Remaining useful life estimation for micro switches of railway vehicles

Abstract Micro switches are widely used in modern control systems, and the reliability of each micro switch may be of great significance to the whole system. Remaining useful life of micro switches is an essential index evaluating their reliability in operation, and the real-time estimation can prevent system failure in a more controllable manner. In this paper, Bayesian updating and expectation maximization are combined to achieve the remaining useful life estimation. Additionally, strong tracking filtering technique is employed to improve the adaptive update capability. The effectiveness of the method is illustrated from an experiment of a micro switch for rail vehicles.

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