A Novel Customised Load Adaptive Framework for Induction Motor Fault Classification Utilizing MFPT Bearing Dataset
Shahd Ziad M Hejazi,
Michael Packianather,
Ying Liu
Abstract:This research presents a novel Customised Load Adaptive Framework (CLAF) for fault classifica-tion in Induction Motors (IMs), utilizing the Machinery Fault Prevention Technology (MFPT) Bearing Dataset. CLAF represents a pioneering approach that extends traditional fault classification methodologies by accounting for load variations and dataset customization. Through a meticulous two-phase process, it unveils load-dependent fault subclasses that have not been readily identified in traditional approaches. Additi… Show more
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