Health status assessment of warship power equipment

Aiming at the problems of complex data and difficult health status assessment of ship power plant, this paper summarizes the research status at home and abroad, and analyzes the current equipment health status assessment methods, including physical model-based methods, knowledge-based methods and data-driven methods. Through the research, it is found that in the process of equipment health status assessment, three steps can be followed. First, the classification of dynamic system conditions based on unsupervised learning algorithm; The second is the regression analysis of the operating state based on the operating data of the dynamic system. Third, the research of power system health assessment based on differential analysis modeling can provide theoretical support for equipment health state assessment.

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Published
2024-12-19
DOI
https://doi.org/10.1117/12.3056212
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
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Health status assessment of warship power equipment

Li Zhang, Yin Dongliang, Chao Liu
Machine Fault Diagnosis Techniques
article

Health status assessment of warship power equipment

Li Zhang, Yin Dongliang, Chao Liu
article en

Abstract

Aiming at the problems of complex data and difficult health status assessment of ship power plant, this paper summarizes the research status at home and abroad, and analyzes the current equipment health status assessment methods, including physical model-based methods, knowledge-based methods and data-driven methods. Through the research, it is found that in the process of equipment health status assessment, three steps can be followed. First, the classification of dynamic system conditions based on unsupervised learning algorithm; The second is the regression analysis of the operating state based on the operating data of the dynamic system. Third, the research of power system health assessment based on differential analysis modeling can provide theoretical support for equipment health state assessment.

Naval University of Engineering (CN)
Openalex Percentile: Top 72%
Machine Fault Diagnosis Techniques
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