A Fault Detection Method for Electrohydraulic Switch Machine Based on Oil-Pressure-Signal-Sectionalized Feature Extraction

Abstract

A turnout switch machine is key equipment in a railway, and its fault condition has an enormous impact on the safety of train operation. Electrohydraulic switch machines are increasingly used in high-speed railways, and how to extract effective fault features from their working condition monitoring signal is a difficult problem. This paper focuses on the sectionalized feature extraction method of the oil pressure signal of the electrohydraulic switch machine and realizes the fault detection of the switch machine based on this method. First, the oil pressure signal is divided into three stages according to the working principle and action process of the switch machine, and multiple features of each stage are extracted. Then the max-relevance and min-redundancy (mRMR) algorithm is applied to select the effective features. Finally, the mini batch k-means method is used to achieve unsupervised fault diagnosis. Through experimental verification, this method can not only derive the best sectionalization mode and feature types of the oil pressure signal, but also achieve the fault diagnosis and the prediction of the status of the electrohydraulic switch machine.

Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects

“…Principal component analysis [27] Linear discriminate analysis [15,44] New feature selection methods mRMR [38,42,45] FRSSED [29,43] is not well-developed. To address this issue, a new technique called feature ranking and subset selection based on Euclidean distance (FRSSED) was introduced.…”

IndustNerf: Accurate 3D Industrial Digital Twin based on Integrating Neural Radiance Fields using Unsupervised Learning

“…Figure 1 shows the process of unsupervised learning Unsupervised learning has found myriad applications across industries, especially in scenarios with vast unlabelled datasets. In the manufacturing realm, it's utilized for tasks like anomaly detection in machinery, where normal operations create a pattern and deviations from this pattern signal potential issues [21,22]. Additionally, it aids in segmenting market data for targeted product releases, allowing companies to better understand customer behaviors and preferences.…”

Wireless sensing in high-speed railway turnouts with battery-free materials and devices

“… 27 An approach for identifying possible fault occurrences of railway point-operating devices using unlabeled signal sensor data was developed. 28 The sectionalized feature extraction technique of the electrohydraulic switch machine’s oil pressure signal 29 and the turnout system’s optimal operation with maintenance 30 were depicted in order to perform fault diagnosis and forecast the state of the electrohydraulic switch machine. Furthermore, to improve overall fault-detection performance, a Kalman filter for the linear discrete data filtering problem encountered when using current sensor data in a point condition monitoring system was proposed to predict problems and enable quick recovery before component failures disrupt operations.…”