Portable rail fault detection device and fault detection method

Abstract

The invention relates to the technical field of rail fault detection, in particular to a portable rail fault detection device and method, and the device comprises a mounting plate, an ultrasonic guided wave transducer, a signal receiving assembly (comprising a first receiver and a second receiver) and a control unit; the ultrasonic guided wave transducer and the receivers are mounted along the length direction of the mounting plate, and the receivers are symmetrically distributed on two sides of the transducer; the control unit is connected with all the components, controls the transducer to send signals, and receives echo signals through the receiver to determine the fault direction and type. According to the method, the installation and maintenance cost is reduced, fault information is comprehensively and accurately analyzed through echo signal collection, multi-feature extraction and screening, multi-dimensional data fusion and classifier diagnosis, and a reliable basis is provided for railway maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of railway track fault detection, and in particular to a portable railway track fault detection device and a fault detection method. Background Art

[0002] As the core load-bearing component of railway operation, railway tracks with diseases such as cracks, deformations, and fractures pose a serious threat to railway traffic safety. To ensure the stable and efficient operation of the railway transportation system, periodic non-destructive testing of railway tracks to promptly detect and handle potential fault hazards has become an important part of railway safety maintenance work.

[0003] Existing railway track detection technologies are mainly divided into two major systems: mobile detection and fixed detection. Mobile detection uses technical means such as ultrasonic detection, eddy current detection, and magnetic flux leakage detection to conduct regular inspections on railway tracks. These methods are easy to operate and have relatively high detection sensitivity, and can effectively detect defects on the surface and near the surface of railway tracks in specific scenarios. However, their detection distance is limited, and the detection operations can usually only be carried out during the idle periods of railway operation, resulting in relatively low overall detection efficiency. At the same time, due to the limitations of the detection principle and equipment characteristics, there are inevitably detection blind spots during the detection process, and it is easy to miss detections.

[0004] Fixed detection realizes real-time monitoring of the state of railway tracks by installing various sensors and monitoring devices on railway lines. Common fixed detection technologies include stress detection, optical fiber detection, ultrasonic guided wave detection, and track circuit detection. Although stress detection can monitor the stress state of railway tracks, the detection distance is short, and the anti-interference ability is weak in a complex electromagnetic environment; optical fiber detection has the advantages of high precision and high sensitivity, but its installation and maintenance costs are high, and it is difficult to implement in railway areas with harsh environmental conditions and complex terrains; track circuit detection can detect changes in the electrical characteristics of railway tracks in real time and accurately, and has relatively high detection accuracy for some faults, but there are limitations in detecting partial fractures of railway tracks; ultrasonic guided wave detection has significant advantages such as long-distance propagation, full cross-section coverage, and fast detection speed. However, the installation position of the fixed ultrasonic guided wave excitation device is usually limited to the rail bottom or rail waist, so that the ultrasonic guided wave energy is mainly concentrated in the rail bottom and rail waist areas, and the detection effect on the rail head part is not good. Summary of the Invention

[0005] To solve the problems of low efficiency, poor accuracy of manual detection, and difficulty in meeting the requirements of railway development in existing railway track detection technologies, the present invention provides a portable railway track fault detection device and a fault detection method. The portable railway track fault detection device is installed on a railway track and includes: a mounting plate, an ultrasonic guided wave transducer, a received signal component, and a control unit; Among them, the ultrasonic guided wave transducer and the received signal assembly are both installed along the length direction of the mounting plate. The received signal assembly includes a first receiver and a second receiver, and the first receiver and the second receiver are symmetrically arranged on both sides of the ultrasonic guided wave transducer along the length direction of the mounting plate; The control unit is connected to the ultrasonic guided wave transducer, the first receiver and the second receiver, controls the ultrasonic guided wave transducer to send ultrasonic guided wave signals, determines the fault orientation according to the time taken for the first receiver and the second receiver to receive the echo signals passing through the fault point, and judges the fault type according to the echo signals.

[0006] In an embodiment of the present invention, the minimum distance between the first receiver and the second receiver satisfies: , , where V represents the transmission speed of the ultrasonic guided wave signal, represents the total duration of the ultrasonic guided wave signal generated by the ultrasonic guided wave transducer, T represents the period of the ultrasonic guided wave signal generated by the ultrasonic guided wave transducer once, and n represents the total number of times of generating the ultrasonic guided wave signal.

[0007] In an embodiment of the present invention, determining the fault orientation according to the time taken for the first receiver and the second receiver to receive the echo signals passing through the fault point includes: Comparing the time taken for the first receiver and the second receiver to receive the echo signals passing through the fault point, determining the direction where the rail fault point is located, and calculating the distance from the rail fault point to the ultrasonic guided wave transducer as follows: , where, represents the total time for the ultrasonic guided wave to propagate from the transducer to any one of the receivers for the first time, represents the total time for the ultrasonic guided wave to propagate from the transducer to the rail fault point and then to the receiver close to the rail fault point; V represents the transmission speed of the ultrasonic guided wave signal.

[0008] In an embodiment of the present invention, a groove is provided on the mounting plate, the groove is clamped on the rail, and the ultrasonic guided wave transducer contacts the rail head or the rail web.

[0009] In an embodiment of the present invention, the resonance frequency range of the ultrasonic guided wave transducer is 35 kHz to 40 kHz.

[0010] Based on the above fault detection device, the present invention also provides a portable railway track fault detection method, which uses the portable railway track fault detection device for fault detection. The portable railway track fault detection method includes the following steps: S1: Collect the ultrasonic guided wave echo signals generated due to railway track faults. Based on the ultrasonic guided wave echo signals, determine the fault orientation and obtain time-domain features, frequency-spectrum features, and audio features; S2: Screen the time-domain features, the frequency-spectrum features, and the audio features to obtain an echo signal feature data set; S3: Use a variety of statistical functions to perform fusion processing on the echo signal feature data set to obtain multi-dimensional feature data; S4: Use the multi-dimensional feature data as the input of a fault classifier to obtain the fault type of the railway track.

[0011] In an embodiment of the present invention, in S2, the method for obtaining the echo signal feature data set is as follows: S21: Collect an original echo signal data set with labels. Each data sample includes time-domain features, frequency-spectrum features, and audio features, and determine the sample class label, denoted as C classes; S22: For each feature k , calculate the between-class variance and the within-class variance, and use the ratio of the between-class variance and the within-class variance as the importance score of this feature k ; S23: Sort all features according to the importance score to obtain the echo signal feature data set.

[0012] In an embodiment of the present invention, the calculation method of the between-class variance is as follows: Calculate the mean i of the samples of each class k on the feature and the global mean k of all samples on the feature ; According to and , calculate the between-class variance k of the feature on the original echo signal data set : wherein, is the number of samples in class i, and n is the total number of samples in the original echo signal data set.

[0013] In an embodiment of the present invention, the calculation method of the within-class variance is as follows: Calculate the mean i of the samples of each class k on the feature and the value of the k-th feature of sample x ; According to and , calculate the within-class variance of feature k on the original echo signal data set : , wherein is the sample set of class i , and n is the total number of samples in the original echo signal data set.

[0014] In an embodiment of the present invention, the echo signal feature data set includes the mean values of the zero-crossing rate, fundamental frequency, spectral peak centroid, spectral centroid, spectral sharpness, spectral entropy, spectral skewness, spectral peak-side ratio, spectral peak-back ratio, the minimum value of the spectral peak-back ratio, the variance of the spectral peak-back ratio, and Mel cepstral coefficients.

[0015] The present invention also provides an electronic device, which includes a processor, a memory, and a bus system. The processor and the memory are connected through the bus system. The memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the portable rail fault detection method.

[0016] The present invention also provides a computer storage medium, which stores a computer software product. The computer software product includes several instructions for causing a computer device to execute the portable rail fault detection method.

[0017] The above technical solutions of the present invention have the following advantages compared with the prior art: The present invention combines the characteristics of simple operation and high sensitivity of mobile detection and full cross-section coverage and long detection distance of fixed ultrasonic guided wave detection, and makes up for the shortcomings of limited detection distance and low efficiency of mobile detection and inability to accurately locate faults and high cost of fixed ultrasonic guided wave detection. The provided device can effectively detect rail head faults through the ultrasonic guided wave excitation device installed on the rail head; use the echo signal to achieve accurate fault location; introduce a deep learning network to process the ultrasonic guided wave signal data in real time, not only with fast detection speed, but also can accurately identify faults such as micro-cracks, reduce human errors, and achieve accurate discrimination of fault types, greatly improving the accuracy and reliability of fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to make the content of the present invention easier to be clearly understood, the following further details the present invention according to the specific embodiments of the present invention in combination with the drawings, wherein Figure 1It is a schematic structural diagram of a portable railway track fault detection device provided in an embodiment of the present invention; Figure 2 It is a schematic connection diagram of the control unit and the signal transmitting and receiving device; Figure 3 It is a schematic diagram of signal propagation when the portable railway track fault detection device provided by the present invention is in use, where (a) represents the signal propagation schematic diagram when no fault occurs, and (b) represents the signal propagation schematic diagram when a fault occurs; Figure 4 It is a schematic diagram of the process of forming and propagating ultrasonic guided waves in a flat plate; Figure 5 It is the acoustic wave attenuation curve in the railway track under different frequency ranges; Figure 6 It is a schematic flowchart of a portable railway track fault detection method provided in an embodiment of the present invention; Figure 7 It is a flowchart of the implementation of an echo signal feature extraction strategy provided in an embodiment of the present invention; Figure 8 It is a schematic flowchart of the training and detection process of the railway track fault detection model provided in an embodiment of the present invention; Explanation of the reference numerals in the specification drawings: 1. Mounting plate; 11. Groove; 2. Ultrasonic guided wave transducer; 3. Received signal assembly; 31. First receiver; 32. Second receiver; 4. Control unit; 5. Rail head; 6. Rail web. Detailed implementation manners

[0019] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and be able to implement it, but the embodiments cited do not limit the present invention.

[0020] Embodiment 1:

[0021] Referring to Figure 1 and Figure 2 As shown, the present invention also provides a portable railway track fault detection device, which is installed on the railway track and includes: a mounting plate 1, an ultrasonic guided wave transducer 2, a received signal assembly 3, and a control unit 4; Wherein, the ultrasonic guided wave transducer 2 and the received signal assembly 3 are both installed along the length direction of the mounting plate 1, the received signal assembly 3 includes a first receiver 31 and a second receiver 32, and the first receiver 31 and the second receiver 32 are symmetrically arranged on both sides of the ultrasonic guided wave transducer 2 along the length direction of the mounting plate 1 respectively; The control unit 4 is connected to the ultrasonic guided wave transducer 2, the first receiver 31, and the second receiver 32, controls the ultrasonic guided wave transducer 2 to send ultrasonic guided wave signals, determines the fault orientation according to the time taken for the first receiver 31 and the second receiver 32 to receive the echo signals passing through the fault point, and determines the fault type according to the echo signals.

[0022] As Figure 3 shown in (a) therein, when there is no fault in the railway track, after the ultrasonic guided wave transducer 2 generates an excitation, no echo signal of the ultrasonic guided wave can be generated, and both the first receiver 31 and the second receiver 32 can only receive a guided wave signal once and the time taken for the two receivers to receive the echo signals is approximately equal. As Figure 3 shown in (b) therein, when faults such as cracks or fractures occur in the railway track, echo signals will be generated. The first receiver 31 and the second receiver 32 can receive two guided wave signals. One is the echo signal directly received from the ultrasonic guided wave transducer 2, and the other is the echo signal received after the ultrasonic guided wave transducer 2 emits and passes through the fault point.

[0023] Obviously, the present invention only needs to simply determine whether the receiver receives an echo signal to judge whether there is a fault in the railway track. Since the broken rail detector has two receivers, in order to make the time for receiving signals have a significant difference, taking the generation of five cycles (i.e., n = 5) of ultrasonic guided wave signals by the ultrasonic guided wave signal as an example, the minimum distance between the first receiver 31 and the second receiver 32 satisfies: , , where V represents the transmission speed of the ultrasonic guided wave signal, represents the total time for the ultrasonic guided wave transducer to generate ultrasonic guided wave signals, T represents the period of the ultrasonic guided wave transducer to generate one ultrasonic guided wave signal, and n represents the total number of times of generating ultrasonic guided wave signals.

[0024] When cracks or fractures occur on one side of the railway track, determining the fault orientation according to the time taken for the first receiver 31 and the second receiver 32 to receive the echo signals passing through the fault point includes: Judging and comparing the time taken for the first receiver 31 and the second receiver 32 to receive the echo signals passing through the fault point, and determining the direction where the fault point of the railway track is located: Determining the time taken for the ultrasonic guided wave to propagate from the transducer to the fault point and then to the first receiver 31 and the time taken for the ultrasonic guided wave to propagate from the transducer to the fault point and then to the second receiver 32 by means of peak detection. If , the fault point is close to the end of the first receiver 31; otherwise, the fault point is close to the end of the second receiver 32.

[0025] Meanwhile, calculate the distance d from the rail fault point to the ultrasonic guided wave transducer 2 as follows: , wherein, represents the total time for the ultrasonic guided wave to propagate from the transducer to any receiver for the first time, represents the total time for the ultrasonic guided wave to propagate from the transducer to the rail fault point and then to the receiver close to the rail fault point, , when n = 1, it indicates that the fault point is close to one end of the first receiver 31. At this time, represents the total time for the ultrasonic guided wave to propagate from the transducer to the fault point and then to the first receiver 31; when n = 2, it indicates that the fault point is close to one end of the second receiver 32. At this time, represents the total time for the ultrasonic guided wave to propagate from the transducer to the fault point and then to the second receiver 32; V represents the propagation speed of the ultrasonic guided wave signal.

[0026] The ultrasonic guided wave is essentially a mechanical wave, which is a special ultrasonic wave generated after the interaction between ultrasonic waves and the waveguide medium boundary. As Figure 4 shown, when ultrasonic waves propagate in elastic waveguide media such as cylindrical shells, rods, and layers, they will continuously reflect and refract with the medium boundary, and at the same time, the conversion between transverse waves and longitudinal waves also occurs continuously, and finally the ultrasonic guided wave is formed.

[0027] In this embodiment, an air compression impact device with adjustable frequency is adopted to conduct an attenuation experiment on the guided wave in the rail on the actual railway line. Three frequency ranges are set in this experiment, which are 0 kHz to 20 kHz, 20 kHz to 40 kHz, and 40 kHz to 60 kHz respectively. The attenuation curve of the ultrasonic guided wave in the rail is obtained through the experiment, specifically as Figure 5 shown.

[0028] It can be seen from Figure 5 that there are differences in the transmission distances of guided waves in different frequency ranges in the rail. Among them, when the frequency range is 20 kHz to 40 kHz, its attenuation curve is relatively flat. This means that under the condition of the same initial energy, the attenuation speed of the ultrasonic guided wave in this frequency range is slower. In the actual application scenario of rail fault detection, the vibration intensity of the ultrasonic guided wave generated by the ultrasonic guided wave transducer is relatively weak. Based on this, it is more appropriate to select the ultrasonic guided wave signal in the frequency range of 20 kHz to 40 kHz.

[0029] In addition, there is a certain correlation with its size. The lower the frequency, the larger the size of the ultrasonic guided wave transducer 2. For example, for the ultrasonic guided wave transducer 2 with a resonance frequency of 25 kHz, its outer diameter reaches 60 mm; while for the ultrasonic guided wave transducer 2 with a resonance frequency of 35 kHz, the outer diameter is 48 mm.

[0030] During the process of exciting ultrasonic guided waves, in order to achieve efficient energy utilization, the energy should be concentrated in the rail head as much as possible. Since there are rail fasteners at the rail bottom, this will limit the propagation of vibration at the rail bottom, resulting in too fast vibration attenuation, and then causing the detection distance to be shortened sharply, which is not conducive to the development of detection work. Therefore, in this embodiment, the mounting plate 1 is provided with a groove 11, the groove 11 is clamped on the rail, and the ultrasonic guided wave transducer 2 can usually only be installed at the positions of the rail head 5 and the rail web 6.

[0031] Considering this installation limitation and the requirements of energy propagation, the size of the ultrasonic guided wave transducer 2 should not be too large. After considering various factors, the resonance frequency range of the ultrasonic guided wave transducer 2 is finally determined to be 35 kHz to 40 kHz.

[0032] In actual detection, the amount of echo signal data obtained from the first receiver 31 or the second receiver 32 is huge. If these data are directly used for classification estimation of fault types, not only is there a great difficulty, but also it is difficult to ensure the accuracy rate. To solve this problem, the control unit 4 performs dimensionality reduction processing and feature extraction operations on the collected echo signal data according to the rail fault detection method described in Embodiment 1. Through these processes, the data that can effectively characterize the fault features are screened out, and then these features are input into the classifier, so as to achieve accurate classification of various fault types such as rail cracks, fractures, and deformations.

[0033] Embodiment 2: Since the echo signals generated by the actually collected rail faults usually have a large amount of data, it is very difficult to directly use these data for classification estimation of fault types, and the accuracy rate is also difficult to guarantee. In order to more effectively and accurately classify the rail fault types, it is necessary to perform dimensionality reduction on these original echo signal data, find a set of features that can best represent these data, and then input these features into the classifier for classification estimation.

[0034] Therefore, as shown in Figure 6 and Figure 7 shown, the present invention provides a portable rail fault detection method, which uses the portable rail fault detection device described in Embodiment 1 for fault detection. The method includes the following steps: S1: Turn on the ultrasonic guided wave transducer 2 through the control unit 4, so that the ultrasonic guided wave transducer 2 sends ultrasonic guided wave signals along both ends of the rail, collect the ultrasonic guided wave echo signal generated by the rail fault through the signal receiving component 3, calculate based on the ultrasonic guided wave echo signal, determine the fault location and obtain the time domain characteristics, spectrum characteristics and audio characteristics; S2: Using Fisher algorithm to screen the time domain features, the spectrum features and the audio features to obtain an echo signal feature data set; S3: using a variety of statistical functions to fuse the echo signal feature data set to obtain multi-dimensional feature data; S4: Using the multi-dimensional feature data as input of a fault classifier to obtain the fault type of the rail.

[0035] When using ultrasonic guided waves to detect rail faults, feature extraction of echo signals is a key step. The key feature parameters of echo signals mainly involve echo amplitude, phase change, frequency attenuation, and waveform distortion. These parameters are of great significance for comprehensively reflecting the degree and nature of defects in the track. In addition to the above basic time domain and frequency domain characteristics, in-depth analysis of the modal conversion and scattering phenomena during the propagation of ultrasonic guided waves is also indispensable. Ultrasonic guided waves of different modes will convert each other during the propagation process, and scattering will also occur when encountering track defects. Research and analysis of these phenomena will help to locate track defects more accurately and deeply understand the essential characteristics of defects. Given the diversity of track materials, the complexity of geometric shapes, and the variability of external environmental factors, a single feature extraction method is often difficult to fully and accurately describe the complex characteristics of echo signals.

[0036] Therefore, in step S1, it is necessary to collect the ultrasonic guided wave echo signal generated by the rail fault, and perform relevant calculations based on the echo signal to determine the fault location, and at the same time obtain time domain features, spectrum features and audio features.

[0037] Furthermore, since a dedicated feature set usually has a high feature dimension, there are many possible combinations of its feature subsets. If classifier training is performed for each feature subset, the computational overhead of the algorithm will increase significantly, the computational complexity will increase significantly, and it will be difficult to implement in practical applications. To solve this problem, in step S2, a feature filtering algorithm is used, such as the Fisher scoring algorithm, to screen the acquired time domain features, spectral features, and audio features to construct an echo signal feature data set. The specific method is as follows: S21: Collect a labeled original echo signal data set, each data sample contains time domain features, spectrum features and audio features, determine the sample category label, and record it as C categories; S22: For each feature k, calculate the between-class variance and the within-class variance, and use the ratio of the between-class variance to the within-class variance as the importance score of feature k; wherein, the method for calculating the between-class variance is as follows: Calculate the mean value i of the samples of each category k on feature and the global mean value k of all samples on feature ; According to and , calculate the between-class variance k of feature on the original echo signal dataset , wherein, is the number of samples in category i, and n is the total number of samples in the original echo signal dataset;

[0038] The method for calculating the within-class variance is as follows: Calculate the mean value i of the samples of each category k on feature and the value of sample x on the k-th feature; According to and , calculate the within-class variance k of feature on the original echo signal dataset , wherein, is the sample set of category i , and n is the total number of samples in the original echo signal dataset; S23: Sort all features according to the importance scores to obtain an echo signal feature dataset.

[0039] Furthermore, the echo signal feature dataset covers features such as zero crossing rate (ZCR), fundamental frequency (F0), spectral peak centroid, spectral centroid, spectral sharpness, spectral entropy, spectral skewness, spectral peak side ratio, mean value of spectral peak back ratio, minimum value of spectral peak back ratio, variance of spectral peak back ratio, and Mel cepstral coefficients.

[0040] Among them, the zero-crossing rate represents the number of times the signal amplitude crosses the zero value within one frame. As the fault distance becomes farther, the amplitude of the received transmitted signal decreases, and the environmental noise inside the railway track gradually dominates, resulting in an increase in the zero-crossing rate. Therefore, the zero-crossing rate ZCR can not only be used to characterize the time-domain characteristics of the reflected signal, but also reflect to a certain extent the distance where the railway track fault occurs. Its calculation method is as follows: , where, , N is the number of sampling points in each frame, is the amplitude of the q-th sampling point.

[0041] The fundamental frequency F0 represents the lowest frequency component of the periodic vibration in the signal. It is the reciprocal of the time required for the signal to repeat a complete cycle and is defined as: , where T is the periodic time interval of the signal. The fundamental frequency reflects the main vibration characteristics of the signal and is the frequency component with the most concentrated energy in the complex signal. In the railway track vibration signal, the fundamental frequency corresponds to the natural vibration frequency of the railway track after being impacted. When there are faults (such as cracks, looseness) in the railway track, its vibration mode will change. When the fault is close to the detection point: the vibration energy is concentrated, the amplitude of the fundamental frequency component is significant and the frequency is stable. When the fault is far from the detection point: the signal attenuation causes the high-frequency components to weaken, the fundamental frequency may shift or split due to the superposition of reflected waves, and at the same time the noise interference increases.

[0042] The spectral peak centroid can reflect the distance at which the receiver locates the railway track fault by performing peak detection on the reflected signal. When no railway track fault is detected, that is, when the signal line spectrum does not exist, the frequency with a large amplitude is generated by the environmental noise inside the railway track and is mainly concentrated in the low-frequency band; when a railway track fault is detected, that is, when the signal line spectrum exists, the frequency amplitude of the environmental noise inside the railway track is significantly lower than the amplitude of the line spectrum, and corresponding line spectra also appear in the high-frequency band. The method for obtaining the spectral peak centroid is as follows: Obtain N maximum spectral peaks in the spectrum of the ultrasonic guided wave echo signal, and define the frequency of the spectral peak as , and the corresponding spectral amplitude is , ; in this embodiment, N = 5; Based on and , calculate the spectral peak centroid C: .

[0043] Furthermore, the spectral centroid represents the center of the signal frequency components and is one of the important physical parameters describing the timbre attribute. It is the frequency weighted and averaged by energy within a certain frequency range, and its calculation method is as follows: , where, f is the frequency of the signal, EIt is the spectral energy corresponding to the frequency after performing the short-time Fourier transform. As the distance of the railway track fault location increases, the amplitude of the low-frequency component of the reflected signal rapidly decreases, while the higher-frequency part is mainly noise component and the amplitude change is small, resulting in the spectral centroid moving towards the high frequency.

[0044] Furthermore, the spectral sharpness represents the ratio of the mid-high frequency energy to the total energy in the signal, describes a feeling related to the frequency components of the sound, reflects the uncomfortable feeling of the sound, but has nothing to do with the loudness of the sound. Its calculation method is: , where is the energy at frequency f, is the sampling frequency. It can be seen from this that as the fault distance gradually increases, the rapid attenuation of the low-frequency component causes the spectral sharpness to gradually increase.

[0045] Furthermore, the spectral entropy represents the order degree of the signal spectrum, and its calculation method is: , where x represents an event within a certain range of the spectral amplitude, is the probability of event x. The distance between the maximum and minimum values of the spectral amplitude is evenly divided into 100 segments, and each segment is regarded as an event. By calculating the probability of each event, the spectral entropy can be obtained. When the distance increases, the spectral entropy increases, which means the order degree of the spectrum decreases and the spectrum gradually becomes disordered. This is mainly because when the target distance is far, the signal-to-noise ratio of the received signal is low and the signal is greatly affected by noise.

[0046] Furthermore, the spectral skewness can measure the skewness direction and degree of the spectrum, and is a numerical characteristic of the asymmetry degree of the spectrum distribution. Its calculation method is: , where and represent the second-order central moment and the third-order central moment respectively, X is the amplitude of the spectrum, and are the mean and variance of X respectively. Generally speaking, when the spectrum is right-skewed, the spectral skewness is greater than 0, and the larger the skewness value, the greater the right-skewness degree. The spectrum of the railway track fault reflected signal can be regarded as a right-skewed distribution. When the distance increases, the amplitude of the low-frequency component rapidly attenuates, which is equivalent to a decrease in the right-skewness degree. Therefore, the spectral skewness value at a farther distance is smaller than that at a closer distance.

[0047] Furthermore, the spectral peak-to-side ratio It represents the underlying features describing a single spectral peak in the local spectrum, used to describe the details of a single spectral peak in the local spectrum. Since the line spectrum corresponding to the reflected signal is high and narrow, by comparing the peak value of the single spectral peak in the local spectrum with the amplitude corresponding to a position that is raised by a fixed frequency from the spectral peak position, the ratio will be significantly higher than the ratio without a line spectrum. Therefore, this ratio is defined as the peak-side ratio, and the formula is as follows: , where \(i\) represents the serial number of the largest spectral peak in the spectrum of the obtained ultrasonic guided wave echo signal, represents the total number of the largest spectral peaks, represents the fixed frequency used to raise the spectral peak position. When a line spectrum exists, the five largest spectral peaks extracted from the spectrum are narrow peaks. Therefore, the peak-side ratios of these 5 spectral peaks are all larger values and are more stable; when a line spectrum does not exist, the widths of the five largest spectral peaks vary and are basically wide peaks, so the peak-side ratios are also smaller values and are all quite different. To fully characterize the spectral characteristics of the reflected signal, the present invention uses the mean value of the 5 peak-side ratios as the underlying characteristic for characterizing the spectral characteristics.

[0048] Furthermore, since the line spectrum corresponding to the reflected signal is few and narrow, the line spectrum has little effect on the mean value of the entire spectrum amplitude. However, compared with the mean value of the background amplitude of the entire spectrum, the amplitude of the line spectrum is significantly increased, and the ratio of the maximum amplitude of the spectrum with a line spectrum to the average amplitude of the spectrum will be significantly higher than the ratio without a line spectrum. Therefore, this ratio is defined as the underlying characteristic for characterizing the spectral characteristics, called the peak-back ratio, and its calculation method is as follows: Obtain \(N\) largest spectral peaks in the spectrum of the ultrasonic guided wave echo signal, and the spectral amplitude of the spectral peak is , ; According to all , calculate its spectral average amplitude , according to the maximum spectral amplitude and the spectral average amplitude , calculate the spectral peak-back ratio \(R\) of each spectral peak: .

[0049] Mel-frequency cepstral coefficients (MFCCs) are used to describe the distribution of audio signal energy in different frequency ranges and are widely used in the field of speech recognition. Past research has shown that they are also effective in flaw detection. The present invention deeply analyzes the Mel-frequency cepstral coefficients. After calculating the first 16 coefficients, it is found that under the condition of the same fault distance, there are significant differences in the 1st to 14th Mel-frequency cepstral coefficients (MFCC 1 - 14) corresponding to different fault types. Therefore, these 1st to 14th Mel-frequency cepstral coefficients (MFCC1 - 14) can be used as key indicators to reflect the spectral characteristics of the reflected signal, so as to distinguish different fault types.

[0050] In summary, the present invention finally selects the zero-crossing rate (ZCR), fundamental frequency (F0), spectral peak centroid, spectral centroid, spectral sharpness, spectral entropy, spectral skewness, spectral peak side ratio, spectral peak back ratio (mean, minimum value, variance), and 1 to 14 coefficients of Mel-frequency cepstral coefficients (MFCC) (MFCC1-14), a total of 25 underlying features to characterize the spectral characteristics of the echo signal.

[0051] In addition, in step S3, the present invention uses 10 statistical functions listed in Table 1 to conduct a comprehensive statistical description of the above-mentioned 25 underlying features. Actually, 13 parameters are returned. These 25 underlying features screened by the Fisher score algorithm, after being processed by 10 statistical functions respectively, finally generate 13×25 = 325-dimensional feature data that can fully characterize different rail faults through feature extraction and fusion. These feature data will be used as the input data for the deep learning algorithm for rail fault detection.

[0052] Table 1

[0053] In step S4, a classifier is used to accurately classify the possible fault types of the rail, such as cracks, fractures, and deformations. The available classifier types are diverse, including neural networks, support vector machines, random forests, naive Bayes classifiers, etc.

[0054] Taking the deep learning model as an example, it plays an important classification role in rail fault detection. When the deep learning model is used as a classifier, it has a set of rigorous training and detection processes. The specific process can refer to Figure 8 .

[0055] In the training stage, a large number of labeled sample data containing different fault types are used to continuously adjust the parameters of the model through algorithms such as backpropagation, so that the model learns the feature patterns corresponding to different fault types. In the detection stage, the 325-dimensional feature data obtained by feature extraction and fusion of the echo signal to be detected is input into the trained deep learning model. The model analyzes the data based on the learned knowledge and outputs the corresponding fault type judgment result, thereby realizing the efficient and accurate identification of rail fault types.

[0056] Example 3:

[0057] The present invention also provides an electronic device, which includes a processor, a memory, and a bus system. The processor and the memory are connected through the bus system. The memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the portable rail fault detection method described in Embodiment 2.

[0058] Embodiment 4:

[0059] The present invention also provides a computer storage medium, which stores a computer software product. The computer software product includes several instructions for causing a computer device to execute the portable rail fault detection method described in Embodiment 2.

[0060] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0061] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0062] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0063] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby the instructions executed on the computer or other programmable apparatus provide steps for realizing the functions specified in one process or a plurality of processes and / or boxes. Figure 1 One process or a plurality of processes and / or boxes Figure 1 Steps for realizing the functions specified in one box or a plurality of boxes.

[0064] Obviously, the above-described embodiments are merely examples given for clear illustration and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation manners here. And the obvious changes or modifications derived therefrom still fall within the protection scope of the present invention.

Claims

1. A portable rail fault detection device, characterized in that: The portable rail fault detection device is installed on the rail, and comprises: a mounting plate, an ultrasonic guided wave transducer, a signal receiving component and a control unit; Wherein, the ultrasonic guided wave transducer and the receiving signal component are both installed along the length direction of the mounting plate, and the receiving signal component includes a first receiver and a second receiver, and the first receiver and the second receiver are symmetrically arranged on both sides of the ultrasonic guided wave transducer along the length direction of the mounting plate; The control unit is connected to the ultrasonic guided wave transducer, the first receiver and the second receiver, controls the ultrasonic guided wave transducer to send an ultrasonic guided wave signal, determines the fault location according to the time taken by the first receiver and the second receiver to receive the echo signal passing through the fault point, and determines the fault type according to the echo signal.

2. The portable rail fault detection device according to claim 1, characterized in that: The minimum distance between the first receiver and the second receiver satisfy: , , where V represents the transmission speed of ultrasonic guided wave signal, represents the total duration of the ultrasonic guided wave transducer generating an ultrasonic guided wave signal, T represents the period of the ultrasonic guided wave transducer generating an ultrasonic guided wave signal, and n represents the total number of times the ultrasonic guided wave signal is generated.

3. The portable rail fault detection device according to claim 1, characterized in that: Determining the fault location according to the time taken by the first receiver and the second receiver to receive the echo signal passing through the fault point includes: Compare the time taken by the first receiver and the second receiver to receive the echo signal passing through the fault point, determine the direction of the rail fault point, and calculate the distance from the rail fault point to the ultrasonic guided wave transducer as follows: ,in, It represents the total time for the ultrasonic guided wave to propagate from the transducer to any receiver for the first time. It represents the total time for the ultrasonic guided wave to propagate from the transducer to the rail fault point and then to the receiver close to the rail fault point; V represents the transmission speed of the ultrasonic guided wave signal.

4. The portable rail fault detection device according to claim 1, characterized in that: The mounting plate is provided with a groove, the groove is clamped on the rail, and the ultrasonic guided wave transducer contacts the rail head or rail waist of the rail.

5. The portable rail fault detection device according to claim 1, characterized in that: The resonant frequency range of the ultrasonic guided wave transducer is 35kHz~40kHz.

6. A portable rail fault detection method, characterized in that: Fault detection is performed using the portable rail fault detection device according to any one of claims 1 to 5, and the portable rail fault detection method comprises the following steps: S1: collecting ultrasonic guided wave echo signals generated by rail faults, determining fault locations based on the ultrasonic guided wave echo signals, and obtaining time domain features, spectrum features, and audio features; S2: Screening the time domain features, the spectrum features and the audio features to obtain an echo signal feature data set; S3: using a variety of statistical functions to fuse the echo signal feature data set to obtain multi-dimensional feature data; S4: Using the multi-dimensional feature data as input of a fault classifier to obtain the fault type of the rail.

7. The portable rail fault detection method according to claim 6, characterized in that: In S2, the method for obtaining the echo signal feature data set is as follows: S21: Collect a labeled original echo signal data set, each data sample contains time domain features, spectrum features and audio features, determine the sample category label, and record it as C categories; S22: For each feature k , calculate the between-class variance and the within-class variance, and take the ratio of the between-class variance to the within-class variance as the feature k Importance score; S23: Sort all features according to the importance scores to obtain an echo signal feature data set.

8. The portable rail fault detection method according to claim 7, characterized in that: The calculation method of the between-class variance is as follows: Calculate each category i The sample has the following features: k The mean on And all samples in the feature k The global mean on ; according to and , calculate the features k The inter-class variance on the original echo signal dataset : , in, is the number of samples of category i, and n is the total number of samples in the original echo signal dataset.

9. The portable rail fault detection method according to claim 7, characterized in that: The calculation method of the intra-class variance is as follows: Calculate each category i The sample has the following features: k The mean on and the value of sample x on the kth feature ; according to and , calculate the features k The intra-class variance on the original echo signal dataset : , in, For Category i The sample set is n, and n is the total number of samples in the original echo signal data set.

10. The portable rail fault detection method according to claim 6, characterized in that: The echo signal feature data set includes zero-crossing rate, fundamental frequency, spectrum peak center of gravity, spectrum centroid, spectrum sharpness, spectrum entropy, spectrum skewness, spectrum peak-to-side ratio, mean of spectrum peak-to-background ratio, minimum of spectrum peak-to-background ratio, variance of spectrum peak-to-background ratio and Mel-cephalogram coefficient.

11. An electronic device, characterized in that: The electronic device includes a processor, a memory and a bus system, the processor and the memory are connected through the bus system, the memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the portable rail fault detection method according to any one of claims 6 to 10.

12. A computer storage medium, characterized in that: The computer storage medium stores a computer software product, and the computer software product includes a number of instructions for enabling a computer device to execute the portable rail fault detection method according to any one of claims 6 to 10.

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