Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder

Zhen, Li; Yin, Zhuo; Tang, Tao; Gao, Chunhai

doi:10.3390/app9235139

Cited by 21 publications

(12 citation statements)

References 29 publications

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“…CNN, transfer learning [64] CNN, transfer learning, Bayesian optimization to tune hyperparameters [100] CNN-LSTM [87,103,107] Faster R-CNN [78,88] Faster R-CNN + CNN [73] FastNet, convolutional network-based [120] Fine-grained bilinear CNNs model [70] FCN [119] GAN for CNN [115] Inception-ResNet-v2 & CNN [113] LSTM-RNN [63,71,99] Mask R-CNN…”

Section: Review Of Rail Track Condition Monitoring With Deep Learningmentioning

confidence: 99%

Rail track condition monitoring: a review on deep learning approaches

Ji¹,

Woo²,

Wong³

et al. 2021

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Rail track is a critical component of rail systems. Accidents or interruptions caused by rail track anomalies usually possess severe outcomes. Therefore, rail track condition monitoring is an important task. Over the past decade, deep learning techniques have been rapidly developed and deployed. In the paper, we review the existing literature on applying deep learning to rail track condition monitoring. Potential challenges and opportunities are discussed for the research community to decide on possible directions. Two application cases are presented to illustrate the implementation of deep learning to rail track condition monitoring in practice before we conclude the paper.

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Section: Review Of Rail Track Condition Monitoring With Deep Learningmentioning

confidence: 99%

Rail track condition monitoring: a review on deep learning approaches

Ji¹,

Woo²,

Wong³

et al. 2021

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“…A hybrid fault diagnosis (HFD) method was adopted to identify a fault based on the current curves of a railway switch machine in [ 8 ]. A locally connected autoencoder was employed to automatically capture high-order features in order to solve the fault diagnosis problem with no training steps based on the current signal of an electric point machine [ 9 ]. DAG-SVMs were applied to intelligently detect a fault after extracting characteristics based on the action current signal of an electric switch machine, and the experiment showed that the accuracy of classification after Kalman filter pretreatment was better than that of direct classification in [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Fault Detection Method for Electrohydraulic Switch Machine Based on Oil-Pressure-Signal-Sectionalized Feature Extraction

Meng

Wen

Bai

et al. 2022

Entropy

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A turnout switch machine is key equipment in a railway, and its fault condition has an enormous impact on the safety of train operation. Electrohydraulic switch machines are increasingly used in high-speed railways, and how to extract effective fault features from their working condition monitoring signal is a difficult problem. This paper focuses on the sectionalized feature extraction method of the oil pressure signal of the electrohydraulic switch machine and realizes the fault detection of the switch machine based on this method. First, the oil pressure signal is divided into three stages according to the working principle and action process of the switch machine, and multiple features of each stage are extracted. Then the max-relevance and min-redundancy (mRMR) algorithm is applied to select the effective features. Finally, the mini batch k-means method is used to achieve unsupervised fault diagnosis. Through experimental verification, this method can not only derive the best sectionalization mode and feature types of the oil pressure signal, but also achieve the fault diagnosis and the prediction of the status of the electrohydraulic switch machine.

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“…To overcome the aforementioned weaknesses, developing a method by which to adaptively learn the features from raw signalizing advanced artificial intelligent techniques would be necessary, instead of extracting and selecting features manually. As a successful DL technology, the autoencoder (AE) with the potential of latent feature extraction has been applied [ 8 , 12 , 34 , 35 ] widely. Compared with traditional feature-extraction methods, AE extracts the deep feature information in the signal more effectively and depends less on prior knowledge or human labor.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with traditional feature-extraction methods, AE extracts the deep feature information in the signal more effectively and depends less on prior knowledge or human labor. Li et al [ 35 ] employed a locally connected autoencoder to automatically capture high-order features. In this work, the current signals are segmented and blended to enhance the temporal characteristic.…”

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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Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder

Cited by 21 publications

References 29 publications

Rail track condition monitoring: a review on deep learning approaches

Rail track condition monitoring: a review on deep learning approaches

A Fault Detection Method for Electrohydraulic Switch Machine Based on Oil-Pressure-Signal-Sectionalized Feature Extraction

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

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