Research on Sparsity Measures for Rotating Machinery Health Monitoring

TL;DR

This paper analyzes existing sparsity measures for machinery health monitoring, introduces a general framework for designing new indexes, and validates their effectiveness using real-world datasets, improving fault detection and degradation assessment.

Contribution

It provides a comprehensive analysis of smoothness index and negative entropy, and proposes a unified paradigm for creating new sparsity-based health indexes.

Findings

New indexes effectively describe degradation trends.

Indexes accurately determine first fault occurrence.

Proposed indexes outperform traditional measures in experiments.

Abstract

Machine health management is one of the main research contents of PHM technology, which aims to monitor the health states of machines online and evaluate degradation stages through real-time sensor data. In recent years, classic sparsity measures such as kurtosis, Lp/Lq norm, pq-mean, smoothness index, negative entropy, and Gini index have been widely used to characterize the impulsivity of repetitive transients. Since smoothness index and negative entropy were proposed, the sparse properties have not been fully analyzed. The first work of this paper is to analyze six properties of smoothness index and negative entropy. In addition, this paper conducts a thorough investigation on multivariate power average function and finds that existing classical sparsity measures can be respectively reformulated as the ratio of multivariate power mean functions (MPMFs). Finally, a general paradigm of index design are proposed for the expansion of sparsity measures family, and several newly designed dimensionless health indexes are given as examples. Two different run-to-failure bearing datasets were used to analyze and validate the capabilities and advantages of the newly designed health indexes. Experimental results prove that the newly designed health indexes show good performance in terms of monotonic degradation description, first fault occurrence time determination and degradation state assessment.