Mathematical Morphology-Based Fault Diagnosis for Rotating Machinery: A Review

Rotating machinery is a crucial element of mechanical equipment, and during serving life, failures are inevitable due to human and non-human factors. Signal processing techniques serve as essential tools for diagnosing such faults. Among them, mathematical morphology (MM) has attracted considerable research interest in this domain owing to nonlinear filtering, simple computation rules and well-established theoretical foundation. Thus, numerous papers have been published in academic journals and conference proceedings. This review paper attempts to outline the morphological framework and to summarize these applications focusing on rolling element bearings and gears. Finally, it provides an analysis of the relevant discussions on MM, and suggests several potential prospects.

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Publication Details

Journal
Processes
Published
2026-02-13
DOI
https://doi.org/10.3390/pr14040650
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
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article

Mathematical Morphology-Based Fault Diagnosis for Rotating Machinery: A Review

Tingkai Gong, Xiaohui Yuan, Zhinong Li, Bing Ji
Processes
Machine Fault Diagnosis Techniques
article

Mathematical Morphology-Based Fault Diagnosis for Rotating Machinery: A Review

Tingkai Gong, Xiaohui Yuan, Zhinong Li, Bing Ji
article en

Abstract

Rotating machinery is a crucial element of mechanical equipment, and during serving life, failures are inevitable due to human and non-human factors. Signal processing techniques serve as essential tools for diagnosing such faults. Among them, mathematical morphology (MM) has attracted considerable research interest in this domain owing to nonlinear filtering, simple computation rules and well-established theoretical foundation. Thus, numerous papers have been published in academic journals and conference proceedings. This review paper attempts to outline the morphological framework and to summarize these applications focusing on rolling element bearings and gears. Finally, it provides an analysis of the relevant discussions on MM, and suggests several potential prospects.

ProcessesVol. 14(4)
Central South University (CN), Huazhong University of Science and Technology (CN), Nanchang Hangkong University (CN)
Openalex Percentile: Top 57%
Machine Fault Diagnosis Techniques
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