Modern Methods for Diagnosing Faults in Rotor Systems: A Comprehensive Review and Prospects for AI-Based Expert Systems
Source abstract
Abstract
Rotor systems are basic in power generation, mechanical, and many other energy equipment and industrial fields. The smooth operation of equipment is linked to the successful operation of technological processes and the safe operation of working equipment. Working conditions nowadays are characterized by intensive rotation speeds, complex structures, and dynamic loads, contributing to different mechanical faults. Detecting such defects in the preliminary stages is inadequate, which could lead to emergencies, high economic loss, and reduced equipment life. Several modern diagnosis methods are widely utilized to monitor the condition in real-time mode, such as vibration parameter analysis, temperature deviation analysis, acoustic emission analysis, and other operational parameter analyses, to avoid the possibility of rotor failure. Some techniques like the vibration signal analysis method, spectral analysis, thermography, ultrasound diagnosis, and machine learning algorithms for predicting failure are of particular interest among them. These techniques allow the defects to be identified immediately and constitute effective preventive maintenance plans, thus significantly enhancing the reliability and economic efficiency of the rotor system operations. This current work is devoted to studying modern diagnostic methods of rotor systems, considering the areas of their realization that are used. This review discusses the theory of the applied methods, advantages, limitations, and the perspective of their further development in innovation integration. It aims to critically analyze and comprehensively systematize methods for energy-consuming rotor equipment condition monitoring that will enhance the efficiency of managing technical conditions for the main components of modern energy systems.
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Research context
OpenAlex topics: Magnetic Bearings and Levitation Dynamics · Engineering Diagnostics and Reliability · Machine Fault Diagnosis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computational Mechanics Named After Volodymyr Martsynkovskyy, Sumy State University, 116, Kharkivska St., 40007 Sumy, Ukraine
Funding
Ministry of Education and Science of Ukraine, R&D project “Fulfillment of tasks of the perspective plan of development of a scientific direction “Technical Sciences” at Sumy State University”
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