Application Research of Classical and Advanced Filtering Techniques in Condition Monitoring and Fault Diagnosis

Abstract

Filtering techniques have been successfully used in the field of condition monitoring and fault diagnosis. By the use of signal filtering, the impending system fault can be revealed effectively to prevent the system from malfunction. This paper discusses recent progress of classical and advanced filters for the condition monitoring and fault diagnosis. Excellent work is introduced from the aspects of the Wiener filtering algorithms, the Kalman filtering algorithms and the novel self-adaptive filtering algorithms. An overview of some promising algorithms for enhancement of filtering performance is presented. The review result suggests that the intelligent information fusion based fault diagnosis expert system with self-learning and self-updating abilities is the future research trend for the condition monitoring fault diagnosis based on filtering theory.