Aging Detection of Electrical Point Machines Based on Support Vector Data Description

Sa, Jaewon; Choi, Younchang; Chung, Yongwha; Lee, Jong-Uk; Park, Jooyoung

doi:10.3390/sym9120290

Cited by 4 publications

(4 citation statements)

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“…Aided by in-field current data, the authors of [14] proposed a classification method to detect the replacement conditions. Sa et al [11] focused on the aging effect of the point machine. This task is regarded as a binary-class classification problem.…”

Section: Related Workmentioning

confidence: 99%

“…The literature includes a wide range of methods for condition monitoring-based failure diagnosis of point machines, including sound analysis [8], gap measurement [9,10], and electric current analysis [11][12][13]. Among these methods, electric current analysis is considered a straightforward and effective approach for failure diagnosis of the point machine, as the point machine is directly actuated by an electric motor [14].…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder

et al. 2019

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder

et al. 2019

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“…Supported by advancements in information communication technology (ICT) and convergence technology in the Industry 4.0 era [7], various industries are conducting research using sound data. In recent times, numerous studies have been conducted in the livestock industry [8][9][10] and railway industry [11][12][13][14][15], making it a broad research topic under the concepts of Industry 4.0. Detection of pig respiratory diseases using sound-signal analysis has been reported where the convergence of research in the livestock industry and IT can be seen.…”

Section: Introductionmentioning

confidence: 99%

“…A practical fault-detection system based on a DTW method was also suggested to detect anomalies in railway-point machines, which could be applied directly to real-world railway sites without requiring a learning process [13]. A study on an aging-condition detection system using electrical signals and applying the SVDD method was also introduced in Reference [14]. In addition to that, a detection and classification system for railway-point machines using MFCC feature information that has been extracted from the sound signals was recently presented by Reference [15].…”

Section: Introductionmentioning

confidence: 99%

Noise-Robust Sound-Event Classification System with Texture Analysis

et al. 2018

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Sound-event classification has emerged as an important field of research in recent years. In particular, investigations using sound data are being conducted in various industrial fields. However, sound-event classification tasks have become more difficult and challenging with the increase in noise levels. In this study, we propose a noise-robust system for the classification of sound data. In this method, we first convert one-dimensional sound signals into two-dimensional gray-level images using normalization, and then extract the texture images by means of the dominant neighborhood structure (DNS) technique. Finally, we experimentally validate the noise-robust approach by using four classifiers (convolutional neural network (CNN), support vector machine (SVM), k-nearest neighbors(k-NN), and C4.5). The experimental results showed superior classification performance in noisy conditions compared with other methods. The F1 score exceeds 98.80% in railway data, and 96.57% in livestock data. Besides, the proposed method can be implemented in a cost-efficient manner (for instance, use of a low-cost microphone) while maintaining high level of accuracy in noisy environments. This approach can be used either as a standalone solution or as a supplement to the known methods to obtain a more accurate solution.

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