CN113447570A - Ballastless track disease detection method and system based on vehicle-mounted acoustic sensing - Google Patents
Ballastless track disease detection method and system based on vehicle-mounted acoustic sensing Download PDFInfo
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Abstract
The invention relates to a ballastless track disease detection method based on vehicle-mounted acoustic sensing, which specifically comprises the following steps: s1, collecting original sound signals in the running process of the train; s2, enhancing the sound radiation signal of the ballastless track structure according to the original sound signal; s3, identifying corresponding track diseases according to the enhanced sound radiation signals of the ballastless track structure to obtain a disease identification result of the ballastless track; and S4, judging the line section needing to be maintained according to the disease identification result of the ballastless track. Compared with the prior art, the method has the advantages of saving labor, equipment and time cost, improving the detection efficiency of the ballastless track and the like.
Description
Technical Field
The invention relates to the field of track traffic infrastructure detection, in particular to a ballastless track disease detection method and system based on vehicle-mounted acoustic sensing.
Background
Due to the advantages in the aspects of operation, maintenance and the like, the ballastless track is widely applied to high-speed railway lines. However, as the transportation density of the high-speed railway is gradually increased, the operation speed is continuously increased, and the load grade is gradually increased, the power interaction between the high-speed train and the line infrastructure is more and more severe. Under the condition, diseases such as mortar layer crack separation, track plate emptying, track plate upwarping, track plate and base plate cracking and the like often occur on the ballastless track. These diseases affect the running stability of the vehicle, and even threaten the driving safety when the diseases are serious.
Common non-destructive inspection methods such as ground penetrating radar, ultrasonic techniques, infrared thermography, etc. have limited detection range and usually require external excitation using special equipment. Therefore, the detection can only be carried out in the skylight period of train operation, and the detection efficiency is low. In addition, concrete placement quality and rebar placement can also affect the accuracy of such detection methods.
In order to improve the detection efficiency, researchers use a vehicle-mounted acceleration sensor to detect the structural state of the ballastless track. Although the method based on the vehicle-mounted vibration sensor is convenient to measure, the sensitivity to the structural damage under the rail is insufficient. In recent years, vehicle-mounted laser optical sensing systems and vehicle-mounted computer vision systems have become emerging methods for rail state detection. However, both methods require expensive equipment and the detection effect is easily affected by ambient light and the degree of cleanliness of the object surface.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a ballastless track disease detection method and system based on vehicle-mounted acoustic sensing, which can detect ballastless tracks in real time in the running process of a train, and have high detection efficiency and low cost.
The purpose of the invention can be realized by the following technical scheme:
a ballastless track disease detection method based on vehicle-mounted acoustic sensing specifically comprises the following steps:
s1, collecting original sound signals in the running process of the train;
s2, enhancing the sound radiation signal of the ballastless track structure according to the original sound signal;
s3, identifying corresponding track diseases according to the enhanced sound radiation signals of the ballastless track structure to obtain a disease identification result of the ballastless track;
and S4, judging the line section needing to be maintained according to the disease identification result of the ballastless track.
In the step S2, the sound radiation signal of the ballastless track structure is enhanced by a sound enhancement method based on deep learning.
Further, the deep learning based sound enhancement method comprises a neural network model training phase and a neural network model application phase.
Further, the specific process of the neural network model training phase comprises the following steps:
s201, collecting interference noise in a train operation environment, forming mixed sound together with a ballastless track structure sound radiation signal generated by numerical simulation, and performing short-time Fourier transform on the ballastless track structure sound radiation signal and the mixed sound respectively to obtain a corresponding time-frequency signal;
s202, extracting a time-frequency mask of the sound radiation signal of the ballastless track structure and time-frequency characteristics of mixed sound from the time-frequency signal, wherein the time-frequency mask of the sound radiation signal of the ballastless track structure is used as a training target, and the time-frequency characteristics of the mixed sound are used as input data and input into a recurrent neural network model for training.
Further, the specific process of the neural network model application phase comprises the following steps:
s203, obtaining a corresponding time-frequency signal from the original sound signal through short-time Fourier transform (STFT);
s204, extracting acoustic characteristics from the time-frequency signal of the original sound signal and inputting the acoustic characteristics into the trained recurrent neural network model to obtain a time-frequency mask of the sound radiation signal of the ballastless track structure;
s205, multiplying the time-frequency mask of the ballastless track structure acoustic radiation signal by the acoustic characteristics of the original sound signal, and then performing short-time inverse Fourier transform (iSTFT) to obtain the enhanced ballastless track structure acoustic radiation signal.
In the step S3, the track diseases are identified by a supervised machine learning algorithm, which specifically includes the following steps:
s301, generating an acoustic radiation signal under a typical defect state of a ballastless track structure through numerical simulation, extracting characteristic information of the acoustic radiation signal, processing the characteristic information through a principal component analysis method, and associating the processed characteristic information with the typical defect state of the ballastless track structure to generate a defect dictionary;
s302, taking the processed characteristic information as input data of a training Support Vector Machine (SVM) model, and performing supervised classification training and verification on the SVM model according to a disease dictionary;
and S303, performing feature extraction on the ballastless track structure acoustic radiation signal enhanced in the step S205, inputting the extracted feature into a trained SVM model, classifying the enhanced acoustic radiation signal by the SVM model to obtain a signal classification result, and identifying a corresponding disease type and a disease degree according to the signal classification result to serve as a disease identification result.
Further, the processing types performed on the feature information by the principal component analysis method in step S301 include a decorrelation processing operation and a redundancy removal processing operation.
Further, the typical damage state types of the ballastless track structure comprise track slab emptying, track slab arching, track slab cracking and base plate cracking.
Further, the feature information includes a time domain feature, a frequency domain feature, and a time-frequency domain feature.
The process of judging the line section needing to be maintained in the step S4 includes disease recording, position recording and threshold judgment.
A system using the ballastless track disease detection method based on vehicle-mounted acoustic sensing comprises the following steps:
the acoustic sensor is used for collecting an original sound signal in the running process of the train;
the signal enhancement unit is used for enhancing the sound radiation signal of the ballastless track structure according to the original sound signal;
the disease identification unit is used for identifying the corresponding track disease according to the enhanced acoustic radiation signal to obtain a disease identification result of the ballastless track;
and the maintenance guiding unit is used for judging the line section needing to be maintained according to the disease identification result of the ballastless track.
The acoustic sensor is arranged at the bottom of the train bogie.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention can realize real-time detection in the running process of the train, does not need to lay sensors on a large scale along the railway, reduces the risk of instrument and equipment intruding into the clearance of the railway system, saves labor and time cost, and effectively improves the detection efficiency of the ballastless track.
2. The hardware equipment required by the invention is only the acoustic sensor, the data transmission equipment, the data analysis equipment and the positioning equipment, and the cost is low.
3. The method can judge the line section needing to be maintained, provides more exact guidance for repairing the structural damage of the ballastless track, and accurately judges the road section needing to be repaired.
4. The ballastless track disease detection method is free from light interference and time limitation, and can be widely applied to disease detection of various ballastless track infrastructures of high-speed railways, urban rail transit and the like.
Drawings
FIG. 1 is a schematic flow chart of the detection method of the present invention;
FIG. 2 is a schematic diagram of the system of the present invention;
fig. 3 is a schematic flow chart of the enhanced acoustic radiation signal based on deep learning according to the present invention.
Reference numerals:
1-an acoustic sensor; 2-a signal enhancement unit; 3-a disease identification unit; 4-maintenance guide unit.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
As shown in fig. 1, a ballastless track disease detection method based on vehicle-mounted acoustic sensing specifically includes the following steps:
s1, collecting original sound signals in the running process of the train;
s2, enhancing the sound radiation signal of the ballastless track structure according to the original sound signal;
s3, identifying corresponding track diseases according to the enhanced sound radiation signals of the ballastless track structure to obtain a disease identification result of the ballastless track;
and S4, judging the line section needing to be maintained according to the disease identification result of the ballastless track.
In step S2, the sound radiation signal of the ballastless track structure is enhanced by a sound enhancement method based on deep learning.
As shown in fig. 3, the deep learning based sound enhancement method includes a neural network model training phase and a neural network model application phase.
The specific process of the neural network model training phase comprises the following steps:
s201, collecting interference noise in a train operation environment, forming mixed sound together with a ballastless track structure sound radiation signal generated by numerical simulation, and performing short-time Fourier transform on the ballastless track structure sound radiation signal and the mixed sound respectively to obtain a corresponding time-frequency signal;
s202, extracting a time-frequency mask of the sound radiation signal of the ballastless track structure and time-frequency characteristics of mixed sound from the time-frequency signal, wherein the time-frequency mask of the sound radiation signal of the ballastless track structure is used as a training target, and the time-frequency characteristics of the mixed sound are used as input data and input into a recurrent neural network model for training.
The specific process of the neural network model application stage comprises the following steps:
s203, obtaining a corresponding time-frequency signal from the original sound signal through short-time Fourier transform (STFT);
s204, extracting acoustic characteristics from the time-frequency signal of the original sound signal and inputting the acoustic characteristics into the trained recurrent neural network model to obtain a time-frequency mask of the sound radiation signal of the ballastless track structure;
s205, multiplying the time-frequency mask of the ballastless track structure acoustic radiation signal by the acoustic characteristics of the original sound signal, and then performing short-time inverse Fourier transform (iSTFT) to obtain the enhanced ballastless track structure acoustic radiation signal.
In the step S3, the track diseases are identified by a supervised machine learning algorithm, which specifically includes the following steps:
s301, generating an acoustic radiation signal under a typical defect state of a ballastless track structure through numerical simulation, extracting characteristic information of the acoustic radiation signal, processing the characteristic information through a principal component analysis method, and associating the processed characteristic information with the typical defect state of the ballastless track structure to generate a defect dictionary;
s302, taking the processed characteristic information as input data of a training Support Vector Machine (SVM) model, and performing supervised classification training and verification on the SVM model according to a disease dictionary;
and S303, performing feature extraction on the ballastless track structure acoustic radiation signal enhanced in the step S205, inputting the extracted feature into a trained SVM model, classifying the enhanced acoustic radiation signal by the SVM model to obtain a signal classification result, and identifying a corresponding disease type and a disease degree according to the signal classification result to serve as a disease identification result.
The types of processing performed on the feature information by the principal component analysis method in step S301 include decorrelation processing operation and redundancy removal processing operation.
Typical damage states of the ballastless track structure comprise track slab void, track slab arch-up, track slab cracking and base plate cracking.
The feature information includes a time domain feature, a frequency domain feature, and a time-frequency domain feature.
In this embodiment, the process of determining the line segment needing to be maintained in step S4 specifically includes the following steps:
s401, recording the disease type and the disease degree in the disease identification result, and simultaneously recording the position of a line where the disease is located;
s402, judging whether the disease degree of each position exceeds a preset damage threshold value;
and S403, detecting the damage position with the damage degree exceeding the damage threshold as a line section needing maintenance.
As shown in fig. 2, a system of a ballastless track disease detection method based on vehicle-mounted acoustic sensing includes:
the acoustic sensor 1 is used for collecting an original sound signal in the running process of a train;
the signal enhancement unit 2 is used for enhancing the sound radiation signal of the ballastless track structure according to the original sound signal;
the disease identification unit 3 is used for identifying the corresponding track disease according to the enhanced acoustic radiation signal to obtain a disease identification result of the ballastless track;
and the maintenance guidance unit 4 is used for judging the line section needing to be maintained according to the disease identification result of the ballastless track.
The acoustic sensor 1 is arranged at the bottom of the train bogie.
In addition, it should be noted that the specific embodiments described in the present specification may have different names, and the above descriptions in the present specification are only illustrations of the structures of the present invention. All equivalent or simple changes in the structure, characteristics and principles of the invention are included in the protection scope of the invention. Various modifications or additions may be made to the described embodiments or methods may be similarly employed by those skilled in the art without departing from the scope of the invention as defined in the appending claims.
Claims (10)
1. A ballastless track disease detection method based on vehicle-mounted acoustic sensing is characterized by comprising the following steps:
s1, collecting original sound signals in the running process of the train;
s2, enhancing the sound radiation signal of the ballastless track structure according to the original sound signal;
s3, identifying corresponding track diseases according to the enhanced sound radiation signals of the ballastless track structure to obtain a disease identification result of the ballastless track;
and S4, judging the line section needing to be maintained according to the disease identification result of the ballastless track.
2. The ballastless track disease detection method based on vehicle-mounted acoustic sensing of claim 1, wherein in step S2, the sound radiation signal of the ballastless track structure is enhanced by a sound enhancement method based on deep learning.
3. The ballastless track disease detection method based on vehicle-mounted acoustic sensing according to claim 2, wherein the sound enhancement method based on deep learning comprises a neural network model training stage and a neural network model application stage.
4. The ballastless track disease detection method based on vehicle-mounted acoustic sensing according to claim 3, wherein the specific process of the neural network model training phase comprises the following steps:
s201, collecting interference noise in a train operation environment, forming mixed sound together with a ballastless track structure sound radiation signal generated by numerical simulation, and performing short-time Fourier transform on the ballastless track structure sound radiation signal and the mixed sound respectively to obtain a corresponding time-frequency signal;
s202, extracting a time-frequency mask of the sound radiation signal of the ballastless track structure and time-frequency characteristics of mixed sound from the time-frequency signal, wherein the time-frequency mask of the sound radiation signal of the ballastless track structure is used as a training target, and the time-frequency characteristics of the mixed sound are used as input data and input into a recurrent neural network model for training.
5. The ballastless track disease detection method based on vehicle-mounted acoustic sensing according to claim 4, wherein the specific process of the neural network model application stage comprises the following steps:
s203, obtaining a corresponding time-frequency signal from the original sound signal through short-time Fourier transform;
s204, extracting acoustic characteristics from the time-frequency signal of the original sound signal and inputting the acoustic characteristics into the trained recurrent neural network model to obtain a time-frequency mask of the sound radiation signal of the ballastless track structure;
s205, multiplying the time-frequency mask of the ballastless track structure acoustic radiation signal by the acoustic characteristics of the original sound signal, and performing short-time Fourier inverse transformation to obtain the enhanced ballastless track structure acoustic radiation signal.
6. The ballastless track disease detection method based on vehicle-mounted acoustic sensing of claim 5, wherein the step S3 of identifying track diseases through a supervised machine learning algorithm specifically comprises the following steps:
s301, generating an acoustic radiation signal under a typical defect state of a ballastless track structure through numerical simulation, extracting characteristic information of the acoustic radiation signal, processing the characteristic information through a principal component analysis method, and associating the processed characteristic information with the typical defect state of the ballastless track structure to generate a defect dictionary;
s302, taking the processed characteristic information as input data of the SVM model, and performing supervised classification training and verification on the SVM model according to a disease dictionary;
and S303, performing feature extraction on the ballastless track structure acoustic radiation signal enhanced in the step S205, inputting the extracted feature into a trained SVM model, classifying the enhanced acoustic radiation signal by the SVM model to obtain a signal classification result, and identifying a corresponding disease type and a disease degree according to the signal classification result to serve as a disease identification result.
7. The ballastless track disease detection method based on vehicle-mounted acoustic sensing is characterized in that the typical disease states of the ballastless track structure comprise track slab void, track slab upwarp, track slab cracking and base plate cracking.
8. The ballastless track disease detection method based on vehicle-mounted acoustic sensing of claim 1, wherein the process of determining the line section needing to be maintained in step S4 includes disease recording, position recording and threshold value determination.
9. The system for using the ballastless track disease detection method based on vehicle-mounted acoustic sensing according to claim 1, is characterized by comprising:
the acoustic sensor (1) is used for collecting an original sound signal in the running process of the train;
the signal enhancement unit (2) is used for enhancing the sound radiation signal of the ballastless track structure according to the original sound signal;
the disease identification unit (3) identifies corresponding track diseases according to the enhanced sound radiation signals of the ballastless track structure to obtain a disease identification result of the ballastless track;
and the maintenance guiding unit (4) is used for judging the line section needing to be maintained according to the disease identification result of the ballastless track.
10. The system for detecting the ballastless track damage based on the vehicle-mounted acoustic sensor is characterized in that the acoustic sensor (1) is arranged at the bottom of a train bogie.
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