Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis

Lee, Jong-Uk; Choi, Heesu; Park, Jooyoung; Chung, Yongwha; Kim, Hee-Young; Yoon, Sukhan

doi:10.3390/s16040549

Cited by 113 publications

(78 citation statements)

References 21 publications

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“…If an EPM starts exhibiting faulty behavior in the daytime, the maintenance staffs check it immediately. If the fault is a one-time fault caused by noise, such as ballast obstruction [12], then the EPM resumes its operation after the ballast has been removed. Otherwise, the EPM is repaired or replaced the same night.…”

Section: Methods For Diagnosis Of Aging In Epmsmentioning

confidence: 99%

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

Choi

Chung

et al. 2017

Symmetry

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Electrical point machines (EPM) must be replaced at an appropriate time to prevent the occurrence of operational safety or stability problems in trains resulting from aging or budget constraints. However, it is difficult to replace EPMs effectively because the aging conditions of EPMs depend on the operating environments, and thus, a guideline is typically not be suitable for replacing EPMs at the most timely moment. In this study, we propose a method of classification for the detection of an aging effect to facilitate the timely replacement of EPMs. We employ support vector data description to segregate data of "aged" and "not-yet-aged" equipment by analyzing the subtle differences in normalized electrical signals resulting from aging. Based on the before and after-replacement data that was obtained from experimental studies that were conducted on EPMs, we confirmed that the proposed method was capable of classifying machines based on exhibited aging effects with adequate accuracy.

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Section: Methods For Diagnosis Of Aging In Epmsmentioning

confidence: 99%

“…One approach to EPM fault diagnosis involves the classification of machines based on health using support vector machines (SVM) and a discrete wavelet transform (DWT)-based strategy [11]. Further, a fault detection and diagnosis approach for EPMs has been reported using audio signals regarding different abnormal situations [12].…”

Section: Introductionmentioning

confidence: 99%

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

Choi

Chung

et al. 2017

Symmetry

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“…( (6) According to 6 Seq , sort the samples in 3 Sel to their respective clustering states. By analyzing the power curve of each state, find and remove the normal state samples.…”

Section: Degradation State Mining Based On Sommentioning

confidence: 99%

“…The fault detection technology of CMS needs to be improved in order to increase the maintenance efficiency of PMs. In recent years, research on fault detection for PMs has gradually transitioned from the threshold-based method to statistical analysis, model-based, and classification methods [3]. Compared to thresholding technology, these three methods can ensure early detection of machine failure.…”

mentioning

confidence: 99%

Degradation state mining and identification for railway point machines

Bian

Yang

Huang

et al. 2019

Reliability Engineering & System Safety

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Point machines (PMs) are used for switching and locking railway turnouts, and are considered one of the most critical elements of a railway signal system. The failure of the point mechanism directly affects the operation of the railway and may cause serious safety accidents. Hence, there is a need for early detection of the anomalies in PMs. From normal operation to complete failure, the machine usually undergoes a series of degradation states. If the degradation states are detected in time, maintenance can be organized in advance to prevent the malfunction. This paper presents a degradation detection method that can effectively mine and identify the degradation state of the PM. First, power data is processed to obtain the feature set that can describe the PM characteristics effectively. Then, a clustering analysis of the feature set is carried out by self-organizing feature-mapping network, and various degradation states are mined. Finally, the optimized support vector machine is used to build the state classifier to identify the degradation state of the PM. The experimental results obtained with the Siemens S700K PM show that the proposed method could not only mine the effective degradation states, but also obtain high identification accuracy. 2The performance of a PM directly affects the safety of train operation. In the field investigation of the Chenzhou West Station, the related faults caused by the PM were found to account for 30% of all signal equipment failures, and the average time for repairing the PM is approximately 23.4 minutes, which has a serious impact on the operation of the high-speed railway. Therefore, it is necessary to detect the faults in the PM in time and make reasonable maintenance plans to improve the operational efficiency of the high-speed railway. A condition monitoring system (CMS) can measure, centralize, and analyze the data from sensors installed in the field equipment to detect equipment failure [1]. Many railway companies have implemented CMS for PMs. However, experience in this field has shown that these systems create an alarm only when a failure occurs or very close to the point of failure. In addition, most of those currently in use do not effectively detect faults, and the system simply creates an alert to indicate abnormal behavior, leading to a delay in repairing of the PM [2]. The fault detection technology of CMS needs to be improved in order to increase the maintenance efficiency of PMs. In recent years, research on fault detection for PMs has gradually transitioned from the threshold-based method to statistical analysis, model-based, and classification methods [3]. Compared to thresholding technology, these three methods can ensure early detection of machine failure. Among them, the classification method is widely used in CMS [4].The classification method can be divided into three main steps, viz. data feature processing, classifier establishment, and fault identification. The data feature processing step converts the measurement signal of the PM, e.g. power signal,...

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“…Automatic processing of sound signals is a very active topic in many fields of science and engineering which find applications in multiple areas, such as speech recognition [1], speaker identification [2,3], emotion recognition [4], music classification [5], outlier detection [6], classification of animal species [7][8][9], detection of biomedical disease [10], and design of medical devices [11]. Sound processing is also applied in urban and industrial contexts, such as environmental noise control [12], mining [13], and transportation [14,15].…”

Section: Introductionmentioning

confidence: 99%

Exploiting the Symmetry of Integral Transforms for Featuring Anuran Calls

et al. 2019

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The application of machine learning techniques to sound signals requires the previous characterization of said signals. In many cases, their description is made using cepstral coefficients that represent the sound spectra. In this paper, the performance in obtaining cepstral coefficients by two integral transforms, Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT), are compared in the context of processing anuran calls. Due to the symmetry of sound spectra, it is shown that DCT clearly outperforms DFT, and decreases the error representing the spectrum by more than 30%. Additionally, it is demonstrated that DCT-based cepstral coefficients are less correlated than their DFT-based counterparts, which leads to a significant advantage for DCT-based cepstral coefficients if these features are later used in classification algorithms. Since the DCT superiority is based on the symmetry of sound spectra and not on any intrinsic advantage of the algorithm, the conclusions of this research can definitely be extrapolated to include any sound signal.

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Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis

Cited by 113 publications

References 21 publications

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

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

Degradation state mining and identification for railway point machines

Exploiting the Symmetry of Integral Transforms for Featuring Anuran Calls

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