Rack rail train driving gear fault diagnosis method, system and device

Abstract

The invention relates to the field of gear fault diagnosis, in particular to a rack rail train driving gear fault diagnosis method, system and device, and solves the problem that fault features cannot be effectively extracted from rack rail section vibration signals in the prior art. The method comprises the following steps: identifying a transition section vibration signal of a transition device; identifying a toothed rail section vibration signal; the rack rail section vibration signals are processed; and fault diagnosis is realized according to the meshing frequency and the sideband information. According to the time domain characteristics of the vibration signals, the vibration signals generated when the driving gear passes through the rack rail section can be accurately recognized from the vibration signals, and self-adaptive recognition of the vibration signals of the rack rail section is achieved. According to the method, noise interference in time-frequency distribution of toothed rail section vibration signals is removed, time-frequency aggregation of the time-frequency distribution is improved, fault features of the driving gear are extracted from the time-frequency distribution, fault diagnosis of the driving gear is achieved, health state monitoring of the toothed rail train driving gear is achieved, and train operation safety is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of gear fault diagnosis, and particularly to a method, a system and a device for diagnosing faults of a driving gear of a rack railway vehicle. Background Art

[0002] A rack railway vehicle is a special rail transit vehicle designed specifically for mountainous areas. The core difference between it and a traditional train is that a rack rail is added in the middle of the track, and at the same time, a driving gear is added at the bottom of the train. The driving gear meshes with the rack rail to provide additional traction. The rack rail mainly consists of a rack section, a transition section and a wheel-rail section, and the driving gear only participates in the meshing operation in the rack section. Due to the extremely harsh service environment of the driving gear, faults such as tooth surface wear and tooth root fracture are likely to occur. If these faults are not detected in time, it will not only exacerbate the damage of the driving gear and the rack rail, reduce the running reliability of the train and the riding comfort of passengers, but also may lead to major safety accidents.

[0003] The sideband energy ratio method for gear meshing fault detection with the application number CN201110296586.7 discloses a method for evaluating the deterioration in a gearbox. Data of harmonic frequency amplitudes and their related sidebands generated due to the operation of the gearbox are obtained from the operating gearbox, and the sideband energy ratio is calculated by dividing the amplitude of the total sideband signal associated with the harmonic by the amplitude of the harmonic; it is calculated based on the sideband amplitudes associated with adjacent harmonics, the ratio is monitored with respect to time, or compared with one or more values to provide an indication of deterioration.

[0004] However, to achieve the fault diagnosis of the driving gear, two key technologies still need to be solved: firstly, accurately identify the vibration signal of the rack section from the vibration signal, and then precisely extract the fault characteristics of the driving gear from the vibration signal of the rack section. At present, in the field of gear health monitoring, there is no effective method for accurately identifying the vibration signal of the rack section from the vibration signal of the rack railway vehicle; at the same time, in the link of fault feature extraction, the existing methods have insufficient noise suppression ability and are difficult to obtain a time-frequency distribution with high time-frequency aggregation, which directly affects the accuracy of fault feature extraction.

[0005] There is an urgent need for a new type of method, system and device for diagnosing faults of a driving gear that can solve the above problems. Summary of the Invention

[0006] A method, a system and a device for diagnosing faults of a driving gear of a rack railway vehicle proposed by the present invention solve two key problems existing in the prior art: one is that it is difficult to identify the vibration signal of the rack section from the vibration signal of the rack railway vehicle; the other is that the fault feature extraction process is seriously interfered by noise and the time-frequency resolution of the time-frequency distribution is low.

[0007] The technical solution of the present invention is realized as follows: A method for diagnosing a fault of a driving gear of a rack train comprises the following steps: S1: Identify the transition section vibration signal of the transition device: Identify the periodic high-amplitude transient impact signal generated by the driving gear passing through the transition device from the vibration signal; construct an adaptive amplitude threshold , select candidate points whose amplitude exceeds the threshold from the vibration signal; based on the roller center distance of the transition device and train line speed , calculate the theoretical impact period, and verify the periodicity of the candidate points; mark the data points that meet both the amplitude threshold conditions and the periodic characteristics as transition section vibration signals; S2: Identify the rack-rail segment vibration signal: A segment of signal is intercepted from both sides of the transition segment vibration signal in S1 as a sample, and the sample is quantitatively analyzed by the defined data energy mean characterization parameter, and the energy mean values ​​of the two samples are compared, and the sample with the larger data energy mean is marked as the rack-rail segment vibration signal; the rack-rail segment and the wheel-rail segment are located on both sides of the transition segment respectively; when the rack-rail train runs in the rack-rail segment, the driving gear meshes with the rack track, so the impact on the driving gear in the rack-rail segment is significantly stronger than that on the wheel-rail segment; the rack-rail segment vibration signal can be identified based on the time domain characteristics of the rack-rail segment and the wheel-rail segment vibration signals; S3: Process the vibration signal of the rack segment: calculate the time-frequency distribution of the vibration signal of the rack segment, and reduce the noise of the time-frequency distribution to improve the time-frequency aggregation of the time-frequency distribution; perform time-frequency analysis on the vibration signal of the rack segment to obtain the time-frequency distribution, divide the time-frequency distribution into multiple time slices, detect the local maximum point in each time slice, and divide the peak energy divergence area with the maximum point as the center; use the difference in correlation between the signal component and the noise to calculate the correlation between the peak energy divergence areas at adjacent moments, and screen out the strongly correlated areas accordingly; the strongly correlated areas are regarded as valid signal components containing fault characteristics, and the remaining areas are regarded as noise and deleted; iterative processing is performed along the time direction to obtain the time-frequency distribution with noise removed; S4: Fault diagnosis is achieved based on the meshing frequency and sideband information: the time-frequency ridge extraction algorithm is used to extract the meshing frequency ridge and its sideband of the driving gear from the time-frequency distribution; based on the driving gear parameters, it is determined whether the interval between the meshing frequency and the sideband is the rotation frequency to achieve fault diagnosis.

[0008] According to a further technical solution, step S1 specifically comprises the following steps: S11 for vibration signals , set an adaptive amplitude threshold , used to accurately identify high-amplitude transient shocks in vibration signals; the threshold The calculation formula is: (1); Among them, represents the maximum amplitude in the vibration signal, represents the 0.75 quantile of the vibration signal amplitude; From the vibration signal screen out high-amplitude transient shocks exceeding the threshold : (2); Among them, represents the amplitude satisfying the threshold condition, represents the corresponding moment, represents that there is no value at this point; S12 Since the drive gear usually passes through the transition section at a constant speed, the impact generated by the collision between the drive gear and the drum of the transition device has significant periodic characteristics; based on the center distance of the drum of the transition device and the train line speed , calculate the theoretical impact period, and accordingly perform a periodicity test on the time series . The expression for testing the periodicity of the time points is: (3); Among them, represents the moment value satisfying the impact period condition, represents the center distance between the two drums in the transition device, represents the train line speed, represents that there is no value at this point; Since the impact period is constrained in the interval , there is naturally periodicity between the time points satisfying this constraint condition; Mark the data points corresponding to the time points satisfying the periodicity determination condition as the vibration signal of the transition section.

[0009] For a further technical solution, the specific steps of S2 are: S21 The toothed rail section and the wheel-rail section are respectively located on both sides of the transition section. When the toothed rail train is running on the toothed rail section, the drive gear meshes with the rack track, so the impact on the drive gear in the toothed rail section is significantly stronger than that in the wheel-rail section; By analyzing the time-domain characteristics of the vibration signals of the toothed rail section and the wheel-rail section, the vibration signal of the toothed rail section is identified; Intercept a section of signal from each side of the vibration signal of the transition section output by S12 as samples, and set them as and ; Next, define a data energy mean characterization parameter: (4); Among them, represents the maximum amplitude in the vibration signal, represents the number of sampled data points intercepted, and this formula is used to quantify the average data energy; represents the average energy; When the rack - and - pinion train runs on the rack - and - pinion section, due to the meshing of the drive gear and the rack, the impact on the drive gear is significantly stronger than that on the wheel - rail. Based on this characteristic, compare the magnitudes of the average energies calculated from the two selected segments of samples, and define the data with a larger average energy as the vibration signal of the rack - and - pinion section: Vibration signal of the rack - and - pinion section = (5); where represents the data average energy calculated, represents the data average energy calculated.

[0010] For a further technical solution, the specific step S3 is as follows: S31 Divide the peak energy divergence region: Perform a short - time Fourier transform on the vibration signal of the rack - and - pinion section output by S22 to obtain the time - frequency distribution of the vibration signal of the rack - and - pinion section , and perform noise reduction on the time - frequency distribution; Based on Search for the set of local maxima and the set of local minima of the amplitude of this column of data along the frequency axis direction: : (6); (7); where represents the local maximum, represents the local minimum, represents at the moment when the frequency value is the amplitude, represents at the moment when the frequency value is the amplitude, is the frequency step; For each local maximum , search for its adjacent upper and lower local minima and , to form a peak energy divergence region : (8); Then the set of all peak energy divergence regions divided at the moment is defined as: (9); where The number of local maxima at time; S32 Screen the strongly correlated regions in the peak energy divergence region: Select at a certain time in to start the analysis. Assume that all peak energy divergence regions in are signal components; Subsequently, in select a peak energy divergence region , and in search for a peak energy divergence region that is the closest to it along the frequency axis , calculate the Pearson correlation coefficient between the two , and repeat the above operation until all peak energy divergence regions at time have been calculated once; (10); (11); Among them, represents the mean amplitude at TFR time, represents the result of the Pearson correlation coefficient, represents the th peak energy divergence region at represents the s th discrete frequency point; represents the number of discrete points in the frequency direction; represents the frequency variable; If the correlation coefficient result is greater than or equal to 0.7, it is determined to be a strong correlation degree, and the peak energy divergence region corresponding to time is retained; Otherwise, this region is considered noise and is directly deleted the peak energy divergence region corresponding to (12); Among them, represents the set of peak energy divergence regions retained at time, S33 Remove the noise in the time-frequency distribution: Along the time direction, repeat the method of S32 from to both ends until all peak energy divergence regions at all times are searched, and output a time-frequency distribution with noise removed .

[0011] Further technical solution: Step S3 further includes S34 to enhance the time-frequency concentration of the time-frequency distribution: Using the time rearrangement operator to compensate for the estimation deviation theoretically existing in the original frequency rearrangement operator, a frequency compensation rearrangement operator is proposed: (13); Among them, represents the derivative with respect to time, represents the instantaneous amplitude, represents the instantaneous phase, represents the instantaneous frequency, represents the window function, represents the imaginary number; represents the time-frequency distribution after removing noise; According to the instantaneous frequency estimated by the frequency compensation rearrangement operator , the time-frequency coefficients that diverge along the frequency direction are redistributed to the energy trajectory, further enhancing the time-frequency concentration of the time-frequency distribution; (14); Among them, is the time-frequency distribution of the frequency compensation synchrosqueezing transform, represents the Dirac function.

[0012] Further technical solution: Step S4 specifically is: Through the ridge line extraction algorithm, the meshing frequency of the driving gear and the rack is extracted from as well as the sidebands. According to the gear parameters, is converted into the rotational frequency of the driving gear, and it is judged whether the interval between the meshing frequency and the sidebands is the rotational frequency to achieve fault diagnosis.

[0013] The fault diagnosis system for the driving gear of the rack railway train includes a data acquisition module, a rack section data identification module, a rack section data processing module, and a driving gear fault diagnosis module that are sequentially connected; the data acquisition module includes a vibration sensor installed near the driving gear of the rack railway train; it is used to collect vibration signals; the data acquisition module is responsible for converting the analog signals collected by the sensor into digital signals and transmitting them to the data processing unit; the data processing unit runs the corresponding algorithm program; among them, the rack section data identification module marks the samples with large data energy mean as the rack section vibration signals and outputs the rack section vibration signals; the rack section data processing module performs information processing on the rack section vibration signals and outputs the time-frequency distribution of the frequency compensation synchrosqueezing transform; the driving gear fault diagnosis module uses the time-frequency ridge line extraction algorithm to extract the meshing frequency ridge line of the driving gear and its sidebands from the time-frequency distribution and then analyzes the type and severity of the fault.

[0014] The present invention also discloses a fault diagnosis device for the drive gear of a rack-rail train. The above-mentioned fault diagnosis system for the drive gear of a rack-rail train is provided, and further includes a display device for displaying the fault diagnosis result, and a power supply module; a communication module for transmitting the vibration signal of the vibration sensor; the power supply module supplies power to the communication module, the display device and the fault diagnosis system.

[0015] Compared with the prior art, the fault diagnosis method and system for the drive gear of a rack-rail train disclosed by the present invention have the following beneficial effects.

[0016] 1. According to the time-domain characteristics of the vibration signal, the vibration signal generated by the drive gear passing through the rack-rail section can be accurately identified from the vibration signal, realizing the adaptive identification of the vibration signal in the rack-rail section.

[0017] 2. By removing the noise interference in the time-frequency distribution of the vibration signal in the rack-rail section and improving the time-frequency aggregation of the time-frequency distribution, the fault characteristics of the drive gear are extracted from the time-frequency distribution, realizing the fault diagnosis of the drive gear, so as to monitor the health status of the drive gear of the rack-rail train and ensure the safe operation of the train. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 : Schematic diagram of the present application Figure 2 : Schematic diagram of the rack-rail track of the present application; Figure 3 : Flow schematic diagram of the rack-rail section data recognition module in the present application.

[0020] Figure 4 : Flow schematic diagram of the rack-rail section data processing module in the present application.

[0021] Figure 5 : Flow schematic diagram of the drive gear fault diagnosis module in the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0023] In this embodiment, it is jointly realized by a vibration sensor, a data acquisition module, a data processing unit and corresponding data processing software mounted on the rack-rail train. As Figure 1 As shown in the system schematic diagram of a method for diagnosing faults of a driving gear of a rack-rail train in this application, the vibration sensor is installed near the driving gear of the rack-rail train for collecting vibration signals; the data acquisition module is responsible for converting the analog signals collected by the sensor into digital signals and transmitting them to the data processing unit; the data processing unit runs corresponding algorithm programs, and the data processing unit includes a rack-rail section data recognition module, a rack-rail section data processing module and a driving gear fault diagnosis module; the collected vibration signals are analyzed and processed to realize the fault diagnosis of the driving gear.

[0024] As Figure 2 As shown in the schematic diagram of the rack-rail track of a method for diagnosing faults of a driving gear of a rack-rail train in this application, the rack-rail train runs on a specific rack-rail railway line, and this line includes a wheel-rail section, a transition section and a rack-rail section. Among them, the center distance between the rollers of the transition device has been set to 0.11 meters through precise measurement, and the linear velocity of the train during operation can be obtained in real time through the speed sensor of the train. At the same time, a high-precision vibration sensor is installed near the driving gear of the rack-rail train. This vibration sensor can collect vibration signals during the operation of the train at a sampling frequency of 10 kHz, and transmit the collected vibration signals to the data processing unit through the data acquisition module for subsequent analysis.

[0025] As Figure 3 As shown in the flow schematic diagram of the rack-rail section data recognition module in this application, identify the vibration signals of the transition section of the transition device: after the data processing unit receives the vibration signals collected by the vibration sensor, an adaptive amplitude threshold is constructed . Specifically, according to the preset algorithm, statistical analysis is performed on the amplitude of the vibration signals to obtain the adaptive amplitude threshold . Based on this threshold, the points with amplitudes exceeding this threshold are selected from the vibration signals as candidate points. According to the center distance between the rollers of the transition device and the linear velocity of the train , calculate the theoretical impact period . This theoretical impact period represents the time interval between two adjacent impacts when the driving gear passes through the transition device. Periodic verification is performed on the selected candidate points, that is, check whether the time interval between adjacent candidate points is consistent with the theoretical impact period , and being within a certain error range (±5%) is regarded as meeting the periodicity. The data points that simultaneously meet the conditions of the amplitude exceeding the threshold and the periodic characteristics are marked as the vibration signals of the transition section.

[0026] Take a section of the signal with a length of 10 seconds and a corresponding signal data volume of 100k from both sides of the vibration signal of the transition section marked in the above steps as samples. Define the data energy mean characterization parameter , and its calculation method is to accumulate each sampling point in the sample signal, multiply by the maximum amplitude in the sample signal, and then divide by the number of sampling points in the sample. Perform quantitative analysis on the two samples through this parameter, and compare the magnitudes of the energy means of the two samples. Since when the rack and pinion train operates in the rack and pinion section, the driving gear meshes with the rack track, and the impact received is significantly stronger than that in the wheel-rail section, the sample with a larger data energy mean is marked as the vibration signal of the rack and pinion section, and the vibration signal of the rack and pinion section is output.

[0027] As Figure 4 shown, perform information processing on the vibration signal of the rack and pinion section: use the short-time Fourier transform method to calculate the time-frequency distribution of the vibration signal of the rack and pinion section . To remove the interference of noise in the time-frequency distribution, a noise reduction algorithm is used for the time-frequency distribution. Divide the time-frequency distribution into multiple time slices. In each time slice, detect local maximum points and local minimum points through a local search algorithm. Taking the detected maximum points as the center and two adjacent minimum points as the boundaries, divide the peak energy divergence region. Utilize the difference in correlation between the signal components and the noise to calculate the correlation degree between the peak energy divergence regions at adjacent times. Specifically, it can be judged by calculating the correlation coefficient, etc. Set a correlation coefficient threshold of 0.7, and regard the region with a correlation degree greater than this threshold as a strongly correlated region. The strongly correlated region is regarded as the effective signal component containing fault characteristics, and the remaining regions are regarded as noise and deleted. Perform iterative processing along the time direction, repeating the above operations of dividing time slices, detecting maximum points, detecting adjacent minimum points, dividing peak energy divergence regions, and screening strongly correlated regions until the time-frequency distribution of the entire vibration signal of the rack and pinion section is processed to obtain the time-frequency distribution with noise removed ; obtain a more accurate instantaneous frequency estimate value through the frequency compensation rearrangement operator; reassign the time-frequency coefficients of the time-frequency distribution , and output the time-frequency distribution of the frequency compensation synchrosqueezing transform .

[0028] As Figure 5 shown, use the time-frequency ridge line extraction algorithm to extract the driving gear meshing frequency ridge line and its sidebands from the time-frequency distribution . According to the number of teeth of the driving gear , convert the meshing frequency to the rotational frequency of the driving gear. Determine whether the interval between the meshing frequency and the sidebands is the rotational frequency or an integer multiple of the rotational frequency. If it conforms to this rule, it is determined that there is a fault in the driving gear, such as tooth surface wear, tooth root fracture and other faults, and the fault diagnosis result is output; if not, it is initially judged that the driving gear is operating normally. At the same time, by analyzing the distribution characteristics of the meshing frequency and its sidebands (such as the number of sidebands, the amplitude change law and the distribution range), the fault type can be accurately discriminated: if the number of sidebands on both sides of the meshing frequency is small but the amplitude fluctuates violently and is concentrated, it is characterized as a tooth surface wear fault; if there are a large number of sidebands with low amplitude, uniform distribution and flatness, it is characterized as a tooth root fracture fault.

[0029] Through the above steps, the fault diagnosis of the driving gear of the rack and pinion train is realized, and potential fault problems of the driving gear can be detected in time to ensure the safe operation of the rack and pinion train.

[0030] Specifically, the fault diagnosis method for the driving gear of the rack and pinion train includes the following steps: S1: Identify the vibration signal of the transition section of the transition device: Identify the periodic high-amplitude transient impact signal generated by the driving gear passing through the transition device from the vibration signal; construct an adaptive amplitude threshold , and screen out the candidate points with amplitudes exceeding this threshold from the vibration signal; based on the drum center distance of the transition device and the train line speed S11 For the vibration signal , set an adaptive amplitude threshold ; the calculation formula of this threshold is: (1); Among them, represents the maximum amplitude in the vibration signal, represents the 0.75 quantile of the vibration signal amplitude; Screen out the high-amplitude transient impacts exceeding the threshold from the vibration signal : (2); Among them, represents the amplitude satisfying the threshold condition, represents the corresponding time, represents that there is no value at this point; S12 Based on the drum center distance of the transition device , calculate the theoretical impact period, and use it to analyze the time series Perform periodicity test, the expression of periodicity at the test time point is: (3); in, Indicates the time value that meets the impact cycle condition, Indicates the center distance between the two rollers in the transition device, represents the linear speed of the train, Indicates that the point has no value; The time point that satisfies the periodicity judgment condition The corresponding data points are marked as transition section vibration signals.

[0031] S2: Identify the rack segment vibration signal: intercept a section of the signal from both sides of the transition segment vibration signal in S1 as a sample, perform quantitative analysis on the sample through the defined data energy mean characterization parameter, compare the energy mean values ​​of the two samples, and mark the sample with the larger data energy mean as the rack segment vibration signal; S21 intercepts a section of the signal on both sides of the transition section vibration signal output by S12 as a sample, and sets them as and ; Next, define a data energy mean characterization parameter: (4); in, Indicates the maximum amplitude in the vibration signal, Represents the number of sample data points intercepted, and this formula is used to quantify the mean data energy; represents the mean energy; S22 compares the energy mean values ​​calculated from the two selected samples, and defines the data with the larger energy mean value as the rack segment vibration signal: Rack segment vibration signal = (5); in, Representation data The calculated mean energy is Representation data Calculate the mean energy. S3: Process the vibration signal of the rack section: Perform time-frequency analysis on the vibration signal of the rack section to obtain the time-frequency distribution. Divide the time-frequency distribution into multiple time slices, detect the local maximum points within each time slice, and divide the peak energy divergence regions centered on the maximum points. Utilize the difference in correlation between the signal components and the noise to calculate the correlation degree between the peak energy divergence regions at adjacent moments, and filter out the strongly correlated regions based on this; the strongly correlated regions are regarded as the effective signal components containing fault characteristics, and iterative processing is performed along the time direction to obtain the time-frequency distribution with noise removed; S31 Divide the peak energy divergence regions: Perform short-time Fourier transform on the vibration signal of the rack section output by S22 to obtain the time-frequency distribution of the vibration signal of the rack section , and perform noise reduction on the time-frequency distribution; Based on Search for the set of local maximum values of the amplitude of this column of data along the frequency axis direction and the set of local minimum values : : (6); (7); Among them, represents the local maximum value, represents the local minimum value, represents at the moment when the frequency value is the amplitude, represents at the moment when the frequency value is the amplitude, is the frequency step size; For each local maximum value , search for its adjacent upper and lower local minimum values and , and form a peak energy divergence region : (8); Then the set of all peak energy divergence regions divided at the moment is defined as: (9); Among them, is the number of local maximum values at the moment; S32 Screen the strongly correlated regions of the peak energy divergence regions: Select at a certain moment in to start the analysis. Assume that all the peak energy divergence regions in Select a peak energy divergence region from , and search for a peak energy divergence region closest to it along the frequency axis in , calculate the Pearson correlation coefficient between the two , and repeat the above operation until all peak energy divergence regions at are calculated once; ; (10); (11); Among them, represents at TFR the average amplitude, represents the result of the Pearson correlation coefficient, represents at the th peak energy divergence region; represents the s th discrete frequency point; represents the number of discrete points in the frequency direction; represents the frequency variable; If the correlation coefficient result is greater than or equal to 0.7, it is determined as a strong correlation degree, and the peak energy divergence region corresponding to is retained; otherwise, this region is considered noise and is directly deleted the peak energy divergence region corresponding to the moment; (12); Among them, represents the set of peak energy divergence regions retained at the moment, represents that there is no value at this point; S33 Remove the noise of the time-frequency distribution: Along the time direction, repeat the method of S32 from to both ends until all peak energy divergence regions at all moments are searched, and output a time-frequency distribution with noise removed .

[0032] S34 Improve the time-frequency concentration of the time-frequency distribution: Propose a frequency compensation rearrangement operator: (13); Among them, represents the derivative with respect to time, represents the instantaneous amplitude, represents the instantaneous phase, represents the instantaneous frequency, represents the window function, represents the imaginary number; Represents the time-frequency distribution after noise removal; According to the frequency compensation rearrangement operator The estimated instantaneous frequency redistributes the divergent time-frequency coefficients along the frequency direction onto the energy trajectory; (14); Wherein, is the time-frequency distribution of the frequency compensation synchrosqueezing transform, represents the Dirac function.

[0033] S4: Implement fault diagnosis according to the meshing frequency and sideband information: Use the time-frequency ridge extraction algorithm to extract the meshing frequency ridge and its sidebands of the driving gear from the time-frequency distribution of the frequency compensation synchrosqueezing transform; According to the parameters of the driving gear, judge whether the interval between the meshing frequency and the sidebands is the rotational frequency to achieve fault diagnosis.

[0034] Through the ridge extraction algorithm, from extract the meshing frequency of the driving gear and the rack and the sidebands, according to the gear parameters, convert to the rotational frequency of the driving gear, judge whether the interval between the meshing frequency and the sidebands is the rotational frequency, and achieve fault diagnosis.

[0035] On the other hand, the present invention provides a fault diagnosis system for the driving gear of a gear-rail train, including a data acquisition module, a gear-rail section data recognition module, a gear-rail section data processing module, and a driving gear fault diagnosis module connected in sequence; the data acquisition module includes a vibration sensor installed near the driving gear of the gear-rail train; used to collect vibration signals; the data acquisition module is responsible for converting the analog signal collected by the sensor into a digital signal and transmitting it to the data processing unit; the data processing unit runs the corresponding algorithm program; wherein, the gear-rail section data recognition module marks the samples with large data energy mean as the gear-rail section vibration signals and outputs the gear-rail section vibration signals; the gear-rail section data processing module processes the information of the gear-rail section vibration signals and outputs the time-frequency distribution of the frequency compensation synchrosqueezing transform ; the driving gear fault diagnosis module uses the time-frequency ridge extraction algorithm to extract the meshing frequency ridge and its sidebands of the driving gear from the time-frequency distribution and further analyzes the type and severity of the fault.

[0036] The present invention also discloses a fault diagnosis device for the driving gear of a gear-rail train, provided with the above-mentioned fault diagnosis system for the driving gear of a gear-rail train, and further includes a display device for displaying the fault diagnosis result, a power supply module; a communication module for transmitting the vibration signal of the vibration sensor; the power supply module supplies power to the communication module, the display device, and the fault diagnosis system.

[0037] Certainly, without departing from the spirit and essence of the present invention, those skilled in the art should be able to make various corresponding changes and modifications according to the present invention, but these corresponding changes and modifications should all fall within the protection scope of the appended claims of the present invention.

Claims

1. A method for diagnosing faults in the drive gears of a rack railway vehicle, characterized in that: The following steps are involved: S1: Identify the vibration signal of the transition section of the transition device: Identify the periodic high-amplitude transient impact signal generated when the driving gear passes through the transition device from the vibration signal; Construct an adaptive amplitude threshold , and screen out the candidate points whose amplitudes exceed this threshold from the vibration signal; Based on the center distance of the drum of the transition device and the train line speed , calculate the theoretical impact period, and verify the periodicity of the candidate points; Mark the data points that simultaneously meet the amplitude threshold condition and the periodic characteristics as the vibration signal of the transition section; S2: Identify the rack segment vibration signal: intercept a section of the signal from both sides of the transition segment vibration signal in S1 as a sample, perform quantitative analysis on the sample through the defined data energy mean characterization parameter, compare the energy mean values ​​of the two samples, and mark the sample with the larger data energy mean as the rack segment vibration signal; S3: Processing the vibration signal of the rack rail segment: performing time-frequency analysis on the vibration signal of the rack rail segment to obtain the time-frequency distribution, dividing the time-frequency distribution into multiple time slices, detecting the local maximum point in each time slice, and dividing the peak energy divergence area with the maximum point as the center; The difference in correlation between signal components and noise is used to calculate the correlation between the peak energy divergence areas at adjacent moments, and the strongly correlated areas are screened out accordingly. The strongly correlated areas are regarded as effective signal components containing fault characteristics, and are iteratively processed along the time direction to obtain the time-frequency distribution after noise removal. S4: Fault diagnosis is achieved based on the meshing frequency and sideband information: the time-frequency ridge extraction algorithm is used to extract the drive gear meshing frequency ridge and its sideband from the frequency-compensated synchronous compression transform time-frequency distribution; based on the drive gear parameters, it is determined whether the interval between the meshing frequency and the sideband is the rotation frequency to achieve fault diagnosis.

2. The method for diagnosing faults of the drive gear of a rack railway vehicle according to claim 1, characterized in that: The step S1 specifically includes the following steps: S11 For the vibration signal , set an adaptive amplitude threshold ; The calculation formula of this threshold is as follows: (1)； Among them, represents the maximum amplitude in the vibration signal, represents the 0.75 quantile of the vibration signal amplitude; From the vibration signal filter out high-amplitude transient shocks above the threshold: (2)； Among them, represents the amplitude that meets the threshold condition, represents the corresponding moment, indicates that there is no value at this point; S12 Center distance of the drum based on the transition device and the train line speed , calculate the theoretical impact period, and based on this, perform a periodic test on the time series . The expression for testing the periodicity of the time point is as follows: (3)； Among them, represents the moment value that satisfies the impact period condition, represents the center distance between two drums in the transition device, represents the train linear velocity, indicates that there is no numerical value at this point; Mark the data points corresponding to the time points that meet the periodic determination condition as the vibration signals of the transition section. ​ 3. The method for diagnosing faults of the drive gear of a rack railway vehicle according to claim 2, wherein: The step S2 specifically includes: S21 intercepts a section of signal on each side of the transition section vibration signal output from S12 as samples, and respectively sets them as and ; Next, define a data energy mean characterization parameter: (4)； Among them, represents the maximum amplitude in the vibration signal, represents the number of sampled data points intercepted, and this formula is used to quantify the mean energy of the data; represents the mean energy; S22 compares the energy mean values ​​calculated from the two selected samples, and defines the data with the larger energy mean value as the rack segment vibration signal: Rack section vibration signal= (5); Among them, represents data the calculated mean energy, represents data the calculated mean energy.

4. The method for diagnosing the failure of the drive gear of a rack railway vehicle according to claim 3, wherein: The step S3 specifically includes: S31 Divide the peak energy divergence region: Perform short-time Fourier transform on the vibration signal of the gear-rail section output by S22 to obtain the time-frequency distribution of the vibration signal of the gear-rail section , and denoise the time-frequency distribution; Based on Search for the set of local maxima of the amplitudes of this column of data along the frequency axis and the set of local minima : (6)； (7)； Among them, represents the local maximum frequency value, represents the local minimum frequency value, represents at the moment when the frequency value is the amplitude, represents at the moment when the frequency value is the amplitude, is the frequency step size; For each local maximum , search for the adjacent local minima and to form a peak energy divergence region : (8)； Then The set of all peak energy divergence regions divided by time is defined as: (9)； Among them, is the number of local maxima at a moment; S32 screening peak energy divergence area with strong correlation: Select at a certain moment start the analysis and assume that all peak energy divergence regions in are signal components; subsequently, in select a peak energy divergence region and in search for a peak energy divergence region that is closest to it along the frequency axis calculate the Pearson correlation coefficient between the two and repeat the above operation until all peak energy divergence regions at the moment of have been calculated once; (10)； (11)； Among them, represents the moment TFR of the amplitude mean value, represents the Pearson correlation coefficient result, represents the th peak energy divergence region at the moment; represents the s th discrete frequency point; represents the number of discrete points in the frequency direction; represents the frequency variable; If the correlation coefficient result is greater than or equal to 0.7, it is determined to be a strong correlation degree and retained the peak energy divergence region corresponding to the moment; otherwise, this region is considered noise and directly deleted the peak energy divergence region corresponding to the moment; (12)； Among them, denotes the set of peak energy divergence regions that are retained at all times, indicates that there is no numerical value at this point; S33 Remove the noise of the time-frequency distribution: Along the time direction, repeat the method of S32 from to both ends until the peak energy divergence regions at all times are searched out, and output a time-frequency distribution with noise removed .

5. The method for diagnosing the failure of the drive gear of a rack railway vehicle according to claim 4, wherein: The step S3 also includes S34 to improve the time-frequency aggregation of the time-frequency distribution: a frequency compensation rearrangement operator is proposed: (13)； Among them, represents the derivative with respect to time, represents the instantaneous amplitude, represents the instantaneous phase, represents the instantaneous frequency, represents the window function, represents the imaginary number; represents the time-frequency distribution after noise removal; Rearranging operator according to frequency compensation The estimated instantaneous frequency redistributes the time-frequency coefficients that diverge along the frequency direction onto the energy trajectory; (14)； Among them, is the time-frequency distribution of the frequency-compensated synchrosqueezing transform, represents the Dirac function.

6. The method for diagnosing faults of the drive gear of a rack railway vehicle according to claim 5, characterized in that: The specific step S4 is as follows: Through the ridge line extraction algorithm, from the meshing frequency of the driving gear and the rack and the sidebands are extracted. According to the gear parameters, it is converted into the rotational frequency of the driving gear, and it is judged whether the interval between the meshing frequency and the sidebands is the rotational frequency to achieve fault diagnosis.

7. A fault diagnosis system for the drive gear of a rack railway train, characterized in that: It includes a data acquisition module, a rack segment data identification module, a rack segment data processing module and a driving gear fault diagnosis module which are connected in sequence; The data acquisition module includes a vibration sensor installed near the driving gear of the rack train; used to collect vibration signals; the data acquisition module is responsible for converting the analog signal collected by the sensor into a digital signal and transmitting it to the data processing unit; the data processing unit runs the corresponding algorithm program; The rack segment data identification module marks samples with large data energy mean as rack segment vibration signals, and outputs the rack segment vibration signals; The toothed rail section data processing module processes the vibration signals of the toothed rail section and outputs the time-frequency distribution of frequency compensation synchrosqueezing transform ; When the driving gear fault diagnosis module is used, the time-frequency ridge line extraction algorithm is used to extract the meshing frequency ridge line of the driving gear and its sidebands from the time-frequency distribution to further analyze the type and severity of the fault.

8. A fault diagnosis device for a rack and pinion train driving gear, which is provided with the fault diagnosis system for a rack and pinion train driving gear according to claim 7, and is characterized in that: It also includes a display device for displaying fault diagnosis results, a power module; a communication module for transmitting the vibration signal of the vibration sensor; the power module supplies power to the communication module, the display device and the fault diagnosis system.

Images