article · Discover Electronics
Fault diagnosis in three-phase induction motors is critical for maintaining equipment performance and operational reliability. Key mechanical and electrical issues affecting these machines include bearing faults, broken rotor bars, and eccentricity. Identifying these issues relies on a variety of conventional and advanced monitoring methods. Among the primary diagnostic approaches are Motor Current Signature Analysis, partial discharge testing, and artificial intelligence frameworks. Early detection mechanisms play a crucial role in preventing severe machinery failures, allowing operators and engineers to maintain optimal operating efficiency. By comparing traditional diagnostic tools alongside advanced artificial intelligence solutions, current practices offer a structured foundation for building effective maintenance strategies. These diagnostic methodologies support maintenance practitioners and engineering researchers in understanding failure modes, aiding informed decision-making to prolong the working life and reliability of three-phase induction machinery across industrial environments.
Three-phase induction motors power numerous essential industrial processes, making unexpected breakdowns costly and disruptive. Understanding how to detect faults like bearing failures and broken rotor bars before complete breakdown occurs ensures continuous operations. By tracking machine health through current analysis, discharge testing, and artificial intelligence, industry operators can make informed maintenance choices, cut unexpected downtime, and improve overall equipment efficiency.
The review evaluates diagnostic methods relevant to industrial motor maintenance, highlighting tools like Motor Current Signature Analysis and artificial intelligence systems. These approaches are aimed at maintenance practitioners seeking informed predictive maintenance strategies to protect machine efficiency. Because the abstract outlines a review of existing traditional and advanced methodologies rather than a specific new prototype or tested tool, the work serves as an informational resource rather than a direct commercialisation pipeline.
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Abstract Delving into fault diagnosis techniques for electrical machines, this comprehensive review focuses on three-phase induction motors. It covers various fault types including eccentricity, broken rotor bars, and bearing faults, discussing techniques such as Motor Current Signature Analysis (MCSA), partial discharge testing, and AI-based approaches. Providing insights into fault detection mechanisms, it emphasizes early identification for optimal machine performance and reliability. With a detailed examination of both traditional and advanced methods, the review serves as a valuable resource for practitioners and researchers in the field, facilitating informed decision-making for maintenance strategies and enhancing machine efficiency.
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DOI: 10.1007/s44291-024-00012-3
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