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Open AccessArticle

Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder

by 1,2,3,*, 2,3, 1 and 2,3
1
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 10044, China
2
National Engineering Laboratory for Urban Rail Transit Communication and Operation Control, Beijing 100044, China
3
Traffic Control Technology Co., Ltd., Beijing 100070, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(23), 5139; https://doi.org/10.3390/app9235139
Received: 4 November 2019 / Revised: 24 November 2019 / Accepted: 25 November 2019 / Published: 27 November 2019
(This article belongs to the Section Applied Industrial Technologies)
Data-driven fault diagnosis is considered a modern technique in Industry 4.0. In the area of urban rail transit, researchers focus on the fault diagnosis of railway point machines as failures of the point machine may cause serious accidents, such as the derailment of a train, leading to significant personnel and property loss. This paper presents a novel data-driven fault diagnosis scheme for railway point machines using current signals. Different from any handcrafted feature extraction approach, the proposed scheme employs a locally connected autoencoder to automatically capture high-order features. To enhance the temporal characteristic, the current signals are segmented and blended into some subsequences. These subsequences are then fed to the proposed autoencoder. With the help of a weighting strategy, the seized features are weight averaged into a final representation. At last, different from the existing classification methods, we employ the local outlier factor algorithm to solve the fault diagnosis problem without any training steps, as the accurate data labels that indicate a healthy or unhealthy state are difficult to acquire. To verify the effectiveness of the proposed fault diagnosis scheme, a fault dataset termed “Cu-3300” is created by collecting 3300 in-field current signals. Using Cu-3300, we perform comprehensive analysis to demonstrate that the proposed scheme outperforms the existing methods. We have made the dataset Cu-3300 and the code file freely accessible as open source files. To the best of our knowledge, the dataset Cu-3300 is the first open source dataset in the area of railway point machines and our conducted research is the first to investigate the use of autoencoders for fault diagnosis of point machines. View Full-Text
Keywords: railway point machines; urban rail transit systems; fault diagnosis; autoencoder railway point machines; urban rail transit systems; fault diagnosis; autoencoder
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MDPI and ACS Style

Li, Z.; Yin, Z.; Tang, T.; Gao, C. Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder. Appl. Sci. 2019, 9, 5139. https://doi.org/10.3390/app9235139

AMA Style

Li Z, Yin Z, Tang T, Gao C. Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder. Applied Sciences. 2019; 9(23):5139. https://doi.org/10.3390/app9235139

Chicago/Turabian Style

Li, Zhen; Yin, Zhuo; Tang, Tao; Gao, Chunhai. 2019. "Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder" Appl. Sci. 9, no. 23: 5139. https://doi.org/10.3390/app9235139

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