Replacement Condition Detection of Railway Point Machines Using an Electric Current Sensor

Abstract

Detecting replacement conditions of railway point machines is important to simultaneously satisfy the budget-limit and train-safety requirements. In this study, we consider classification of the subtle differences in the aging effect—using electric current shape analysis—for the purpose of replacement condition detection of railway point machines. After analyzing the shapes of after-replacement data and then labeling the shapes of each before-replacement data, we can derive the criteria that can handle the subtle differences between “does-not-need-to-be-replaced” and “needs-to-be-replaced” shapes. On the basis of the experimental results with in-field replacement data, we confirmed that the proposed method could detect the replacement conditions with acceptable accuracy, as well as provide visual interpretability of the criteria used for the time-series classification.

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

“…Of course, the performance of both the SVM and random forest methods outperformed the proposed method in some datasets, but the classification methods could not yield as consistent a performance as the proposed method. Furthermore, although the shapelet method achieved a good classification performance for the two (i.e., one aged and one not-yet-aged) patterns in [17], it had a limitation in distinguishing multiple aged patterns from the not-yet-aged pattern. Given that the EPM environment undergoes many variations in each location, the possibility of unpredictable or various aging patterns should not be ignored.…”

“…(a) For example, our previous study employed a shapelet method [16] and achieved an acceptable accuracy [17]. The Shapelet method is a machine learning approach that is used to classify data by analyzing time-series shapes.…”

Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder

“…Using the electric current as a parameter, Asada et al [7] proposed a wavelet transform-based feature extraction scheme by which an accurate health prediction was obtained. 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.…”

“…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]. The support vector machine is a representative method to solve the task [15].…”

Data Driven Detection of Railway Point Machines Failures

“…Main approaches for providing early faults detection of a point machine seems to be based on the analysis of their power consumption [1] [2] [3]. In [4], authors estimate the remaining useful life based on electric current measurements, in [5] vision-based measurements of a gap between point machine blade and the rail is used, and in [6] an analysis of the sounds from the point machine is done.…”