An Unsupervised Fault-Detection Method for Railway Turnouts

• Published in: IEEE transactions on instrumentation and measurement Vol. 69; no. 11; pp. 8881 - 8901
• Main Authors: Guo, Zijian, Wan, Yiming, Ye, Hao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Clustering; Condition monitoring; Data mining; Data models; Deep autoencoders (DAEs); ensemble monitoring model; Fault detection; Fault diagnosis; High speed rail; Machine learning; Monitoring; Rail transportation; Railway accidents; railway turnouts; Support vector machines; transfer learning (TL); unknown modes’ mining; unsupervised fault detection
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2020.2998863

Railway turnouts require high-performance condition monitoring to prevent disastrous railway accidents. In industrial practice, turnouts' monitoring is usually done by railway workers who visually inspect the operating current curves. This results in huge labor costs and prone to human mistakes. Thus, automating the process of turnouts' monitoring via fault-detection algorithms is imperative. The available turnout field data bring three difficulties to fault detection: 1) large amounts of data do not have any labels; 2) data collected in normal condition have multiple unknown modes; and 3) there are only a small number of samples in some modes. To address these difficulties, this article develops a novel unsupervised fault-detection method by using deep autoencoders, which is composed of an unknown modes' mining stage and a multimode fault-detection stage. First, unknown modes are identified through clustering and employing engineer expertise. Then, an ensemble monitoring model, consisting of local monitoring models developed with individual fault-free modes and a global monitoring model developed by merging the data in all fault-free modes, is proposed to improve the overall fault-detection performance. In addition, to construct local models for the modes with a small number of samples, a one-class transfer learning algorithm is presented. In online monitoring, the decision of a newly arrived sample exploits both local models and the global model. Using both the simulated turnout data and the field data collected from a high-speed railway in China, the efficacy and robustness of the proposed approach are demonstrated by comparisons with other methods.