An online diagnosis method for voltage sensor intermittent fault in railway traction drive systems based on NARX-ELM predictor

Abstract

In the field of railway traction drive systems, voltage sensor intermittent faults can significantly impact the reliability and safety of the entire system. This paper proposes an online diagnosis method for detecting such faults using an Artificial Intelligence (AI) predictor based on a Nonlinear Autoregressive with eXogenous inputs (NARX) data structure. The model is trained efficiently using the Extreme Learning Machine (ELM) algorithm. The NARX model captures the dynamic characteristics of the voltage sensor data, enabling the AI predictor to learn complex nonlinear relationships. The ELM training method ensures rapid convergence and high accuracy. Through extensive experimental validation, the proposed method demonstrates high sensitivity to voltage sensor intermittent faults and robust performance under varying operating conditions. This approach offers a promising solution for enhancing the diagnostic capabilities of railway traction systems, ensuring timely fault detection and improving overall system reliability.

Keywords

Intermittent faults diagnosis / Nonlinear Autoregressive with eXogenous inputs (NARX) / Voltage sensor

CRediT authorship contribution statement

Yifan Sun: Investigation. Haichuan Tang: Methodology. Qi Liu: Investigation. Xiaoyu Shen: Software.

Declaration of Competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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