the impact single sample data is judged to have the diagnostic characteristics of a tooth root crack.

[0088] In this way, by performing spectral analysis on the current gear impact single-sample data daily, we ensure timely detection of daily fault characteristics. By filtering the top 20 target frequency components to capture key characteristics of gear faults, we reduce computational effort, optimize efficiency, and adapt to actual engineering needs while ensuring diagnostic accuracy. Daily preliminary diagnosis of the impact single-sample data is performed to screen out potential fault signals, reducing the amount of data required for long-term tracking. However, impact single-sample data can be affected by random factors such as transient noise and brief equipment anomalies, leading to misjudgments. For example, a high-amplitude spectral line appearing in a measurement may be caused by temporary vibration rather than a true fault. In the early stages of a fault, the spectral characteristics of cracks or wear may be very weak (e.g., with amplitudes below the threshold), making single-sample analysis prone to missing these early signals. Single-sample analysis only reflects the current state and cannot determine whether the fault is developing or worsening. Therefore, after performing fault diagnosis on the impact single-sample data, further analysis from other perspectives is performed to obtain comprehensive data.

[0089] Step S12: performing trend analysis of gear impact and meshing spectrum impact based on each of the impact single sample data to obtain gear impact trend results and meshing spectrum impact trend results respectively, and obtaining gear impact continuity statistics of each of the impact single sample data.

[0090] In this embodiment, the strength index of the gear impact signal strength within the first single time period and the strength index of the meshing spectrum impact signal strength within the second single time period of each of the impact single sample data are calculated respectively to obtain the first time series trend data of the gear impact signal strength and the second time series trend data of the meshing spectrum impact signal strength respectively; through a multi-time scale dynamic smoothing method, and based on the first time series trend data and the second time series trend data, a trend evolution analysis is performed on the impact trend data within a preset time period to obtain the gear impact trend results and the meshing spectrum impact trend results respectively. It can be understood that a single time period can be set to 24 hours, as can be seen from the specific embodiment of the above steps. Therefore, the root mean square value of the gear impact dB (Decibel) of the impact single sample data is calculated on a daily basis to obtain the first single-day strength index; the root mean square value of the meshing spectrum impact dB of the impact single sample data is calculated on a daily basis to obtain the second single-day strength index; then, based on the first single-day strength index of the day and the first single-day strength index of the history, the gear impact dB root mean square trend data is formed, that is, the first time series trend data; based on the second single-day strength index of the day and the second single-day strength index of the history, the meshing spectrum impact dB root mean square trend data is formed, that is, the second time series trend data; in this way, the impact trend data is obtained, and the impact trend data specifically includes the first time series trend data and the second time series trend data, and then according to the long and short moving average sliding average method, the gear impact dB root mean square trend data and the meshing spectrum impact dB root mean square trend data are trend analyzed respectively to analyze whether there is an obvious upward trend, and obtain a trend upward result. Among them, trend analysis can be performed using two different implementation methods alone, or in combination with the two implementation methods. The specific trend analysis process is as follows:

[0091] In one embodiment, a first preset time window is used to perform sliding average processing on the first time series trend data within the preset time period to obtain a first short-term moving average; a second preset time window is used to perform sliding average processing on the first time series trend data within the preset time period to obtain a first long-term moving average; a first preset time window is used to perform sliding average processing on the second time series trend data within the preset time period to obtain a second short-term moving average; a second preset time window is used to perform sliding average processing on the second time series trend data within the preset time period to obtain a second long-term moving average; the corresponding data mean points on the first short-term moving average and the first long-term moving average under the same single time period are respectively extracted, and difference processing is performed to obtain a first short-term variation; the same The corresponding data mean points on the second short-term moving average and the second long-term moving average in a single time period are subjected to difference processing to obtain the second short-term variation; the two data mean points on the first long-term moving average located in adjacent single time periods are subjected to difference processing to obtain the first long-term variation; the two data mean points on the second long-term moving average located in adjacent single time periods are subjected to difference processing to obtain the second long-term variation; if the first short-term variation satisfies the short-term variation threshold condition, and the first long-term variation satisfies the long-term variation threshold condition, then the gear impact trend result is determined to be a trend-up result; if the second short-term variation satisfies the short-term variation threshold condition, and the second long-term variation satisfies the long-term variation threshold condition, then the meshing spectrum impact trend result is determined to be a trend-up result. It can be understood that, if Figure 2As shown, the preset time period is set to 30 days including the current day, and then a smaller time window, such as 7 days, is used to perform sliding processing on the first time series trend data or the second time series trend data (RMS trend data z), and then the weekly change (7-day change) is calculated to obtain the first short-term moving average or the second short-term moving average, respectively. A larger time window, such as 28 days, is further used to perform sliding processing on the first time series trend data or the second time series trend data (RMS trend data z), and then the 28-day change is calculated to obtain the first long-term moving average or the second long-term moving average, respectively. Furthermore, if the analysis requirement is for a fault diagnosis requirement on a certain day, the data mean point (RMS mean) corresponding to the current day is obtained from the first short-term moving average, and the data mean point corresponding to the current day is obtained from the first long-term moving average. The data mean point on the first short-term moving average is subtracted from the numerical mean point on the first long-term moving average to obtain the first short-term change to measure the magnitude of the short-term deviation from the long-term trend (the first short-term change). If the first short-term change is greater than the threshold A, it indicates that the recent impact intensity is higher than the long-term average level. At the same time, the difference between the current day's long-term moving average (the average point of the data on the first long-term moving average belonging to that day) and the previous day's long-term moving average (the average point of the data on the first long-term moving average belonging to the previous day) is calculated to obtain the first long-term change, which reflects the evolution rate of the long-term trend. If the first long-term change is greater than threshold B, it indicates that the long-term trend is generally upward. Therefore, if the first short-term change is greater than threshold A and the first long-term change is greater than threshold B, it indicates that the gear impact trend result is an upward trend. The trend analysis process for the meshing general impact trend is similar to that for the gear impact trend, so it will not be repeated here. In this way, by directly comparing the mean changes in different time windows, we can directly focus on whether the trend is accelerating or slowing down.

[0092] In another embodiment, a first preset time window is used to perform sliding average processing on the first time series trend data within the preset time period to obtain a first short-time moving average; a second preset time window is used to perform sliding average processing on the first time series trend data within the preset time period to obtain a first long-time moving average; a first preset time window is used to perform sliding average processing on the second time series trend data within the preset time period to obtain a second short-time moving average; a second preset time window is used to perform sliding average processing on the second time series trend data within the preset time period to obtain a second long-time moving average; a trend evolution analysis is performed on the gear impact signal strength of the impact trend data based on the relative position of the moving average between the first short-time moving average and the first long-time moving average and the size of the slope of the moving average to obtain a gear impact trend result; a trend evolution analysis is performed on the meshing spectrum impact signal strength of the impact trend data based on the relative position of the moving average between the second short-time moving average and the second long-time moving average and the size of the slope of the moving average to obtain a meshing spectrum impact trend result. It can be understood that the preset time period is set to 30 days including the current day, and then a smaller time window, such as 7 days, is used to perform sliding processing on the first time series trend data or the second time series trend data (RMS impact trend data z), and then the weekly change (7-day change) is calculated to obtain the first short-term moving average or the second short-term moving average respectively, and further a larger time window, such as 28 days, is used to perform sliding processing on the first time series trend data or the second time series trend data (RMS trend data z), and then After calculating the 28-day change, the first or second long-term moving average is obtained. The short-term and long-term moving averages are then analyzed for their relative position. For example, if the short-term moving average is higher than the long-term moving average for K consecutive days (e.g., 3 days) and exceeds threshold A, it is marked as a "short-term rising trend." Moving average slope analysis is then performed on the short-term and long-term moving averages. For example, the slopes of the short-term and long-term moving averages are calculated (e.g., linear regression fitting). If the slope of the short-term moving average is greater than the slope of the long-term moving average, and the slope difference exceeds threshold B, it is marked as an "accelerating upward trend." Finally, a comprehensive judgment is performed to generate an "upward trend result" if any of the following conditions are met:

[0093] Condition 1: The short-term trend is rising (the moving average position continues to cross upward).

[0094] Condition 2: Accelerating upward trend (the slope of the moving average increases significantly).

[0095] According to the above-mentioned slope relative position analysis and moving average slope analysis methods, the trend directions of the first short-term moving average and the first long-term moving average, and the trend directions of the second short-term moving average and the second long-term moving average are analyzed respectively to obtain the gear impact trend results and the meshing spectrum impact trend results respectively. Among them, if the trend evolution analysis of the gear impact signal intensity meets the rising condition, it is marked as "gear impact trend rising"; if the trend evolution analysis of the meshing spectrum impact signal intensity meets the rising condition, it is marked as "meshing spectrum trend rising".

[0096] This approach converts the raw signal into a single-day strength index (calculating the root mean square value), eliminates high-frequency noise and transient interference, and applies multi-timescale smoothing to the calculated single-day strength index, separating short-term fluctuations from long-term trends. The relative position of the short-term and long-term moving averages verifies the persistence of the fault, while the long-term moving average verifies the long-term health status, forming a closed loop of "instant detection + trend tracking." This combination of the two enables an upgrade from "single-day snapshots" to "full-cycle trends," providing a reliable technical path for the accurate identification and early warning of gear fracture failures.

[0097] In this embodiment, based on the first time series trend data of each impact single sample data and the actual operating mileage information of the equipment where the current gear is located, the gear impact continuity statistics of the impact trend data within every 100 kilometers are calculated. Specifically, the target frequency of the intensity index within the first single time period of the first time series trend data of each impact single sample data being greater than the preset intensity index threshold within the single time period is counted; based on the target frequency and the actual operating mileage of the equipment where the current gear is located, the gear impact continuity statistics of the impact trend data within every 100 kilometers are calculated using a preset gear impact continuity statistics equation; the preset gear impact continuity statistics equation is:

[0098] ;

[0099] in, Indicates the target frequency of the daily strength index being greater than the threshold of the strength index within the preset single time period. Indicates the actual operating mileage of a single day. It represents the statistics of gear impact continuity. The single time period is a single day.

[0100] It can be understood that the impact continuity statistics (the number of gear dB per 100 kilometers) are calculated based on the first time series trend data. Specifically, the target frequency of the first single-day intensity index greater than 0 in the first time series trend data (impact trend data) of the impact single sample data of a certain day and the actual single-day operating history of the equipment where the current gear is located are counted. The abnormal frequency of the gear impact signal per 100 kilometers is calculated through the above-mentioned preset gear impact continuity statistics equation to obtain the gear impact continuity statistics.

[0101] In this way, if the gear shock dB value (first time series trend data) occasionally rises on a given day, but the gear shock continuity statistic doesn't increase significantly, it's likely a brief disturbance rather than a persistent fault, thus ruling out occasional interference. Furthermore, if the dB value per 100 km continues to rise, it indicates that fault shock events are becoming more frequent with increasing mileage, further confirming a worsening fault. Furthermore, by normalizing the gear shock continuity statistic to a single sample of shock data per 100 km, we avoid the misjudgment caused by directly counting the number of shocks per day when the daily mileage of different devices varies significantly (e.g., 200 km on one day and only 50 km on another). This allows for a consistent judgment of data under different operating conditions. For example: Device A: 10 instances of dB > 0 were detected during a 200 km run, resulting in a dB > 0 value per 100 km of 5. Device B: 3 instances of dB > 0 were detected during a 50 km run, resulting in a dB > 0 value per 100 km of 6. Conclusion: Device B has a higher frequency of shocks and warrants particular attention.

[0102] Step S13: Determine whether the current gear has a gear breakage fault based on the current health level of the current gear, the single sample fault diagnosis result, the gear impact trend result, the meshing spectrum impact trend result and the gear impact continuity statistic, so as to obtain a corresponding gear breakage identification result.

[0103] In this embodiment, the gear health level is pre-set to five levels: normal, sub-healthy, minor fault, moderate fault, and severe fault. The current health level of the current gear state is obtained. Specifically, the current health level of the current gear is output based on the train key component health assessment and intelligent operation and maintenance model. A determination is made as to whether the current health level is within any of the unhealthy levels to obtain a corresponding level determination result. The unhealthy levels include the pre-set sub-healthy level, the pre-set minor fault level, the pre-set moderate fault level, and the pre-set severe fault level. Among them, after obtaining the impact single sample data, the obtained impact single sample data is transmitted to the external train key component health assessment and intelligent operation and maintenance model, and the train key component health assessment and intelligent operation and maintenance model can output the health level of the current gear. Among them, the current health level can be determined by the train key component health assessment and intelligent operation and maintenance model based on historical single sample analysis results (daily single sample failure ratio (such as broken teeth, crack feature ratio)), trend data, statistics (dB per 100 kilometers, impact event frequency, etc.), and external information (load intensity, lubrication status, operating time, etc.). The train key component health assessment and intelligent operation and maintenance model obtains the health score of the current gear through weighted calculation of the above-mentioned various indicators, and determines the current health level based on the score division rules, wherein the score division rules are as follows: normal: health score <0.2, sub-health: 0.2≤health score <0.5, minor fault: 0.5≤health score <0.7, moderate fault: 0.7≤health score <0.9, serious fault: health score ≥0.9. It should be noted that the above health level determination process is only an example, and the health level determination and output process of the train key component health assessment and intelligent operation and maintenance model are not specifically limited.

[0104] In this embodiment, if the current health level of the current gear is in any of the unhealthy levels, the number of single-tooth broken tooth diagnostic features or tooth root crack diagnostic features in the single-sample fault diagnosis result accounts for a feature ratio greater than a first preset fault feature ratio relative to the total number of single-sample fault diagnosis results, and the gear impact trend result is an upward trend result, then the current gear is determined to have a gear breakage fault, and a gear breakage fault alarm is issued. It is understood that if the current gear is in any of the unhealthy levels, and the gear impact trend result is an upward trend result (the short-term moving average is continuously higher than the long-term moving average), and the single-sample fault ratio is greater than the first preset fault feature ratio (0.5), then the current gear is determined to have a gear breakage fault. That is, if the gear impact trend worsens and transient faults occur frequently, a gear breakage fault is confirmed, and a gear breakage fault alarm is directly issued.

[0105] For example: Scenario 1: Health level: minor fault; Gear impact trend result: rising for 5 consecutive days (short moving average > long moving average); Single sample failure ratio: 0.6, Judgment result: Trigger condition, output "Gear breakage fault, repair recommended".

[0106] In this embodiment, if the current health level of the current gear is in any of the unhealthy levels, the number of single-tooth broken tooth diagnostic features or tooth root crack diagnostic features in the single-sample fault diagnosis result exceeds a first preset fault feature ratio, and the meshing spectrum impact trend result shows a downward trend and / or the gear impact continuity statistic exceeds a preset statistical threshold, then the current gear is determined to have a gear breakage fault and a gear breakage fault alarm is issued. It is understood that if the current health level of the current gear is in any of the unhealthy levels and the gear impact trend result does not increase (the trend is stable or fluctuating), and the single-sample fault ratio exceeds the first preset fault feature ratio, then if the following additional conditions (either one of them is sufficient) are met: the meshing spectrum impact trend result decreases, or the gear impact continuity statistic ratio exceeds the preset statistical threshold (increasing surface impact event density), then the current gear is determined to have a gear breakage fault. That is, the gear impact trend has not deteriorated, but the meshing state is abnormal or the impact frequency increases, confirming that a breakage fault has occurred and a gear breakage fault alarm is issued.

[0107] For example: Scenario 2: Health level: Sub-healthy; Gear impact trend result: Stable; Single sample failure ratio: 0.6; Meshing spectrum impact trend result: Declining (tooth surface wear); Judgment result: Trigger condition, output alarm.

[0108] Furthermore, if the historical fault identification result of the current gear has been determined to be a broken fault but has not been repaired, the conclusion of the gear broken fault is maintained and a gear broken fault alarm is directly issued, that is, to ensure that unhandled faults are not missed and are forced to be tracked until repair.

[0109] Step S14: If the gear breakage identification result is that there is no gear breakage fault, a gear breakage warning judgment process is performed to obtain a corresponding gear breakage warning result.

[0110] In this embodiment, early signs of gear breakage faults are captured, and an early warning is issued before the fault reaches the alarm threshold, supporting preventive maintenance. Specifically, if the gear breakage identification result is that there is no gear breakage fault, a gear breakage warning judgment process is performed to obtain the corresponding gear breakage warning result, including: if an external alarm message is received, and the number of features of the single-tooth broken tooth diagnosis feature or the tooth root crack diagnosis feature in the single-sample fault diagnosis result accounts for a feature ratio greater than the second preset fault feature ratio of the total number of results of the single-sample fault diagnosis result, a gear breakage warning is performed; wherein the second preset fault feature ratio is less than the first preset fault feature ratio; or, if no external alarm message is received, and if it is detected that the meshing spectrum impact trend result is a trend rising result and the gear impact continuity statistic is greater than the preset statistic threshold, a gear breakage warning is performed. It can be understood that the breakage warning does not require a health level threshold, and the warning can still be triggered even if the health level is normal. The triggering conditions for the breakage warning (including early crack fault of the gear) are:

[0111] Condition 1: When external warning / alarm information is present (in the rail transit sector, this refers to the real-time alarm information output by the onboard fault monitoring device for the current gear within the current cycle), the single-sample fault ratio is greater than the second preset fault characteristic ratio (0.2), for example, ≥2 out of 10 analyses display the fault characteristic. This indicates that combining external warnings with weak fault signals can provide early warning.

[0112] Condition 2: In the absence of external warning / alarm information, the meshing spectrum impact trend increases, and the gear impact continuity statistic exceeds the preset statistical threshold (0.5), triggering an early warning. This shows that early faults can be identified independently through abnormal meshing state and changes in impact frequency.

[0113] Scenario 1: Health level: Normal; Lubrication system alarm (pre-alarm information) present; Single-sample failure ratio: 0.3 (exceeding threshold D=0.2); Judgment result: Trigger condition ①, output "Early fracture characteristics present, inspection recommended."

[0114] Scenario 2: Health level: Normal; Meshing spectrum trend: Increasing for three consecutive days (early stage of crack propagation); Gear impact continuity statistic: Increasing from 5 to 8 (increase ratio 60% > 0.5); Judgment result: Trigger condition ②, output warning.

[0115] like Figure 3 The figure shows the overall process of the gear fracture identification method, which includes six main steps: data collection, single sample analysis, trend analysis, statistical calculation, fault judgment and early warning analysis. Specifically:

[0116] First, perform data acquisition. Specifically, the input is the impact signal of the gear during operation (collected by the sensor). The output is the impact single sample data (time domain waveform) of each gear measurement point.

[0117] Specific operation: Install sensors at key positions of gears (such as tooth roots and near bearings), collect impact signals in real time, and store them as single sample data on a daily basis.

[0118] Next, single-sample fault diagnosis analysis is performed. Specifically, the input is the impact single-sample data of a single measurement. The output is the single-sample fault diagnosis result (broken tooth, crack, or no fault).

[0119] Operation: Perform spectrum analysis (FFT) on single sample data and extract the first 20 spectral lines.

[0120] The order n of the statistical amplitude ≥ 50% of the maximum amplitude is calculated. If n ≥ 8, it is determined that there is a broken tooth or crack fault; otherwise, it is determined that there is no fault.

[0121] Example: In a certain measurement, 10 of the first 20 spectral lines have amplitudes ≥ 50% of the maximum amplitude → This is determined to be a broken tooth fault.

[0122] Further trend analysis is performed. Specifically, the input is the root mean square (RMS) value of the daily gear shock dB and mesh spectrum shock dB. Output: gear shock trend results and mesh spectrum shock trend results (increase / decrease indicators).

[0123] Specific Operation: Calculate the RMS value of gear impact dB and mesh spectrum impact dB on a daily basis to generate trend data. Smooth the trend data using a sliding average of the short and long moving averages (e.g., a 7-day short moving average and a 30-day long moving average). If the short moving average is continuously above the long moving average, mark it as "trend up"; otherwise, mark it as "trend stable or down."

[0124] Example: Gear Impact dB trend is rising for 5 consecutive days (short moving average > long moving average) → mark as “Trending Up”.

[0125] Then, based on the trend analysis, we further conduct trend statistics. Specifically, the input is gear impact dB trend data. The output is gear dB per 100 kilometers (impact continuity statistics).

[0126] Specific operation: Count the number of times the gear impact dB>0 in a single day, and calculate the dB number per 100 kilometers.

[0127] Example: On a certain day, the number of gear impact dB>0 is 10 times, and the operating mileage is 200 kilometers → dB per 100 kilometers = 5.

[0128] Perform a gear breakage fault determination. Specifically, the inputs are: health level, single sample failure rate, trend indicator, and dB / 100km. Output: Gear breakage fault alarm (yes / no).

[0129] Prerequisite: Health Level ≥ Sub-Health. Determination Conditions (Any of the following conditions will trigger an alarm): Condition 1: Increasing gear impact trend + Single-sample failure ratio > Threshold A (0.5); Condition 2: Stable gear impact trend + Single-sample failure ratio > Threshold B (0.5) + (Decreasing meshing spectrum trend or dB / 100km increase ratio > C (0.5)). Condition 3: Historical faults remain unresolved.

[0130] Example: Health level: minor fault, gear impact trend increasing, single sample fault ratio: 0.6, judgment result: trigger alarm.

[0131] Finally, perform gear breakage early warning analysis. Specifically, the inputs are: pre- / alarm information, single-sample failure rate, meshing spectrum trend, and dB / 100km. Output: Gear breakage early warning (yes / no). Decision conditions (a warning is issued if any of the following conditions are met): Condition 1: Pre- / alarm information exists + single-sample failure rate > threshold D (0.2). Condition 2: No pre- / alarm information exists + meshing spectrum trend increases + dB / 100km increase > E (0.5).

[0132] Example: The meshing spectrum trend increases for three consecutive days, and the dB per 100 kilometers increases from 5 to 8 (increase ratio 60%). Judgment result: trigger an early warning.

[0133] It can be seen that the present application discloses a gear breakage identification method, including: performing single-sample fault diagnosis on the impact single-sample data of each gear measuring point of the current gear to obtain a single-sample fault diagnosis result; performing trend analysis of gear impact and meshing spectrum impact based on each of the impact single-sample data to obtain a gear impact trend result and a meshing spectrum impact trend result, and obtaining the gear impact continuity statistics of each of the impact single-sample data; determining whether the current gear has a gear breakage fault based on the current health level of the current gear, the single-sample fault diagnosis result, the gear impact trend result, the meshing spectrum impact trend result, and the gear impact continuity statistics to obtain a corresponding gear breakage identification result; if the gear breakage identification result is that there is no gear breakage fault, then performing a gear breakage warning judgment process to obtain a corresponding gear breakage warning result. It can be seen that multi-angle features are extracted through single-sample analysis and trend analysis (gear impact, meshing spectrum trend). Then, comprehensive gear breakage fault identification is performed in combination with health level and multi-angle characteristics to avoid misjudgment of a single indicator. In addition, the false alarm rate is reduced through triple verification of single sample characteristics, trends, and statistics. In addition, early warning of gear breakage faults (including early gear crack faults) is achieved through single sample analysis and trend analysis.

[0134] Reference Figure 4As shown, the present invention provides a gear breakage identification device, comprising:

[0135] The single sample diagnosis module 11 is used to perform single sample fault diagnosis on the impact single sample data of each gear measuring point of the current gear to obtain a single sample fault diagnosis result;

[0136] a trend analysis module 12 for performing trend analysis of gear impact and meshing spectrum impact based on each of the impact single sample data, so as to obtain a gear impact trend result and a meshing spectrum impact trend result, respectively, and to obtain a gear impact continuity statistic of each of the impact single sample data;

[0137] a fault identification module 13, configured to determine whether the current gear has a gear breakage fault based on the current health level of the current gear, the single sample fault diagnosis result, the gear impact trend result, the meshing spectrum impact trend result, and the gear impact continuity statistic, so as to obtain a corresponding gear breakage identification result;

[0138] The fault warning module 14 is configured to perform a gear breakage warning judgment process to obtain a corresponding gear breakage warning result if the gear breakage identification result indicates that there is no gear breakage fault.

[0139] It can be seen that the present application discloses performing single-sample fault diagnosis on the impact single-sample data of each gear measuring point of the current gear to obtain a single-sample fault diagnosis result; performing trend analysis of gear impact and meshing spectrum impact based on each of the impact single-sample data to obtain gear impact trend results and meshing spectrum impact trend results, and obtaining the gear impact continuity statistics of each of the impact single-sample data; determining whether the current gear has a gear breakage fault based on the current health level of the current gear, the single-sample fault diagnosis result, the gear impact trend result, the meshing spectrum impact trend result and the gear impact continuity statistics to obtain a corresponding gear breakage identification result; if the gear breakage identification result is that there is no gear breakage fault, then performing a gear breakage warning judgment process to obtain a corresponding gear breakage warning result. It can be seen that multi-angle features are extracted through single-sample analysis and trend analysis (gear impact, meshing spectrum trend). Then, comprehensive gear breakage fault identification is performed in combination with health level and multi-angle characteristics to avoid misjudgment of a single indicator. In addition, the false alarm rate is reduced through triple verification of single sample characteristics, trends, and statistics. In addition, early warning of gear breakage faults (including early gear crack faults) is achieved through single sample analysis and trend analysis.

[0140] Furthermore, the embodiment of the present application also discloses an electronic device, Figure 5This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram should not be considered as any limitation to the scope of application of the present application.

[0141] Figure 5 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps of the gear breakage identification method disclosed in any of the aforementioned embodiments. Furthermore, the electronic device 20 in this embodiment may be a computer.

[0142] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device. The communication protocol it follows is any communication protocol that can be applied to the technical solution of this application and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world. Its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0143] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 can be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 21 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0144] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or CD, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0145] The operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20, enabling the processor 21 to calculate and process the massive amount of data 223 in the memory 22. It can be Windows Server, NetWare, Unix, Linux, etc. In addition to including computer programs capable of implementing the gear breakage identification method disclosed in any of the aforementioned embodiments and executed by the electronic device 20, the computer programs 222 may further include computer programs capable of performing other specific tasks. Data 223 may include data received by the electronic device from external devices as well as data collected by its own input / output interface 25.

[0146] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when executed by a processor, the computer program implements the aforementioned gear breakage identification method. The specific steps of this method can be referred to the corresponding contents disclosed in the aforementioned embodiments and will not be repeated here.

[0147] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Reference can be made to the descriptions of the identical or similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and the relevant parts can be referred to the descriptions of the methods.

[0148] Professionals may further appreciate that the units and algorithmic steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application. The steps of the method or algorithm described in conjunction with the embodiments disclosed herein can be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module can be placed in a random access memory RAM (Random Access Memory), memory, read-only memory ROM (Read Only Memory), electrically programmable EPROM (Electrically Programmable Read Only Memory), electrically erasable programmable EEPROM (Electric Erasable Programmable Read Only Memory), registers, hard disk, removable disk, CD-ROM (Compact Disc-Read Only Memory), or any other form of storage medium known in the technical field.

[0149] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0150] The above is a detailed introduction to the solution provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A gear breakage identification method, characterized in that: include: Perform single-sample fault diagnosis on the impact single-sample data of each gear measuring point of the current gear to obtain a single-sample fault diagnosis result; Performing trend analysis on the gear impact and meshing spectrum impact based on each of the impact single sample data to obtain a gear impact trend result and a meshing spectrum impact trend result, respectively, and obtaining a gear impact continuity statistic for each of the impact single sample data; Outputting the current health level of the current gear based on the health assessment and intelligent operation and maintenance model of key train components, determining whether the current health level is in any of the unhealthy levels, and obtaining a corresponding level judgment result; wherein the unhealthy levels include a preset sub-health level, a preset minor fault level, a preset moderate fault level, and a preset severe fault level; Determining whether the current gear has a gear breakage fault based on the current health level of the current gear, the single sample fault diagnosis result, the gear impact trend result, the meshing spectrum impact trend result, and the gear impact continuity statistic to obtain a corresponding gear breakage identification result; If the gear breakage identification result is that there is no gear breakage fault, the gear breakage warning judgment process is performed to obtain a corresponding gear breakage warning result; The determining whether the current gear has a gear breakage fault according to the current health level of the current gear, the single sample fault diagnosis result, the gear impact trend result, the meshing spectrum impact trend result, and the gear impact continuity statistic includes: If the current health level of the current gear is at any level among the unhealthy levels, the feature ratio of the number of single-tooth broken tooth diagnostic features or tooth root crack diagnostic features in the single-sample fault diagnosis result to the total number of results of the single-sample fault diagnosis result is greater than a first preset fault feature ratio, and the gear impact trend result is an upward trend result, then it is determined that the current gear has a gear breakage fault, and a gear breakage fault alarm is issued; If the current health level of the current gear is at any level among the unhealthy levels, the gear impact trend result is a non-increasing trend result, the feature ratio of the number of single-tooth broken tooth diagnostic features or tooth root crack diagnostic features in the single-sample fault diagnosis result to the total number of results of the single-sample fault diagnosis result is greater than a first preset fault feature ratio, and the meshing spectrum impact trend result is a decreasing trend result and / or the gear impact continuity statistic is greater than a preset statistic threshold, then it is determined that the current gear has a gear breakage fault, and a gear breakage fault alarm is issued.

2. The gear breakage identification method according to claim 1, characterized in that: The single-sample fault diagnosis is performed on the impact single-sample data of each gear measuring point of the current gear to obtain a single-sample fault diagnosis result, including: Performing spectrum analysis on the impact single sample data of each gear measuring point of the current gear, obtaining a preset number of target frequency components after spectrum analysis in descending order of amplitude, and determining the maximum amplitude, so as to set an amplitude threshold based on the maximum amplitude; The number of frequency components in the target frequency components that are greater than the amplitude threshold is counted, and a single-sample fault diagnosis result is determined based on the number of frequency components and a fault judgment condition.

3. The gear breakage identification method according to claim 2, characterized in that: The determining of a single-sample fault diagnosis result based on the number of frequency components and the fault judgment condition includes: Determining whether the number of frequency components meets a fault determination condition established based on a preset fault feature number threshold; If the number of the frequency components is greater than or equal to the preset fault feature number threshold, it is determined that the impact single sample data has a single tooth broken tooth diagnosis feature or a tooth root crack feature, so as to determine the corresponding single sample fault diagnosis result; If the number of the frequency components is less than the preset fault feature number threshold, it is determined that the impact single sample data has no fault diagnosis feature to determine the corresponding single sample fault diagnosis result.

4. The gear breakage identification method according to claim 1, characterized in that: The trend analysis of gear impact and meshing spectrum impact is performed based on each of the impact single sample data to obtain gear impact trend results and meshing spectrum impact trend results, respectively, including: Calculating the strength index of the gear impact signal strength within a first single time period and the strength index of the meshing spectrum impact signal strength within a second single time period of each of the impact single sample data, respectively, to obtain first time series trend data of the gear impact signal strength and second time series trend data of the meshing spectrum impact signal strength; By using a multi-time-scale dynamic smoothing method, and based on the first time series trend data and the second time series trend data, a trend evolution analysis is performed on the impact trend data within a preset time period to obtain gear impact trend results and meshing spectrum impact trend results, respectively.

5. The gear breakage identification method according to claim 4, characterized in that: The method of performing trend evolution analysis on the impact trend data within a preset time period based on the first time series trend data and the second time series trend data by using a multi-time scale dynamic smoothing method to obtain a gear impact trend result and a meshing spectrum impact trend result, respectively, includes: Performing sliding average processing on the first time series trend data within the preset time period using a first preset time window to obtain a first short-term moving average; Performing sliding average processing on the first time series trend data within the preset time period using a second preset time window to obtain a first long-term moving average; Performing sliding average processing on the second time series trend data within the preset time period using the first preset time window to obtain a second short-term moving average; Performing sliding average processing on the second time series trend data within the preset time period using a second preset time window to obtain a second long-term moving average; extracting the corresponding data mean points on the first short-term moving average and the first long-term moving average in the same single time period, respectively, and performing difference processing to obtain a first short-term variation; respectively extracting corresponding data mean points on the second short-term moving average and the second long-term moving average in the same single time period, and performing difference processing to obtain a second short-term variation; performing a difference processing on two data mean points located in adjacent single time periods on the first long-term moving average to obtain a first long-term variation; performing a difference processing on two data mean points on the second long-term moving average that are located in adjacent single time periods to obtain a second long-term variation; If the first short-term variation satisfies a short-term variation threshold condition, and the first long-term variation satisfies a long-term variation threshold condition, determining that the gear impact trend result is an upward trend result; If the second short-time variation satisfies a short-time variation threshold condition, and the second long-time variation satisfies a long-time variation threshold condition, it is determined that the meshing spectrum impact trend result is a trend rising result.

6. The gear breakage identification method according to claim 4, characterized in that: The method of performing trend evolution analysis on the impact trend data within a preset time period based on the first time series trend data and the second time series trend data by using a multi-time scale dynamic smoothing method to obtain a gear impact trend result and a meshing spectrum impact trend result, respectively, includes: Performing sliding average processing on the first time series trend data within the preset time period using a first preset time window to obtain a first short-term moving average; Performing sliding average processing on the first time series trend data within the preset time period using a second preset time window to obtain a first long-term moving average; Performing a sliding average process on the second time series trend data within the preset time period using the first preset time window to obtain a second short-term moving average; Performing sliding average processing on the second time series trend data within the preset time period using a second preset time window to obtain a second long-term moving average; Performing a trend evolution analysis on the gear impact signal strength of the impact trend data based on the relative position and slope of the first short-term moving average and the first long-term moving average to obtain a gear impact trend result; Based on the relative position and slope of the second short-term moving average and the second long-term moving average, a trend evolution analysis is performed on the meshing spectrum impact signal intensity of the impact trend data to obtain a meshing spectrum impact trend result.

7. The gear breakage identification method according to claim 4, characterized in that: The obtaining of the gear impact continuity statistics of each of the impact single sample data includes: Based on the first time series trend data of each impact single sample data and the actual operating mileage information of the equipment where the current gear is located, the gear impact continuity statistics of the impact trend data within every 100 kilometers are calculated.

8. The gear breakage identification method according to claim 7, characterized in that: The calculation of the gear impact continuity statistics of the impact trend data within every 100 kilometers based on the first time series trend data of each impact single sample data and the actual operating mileage information of the equipment where the current gear is located includes: Counting the target frequency of the intensity index in the first single time period of the first time series trend data of each of the impact single sample data being greater than the preset intensity index threshold value in the single time period; Calculate the gear impact continuity statistics of the impact trend data within every 100 kilometers according to the target frequency and the actual operating mileage of the equipment where the current gear is located using a preset gear impact continuity statistics equation; The preset gear impact continuity statistics equation is: ； in, represents the target frequency, Indicates the actual operating mileage, Represents the gear impact continuity statistic.

9. The gear breakage identification method according to claim 1, characterized in that: The gear breakage warning judgment process is performed to obtain the corresponding gear breakage warning result, including: If an external alarm message is received and the number of single-tooth broken tooth diagnostic features or tooth root crack diagnostic features in the single-sample fault diagnosis result accounts for a feature ratio greater than a second preset fault feature ratio in the total number of results of the single-sample fault diagnosis result, a gear breakage warning is issued; wherein the second preset fault feature ratio is less than the first preset fault feature ratio; Alternatively, if no external alarm information is received, and it is detected that the meshing spectrum impact trend result is an upward trend result and the gear impact continuity statistic is greater than a preset statistic threshold, a gear breakage warning is issued.

10. A gear breakage identification device, characterized in that: include: The single sample diagnosis module is used to perform single sample fault diagnosis on the impact single sample data of each gear measuring point of the current gear to obtain a single sample fault diagnosis result; a trend analysis module for performing trend analysis of gear impact and meshing spectrum impact based on each of the impact single sample data, so as to obtain a gear impact trend result and a meshing spectrum impact trend result, respectively, and to obtain a gear impact continuity statistic of each of the impact single sample data; The gear breakage identification device is further configured to output the current health level of the current gear based on the train key component health assessment and intelligent operation and maintenance model, and determine whether the current health level is at any of the unhealthy levels, so as to obtain a corresponding level judgment result; wherein the unhealthy levels include a preset sub-healthy level, a preset minor fault level, a preset moderate fault level, and a preset severe fault level; a fault identification module, configured to determine whether the current gear has a gear breakage fault based on the current health level of the current gear, the single sample fault diagnosis result, the gear impact trend result, the meshing spectrum impact trend result, and the gear impact continuity statistic, so as to obtain a corresponding gear breakage identification result; a fault warning module, configured to, if the gear breakage identification result indicates that there is no gear breakage fault, perform a gear breakage warning judgment process to obtain a corresponding gear breakage warning result; The fault identification module is specifically configured to determine that a gear breakage fault exists in the current gear and issue a gear breakage fault alarm if the current health level of the current gear is at any one of the unhealthy levels, the feature ratio of the number of single-tooth broken tooth diagnostic features or tooth root crack diagnostic features in the single-sample fault diagnosis result to the total number of results of the single-sample fault diagnosis result is greater than a first preset fault feature ratio, and the gear impact trend result is an upward trend result; and if the current health level of the current gear is at any one of the unhealthy levels, the gear impact trend result is a non-increasing trend result, the feature ratio of the number of single-tooth broken tooth diagnostic features or tooth root crack diagnostic features in the single-sample fault diagnosis result to the total number of results of the single-sample fault diagnosis result is greater than a first preset fault feature ratio, and the meshing spectrum impact trend result is a downward trend result and / or the gear impact continuity statistic is greater than a preset statistic threshold, determine that a gear breakage fault exists in the current gear and issue a gear breakage fault alarm.

11. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the steps of the gear breakage identification method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that Used to store a computer program; wherein, when the computer program is executed by a processor, the steps of the gear breakage identification method according to any one of claims 1 to 9 are implemented.

Images