Advances in Sparsity Measures and Complexity Measures for Machine Health Monitoring

doi:10.3901/JME.2025.01.123

Abstract

Abstract: Machine health monitoring plays a crucial role in ensuring machine health operation, and one of its core technologies is fault feature extraction. Due to the sparsity of mechanical fault signals in both the time and frequency domains, sparsity and complexity measures based fault feature extraction is widely used in machine health monitoring. In the past years, in the domain of machine health monitoring, research on sparsity and complexity measures was mainly conducted through experimental studies, lacking enough theoretical supports. This article mainly summarizes and reviews some new progresses in the theoretical foundation research of sparsity and complexity measures in recent years, and elaborates on their integration with machine health monitoring, which is beneficial for researchers in the field of machine health monitoring to fully understand: (1) The generalized mathematical framework of sparsity measures; (2) Constructing a new type of fault feature statistics based on the ratio of quasi-arithmetic means; (3) Construction of new sparsity measures and fault feature statistics; (4) Sparsity measure performance comparison; (5) Improved sparsity measures; (6) The difference between sparsity and complexity measures. The application effects of sparsity and complexity measures are compared through case studies. Finally, research outlook provides some future development directions of sparsity and complexity measures in the field of machine health monitoring.

Key words: sparsity measure, complexity measure, kurtosis, entropy, Gini index, correlation dimension, approximate entropy, fuzzy entropy, sample entropy, machine health monitoring

CLC Number:

TH17

WANG Dong, HOU Bingchang, WANG Yuting, XIA Tangbin, PENG Zhike, XI Lifeng. Advances in Sparsity Measures and Complexity Measures for Machine Health Monitoring[J]. Journal of Mechanical Engineering, 2025, 61(1): 123-139.

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