Real-Time Prognostics and Health Management Without Run-to-Failure Data on Railway Assets

• Published in: IEEE access Vol. 11; pp. 28724 - 28734
• Main Authors: Shimizu, Minoru, Perinpanayagam, Suresh, Namoano, Bernadin, Starr, Andrew
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Algorithms; Component reliability; Data models; Degradation; door systems; electro-mechanical actuators; EMAs; Estimation; Failure; Fault detection; linear actuator; PHM; Prediction models; Predictive maintenance; Predictive models; Prognosis; Prognostics and health management; Rail transportation; railway; remaining useful life; signal processing; Structural health monitoring
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3259221

Prognosis is a challenging technology that aims to accurately predict and estimate the remaining useful life of a component or system in order to enhance its reliability and performance. Although prognosis research for predictive maintenance is a well-researched topic, practical examples of successful prognostic applications remain scarce. This is due to the lack of available run-to-failure data to build the prediction model as maintenance is usually conducted regularly to avoid significant defects. This paper proposes a novel prognosis method that can be applied to real-world railway maintenance planning without employing run-to-failure data. The key idea is that the fault severity assessment and approximate remaining time prediction are often all that is needed in order to plan maintenance. Firstly, using motor current signals, a degradation indicator on railway door systems is generated based on the dynamic time warping method to measure similarity between typical normal and faulty behaviour. Then, the K-means algorithm is applied to assess fault severity, followed by the representative time estimation for each level of fault severity. This estimation thus allows the remaining time prediction until reaching the critical fault severity level without using run-to-failure data. As a result, the proposed method enables predictive maintenance planning for railway door systems. In addition, the fault severity threshold can be updated by additional operational data, enabling the remaining time prediction to be more reliable. Furthermore, the proposed method can be applied to conventional railway assets and other electro-mechanical actuators as motor current signals are primarily available from the controller or motor drive without additional sensors.