A simplified convolutional sparse filter for impulsive signature enhancement and its application to the prognostic of rotating machinery

TL;DR

This paper introduces a novel impulsive signature enhancement algorithm tailored for rotating machinery, improving data quality for prognostics and health management through scale-invariant feature extraction and experimental validation.

Contribution

A new impulsive signature enhancement algorithm is proposed, emphasizing scale-invariant features to improve machinery prognostics and health management.

Findings

Effective data quality enhancement for failure detection

Improved diagnosis accuracy demonstrated in experiments

Algorithm outperforms existing methods in robustness

Abstract

Impulsive signature enhancement (ISE) is an important topic in the monitoring of rotating machinery and many different methods have been proposed. Even though, the topic of how to leverage these ISE techniques to improve the data quality in terms of prognostics and health management (PHM) still needs to be investigated. In this work, a systematic view for data quality enhancement is presented. The data quality issues for the prognostics and health management (PHM) of rotating machinery are identified, and the major steps to enhance data quality are organized. Based on this, a novel ISE algorithm is originally proposed, the importance of extracting scale invariant features are explained, and also related features are proposed for the PHM of rotating machinery. In order to demonstrate the effectiveness of the novelties, two experimental studies are presented. The final results indicate that the proposed method can be effectively employed to enhance the data quality for machine failure detection and diagnosis.