A fault detection method for railway point systems

Vileiniskis, Marius; Remenyte‐Prescott, Rasa; Rama, Dovile

doi:10.1177/0954409714567487

Cited by 60 publications

(60 citation statements)

References 29 publications

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“…Similarly, a fault diagnostics approach was also presented by the authors in other studies based on the principal component analysis (PCA) and SVM classifier using the extracted statistical features. Similar works for fault detection of point machines have been studied in literature as well . Next to machine learning tools, statistical techniques were also used in point machine CM.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

“…Similar works for fault detection of point machines have been studied in literature as well. 24,25 Next to machine learning tools, statistical techniques were also used in point machine CM.…”

mentioning

confidence: 99%

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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“…A methodology for early warning of possible point failure through early detection of changes in the current drawn by the point motor proposed in [28], which use the one class support vector machine classification method with the similarity measure of ERP taking into account specific features of the data in railway transportation systems field. It is able to detect the changes in the measurements of the current of the point operating equipment with greater accuracy compared with the commonly used threshold based technique.…”

Section:  Application 2: Erp In Transportation Systemsmentioning

confidence: 99%

FESM: An Analytical Framework for Elastic Similarity Measures Based Time Series Pattern Recognition

Esmaeili¹,

Faez²

2016

IJCA

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Compare the similarity of time series is a key for most tasks and there are various similarity measures which measure the similarity of time series. Similarity measures are the basis of time series research, they are quite important for improving the efficiency and accuracy of the time series pattern recognition tasks. Therefore selection the best similarity measures are very essential. On this issue, in this paper an analytical framework for elastic similarity measures based time series pattern recognition as, FESM for short, is proposed. FESM consists of three main components: 1) Classification of elastic similarity measures of time series, 2) Comparative evaluation of classified similarity measures based on proposed qualitative evaluation criteria, and 3) Application scopes of classified similarity measures. FESM will be proper for the quick understanding and comparing of time series similarity measures, and selection the best of existing similarity measures for respective time series pattern recognition tasks. General TermsPattern Recognition

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“…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.…”

mentioning

confidence: 99%

“…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].…”

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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A fault detection method for railway point systems

Cited by 60 publications

References 29 publications

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

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

FESM: An Analytical Framework for Elastic Similarity Measures Based Time Series Pattern Recognition

Degradation state mining and identification for railway point machines

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