Fault diagnosis of railway point machines using dynamic time warping

Kim, H.; Sa, Jaewon; Chung, Yongwha; Park, D.; Yoon, Sukhan

doi:10.1049/el.2016.0206

Cited by 46 publications

(27 citation statements)

References 5 publications

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“…Otherwise, the EPM is repaired or replaced the same night. Furthermore, as indicated in Figure 2, the electric current shape of a faulty (i.e., abnormal) EPM is totally different from that of a not-faulty (i.e., normal) EPM, and the faulty EPM can be easily detected by the dynamic time warping (DTW) method [15] for quick repair. However, the subtle difference caused by aging may not be detected by the previous method.…”

Section: Methods For Diagnosis Of Aging In Epmsmentioning

confidence: 99%

“…With the assistance of the maintenance staff, before-replacement data was categorized into two classes: "normal" and "abnormal." As indicated in Figure 3a, the differences between the two classes resulting from aging and variations in the classes were subtle (especially when compared with the differences resulting from faulty machinery [15] displayed in Figure 1). By performing length-normalization and Z-normalization on the data, variations within each class were reduced, meaning that the effect of aging on electric current signals could be clearly understood.…”

Section: In-field Current Signalsmentioning

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%

Section: In-field Current Signalsmentioning

confidence: 99%

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

Choi

Chung

et al. 2017

Symmetry

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“…However, the lack of available labeled data necessary for the training of the models used for anomaly detection techniques is usually a major obstacle to apply such models. Kim et al [12] pioneered a diagnosis method using dynamic time warping to manage the variation of the current signals without training steps. Inspired by the authors of [12], we attempt to solve the fault detection problem in a non-training way.…”

Section: Related Workmentioning

confidence: 99%

“…Kim et al [12] pioneered a diagnosis method using dynamic time warping to manage the variation of the current signals without training steps. Inspired by the authors of [12], we attempt to solve the fault detection problem in a non-training way. In this paper, the LOF algorithm is employed for fault detection.…”

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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Feature selection and fault‐severity classification–based machine health assessment methodology for point machine sliding‐chair degradation

Atamuradov

Medjaher

Camci

et al. 2019

Quality & Reliability Eng

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In this paper, we propose an offline and online machine health assessment (MHA) methodology composed of feature extraction and selection, segmentation‐based fault severity evaluation, and classification steps. In the offline phase, the best representative feature of degradation is selected by a new filter‐based feature selection approach. The selected feature is further segmented by utilizing the bottom‐up time series segmentation to discriminate machine health states, ie, degradation levels. Then, the health state fault severity is extracted by a proposed segment evaluation approach based on within segment rate‐of‐change (RoC) and coefficient of variation (CV) statistics. To train supervised classifiers, a priori knowledge about the availability of the labeled data set is needed. To overcome this limitation, the health state fault‐severity information is used to label (eg, healthy, minor, medium, and severe) unlabeled raw condition monitoring (CM) data. In the online phase, the fault‐severity classification is carried out by kernel‐based support vector machine (SVM) classifier. Next to SVM, the k‐nearest neighbor (KNN) is also used in comparative analysis on the fault severity classification problem. Supervised classifiers are trained in the offline phase and tested in the online phase. Unlike to traditional supervised approaches, this proposed method does not require any a priori knowledge about the availability of the labeled data set. The proposed methodology is validated on infield point machine sliding‐chair degradation data to illustrate its effectiveness and applicability. The results show that the time series segmentation‐based failure severity detection and SVM‐based classification are promising.

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Fault diagnosis of railway point machines using dynamic time warping

Cited by 46 publications

References 5 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

Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder

Feature selection and fault‐severity classification–based machine health assessment methodology for point machine sliding‐chair degradation

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