Turnout Fault Diagnosis Based on CNNs with Self-Generated Samples

Huang, Shize; Yang, Liansheng; Zhang, Fan; Chen, Wei; Wu, Zaixin

doi:10.1061/jtepbs.0000432

Cited by 16 publications

(14 citation statements)

References 7 publications

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“…Assume that the value of the early eliminating judgment val is 30. Since ds(2,1), ds(3,1), ds(4,1), ds(6,2), ds (7,3), and ds (5,5) are all greater than val, these cells are excluded. at is, they do not participate in the calculation of all subsequent FastDTW cumulative distance matrices.…”

Section: Fast Dynamic Time Warping Algorithm Based On Early Eliminati...mentioning

confidence: 99%

“…Input: time series X，fitting error value e，and number of segments number Output: important points collection of time series P (1) function SEGMENTATION(X,e,k) (2) It Queue(Q)//initialize the queue to be checked, which follows the first-in first-out principle (3) in Queue(stapoint, endpoint)//start point stapoint and end point enter the Q (4) while Q is not null and length (FPIP) < number-1 do (5) (stapoint, endpoint)←out Queue()//take out the series to be tested (6) (segpoint, error)←calculate pip(stapoint, endpoint) (7) //calculate the farthest point segpoint that deviates from the linear fitting of the series to be tested, and then return the regression coefficient error (8) if error > e then//continue to segment series with poor fitting result (9) P←segpoint//important point joins the collection (10)…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…Shize et al analyzed the action mechanism of a certain kind of turnout, establishing a turnout action model to simulate the operating current curve of turnout. en, they used a large number of generated samples to train a turnout fault diagnosis model based on convolutional neural network (CNN), which can effectively identify faults based on curve pictures in various formats [3]. Wei et al proposed a deep learning-based joint learning method for mechanical fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Study on Intelligent Diagnosis of Railway Turnout Switch Based on Improved FastDTW and Time Series Segmentation under Big Data Monitoring

Gao

Yong

et al. 2022

Mathematical Problems in Engineering

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Turnout equipment is a key component to ensure the safe operation of trains. How to identify turnout faults is one of the important tasks of railway engineering departments and electrical departments. We used machine learning algorithm to analyze the similarity of mechanical characteristic data during turnout actions and then realized the intelligent diagnosis of turnout faults under the background of big data. We segmented the mechanical motion curve according to the characteristics of the original curve, calculated the similarity between the motion curve to be diagnosed and the template motion curve through the improved fast dynamic time warping algorithm (FastDTW), and diagnosed the fault according to the dynamically determined optimal threshold. Experiments show that the algorithm for predicting turnout faults based on improved FastDTW and time series segmentation algorithm is an accurate and effective method, which can improve the key technology of intelligent monitoring, early warning of the whole process of turnout movement, and more detailed analysis of the motion curve for the railway department.

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Section: Fast Dynamic Time Warping Algorithm Based On Early Eliminati...mentioning

confidence: 99%

Section: Conflicts Of Interestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Study on Intelligent Diagnosis of Railway Turnout Switch Based on Improved FastDTW and Time Series Segmentation under Big Data Monitoring

Gao

Yong

et al. 2022

Mathematical Problems in Engineering

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“…At present, various articles in the literature have put forward the application of artificial intelligence technology to the fault diagnosis of RTS, and the related methods can be summarized as the threshold-based method [ 9 , 10 ], machine learning (ML) based [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ], or deep learning (DL) based [ 23 , 24 , 25 , 26 ]. Huang et al [ 9 ] proposed a fault-detection method by using dynamic time warping based on the turnout current curve, which can detect faults by comparing the distance from the template curve.…”

Section: Introductionmentioning

confidence: 99%

A Fault-Diagnosis Method for Railway Turnout Systems Based on Improved Autoencoder and Data Augmentation

Hei

et al. 2022

Sensors

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In recent years, with the rapid increase in coverage and lines, security maintenance has become one of the top concerns with regard to railway transportation in China. As the key transportation infrastructure, the railway turnout system (RTS) plays a vital role in transportation, which will cause incalculable losses when accidents occur. The traditional fault-diagnosis and maintenance methods of the RTS are no longer applicable to the growing amount of data, so intelligent fault diagnosis has become a research hotspot. However, the key challenge of RTS intelligent fault diagnosis is to effectively extract the deep features in the signal and accurately identify failure modes in the face of unbalanced datasets. To solve the above two problems, this paper focuses on unbalanced data and proposes a fault-diagnosis method based on an improved autoencoder and data augmentation, which realizes deep feature extraction and fault identification of unbalanced data. An improved autoencoder is proposed to smooth the noise and extract the deep features to overcome the noise fluctuation caused by the physical characteristics of the data. Then, synthetic minority oversampling technology (SMOTE) is utilized to effectively expand the fault types and solve the problem of unbalanced datasets. Furthermore, the health state is identified by the Softmax regression model that is trained with the balanced characteristics data, which improves the diagnosis precision and generalization ability. Finally, different experiments are conducted on a real dataset based on a railway station in China, and the average diagnostic accuracy reaches 99.13% superior to other methods, which indicates the effectiveness and feasibility of the proposed method.

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“…e systems are used to detect obstacles and their locations beforehand and take corresponding measures. Neural networks have been adopted widely, such as vehicle license plate recognition [5], system control [6][7][8], turnout fault diagnosis [9], and immunohistochemistry [10]. e growing perception systems have incited ever-rising adversarial attacks on deep learning algorithms [11][12][13], imposing hidden threats to tram security.…”

Section: Introductionmentioning

confidence: 99%

Two Improved Methods of Generating Adversarial Examples against Faster R-CNNs for Tram Environment Perception Systems

et al. 2020

Self Cite

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Trams have increasingly deployed object detectors to perceive running conditions, and deep learning networks have been widely adopted by those detectors. Growing neural networks have incurred severe attacks such as adversarial example attacks, imposing threats to tram safety. Only if adversarial attacks are studied thoroughly, researchers can come up with better defence methods against them. However, most existing methods of generating adversarial examples have been devoted to classification, and none of them target tram environment perception systems. In this paper, we propose an improved projected gradient descent (PGD) algorithm and an improved Carlini and Wagner (C&W) algorithm to generate adversarial examples against Faster R-CNN object detectors. Experiments verify that both algorithms can successfully conduct nontargeted and targeted white-box digital attacks when trams are running. We also compare the performance of the two methods, including attack effects, similarity to clean images, and the generating time. The results show that both algorithms can generate adversarial examples within 220 seconds, a much shorter time, without decrease of the success rate.

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Turnout Fault Diagnosis Based on CNNs with Self-Generated Samples

Cited by 16 publications

References 7 publications

Study on Intelligent Diagnosis of Railway Turnout Switch Based on Improved FastDTW and Time Series Segmentation under Big Data Monitoring

Study on Intelligent Diagnosis of Railway Turnout Switch Based on Improved FastDTW and Time Series Segmentation under Big Data Monitoring

A Fault-Diagnosis Method for Railway Turnout Systems Based on Improved Autoencoder and Data Augmentation

Two Improved Methods of Generating Adversarial Examples against Faster R-CNNs for Tram Environment Perception Systems

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