A novel customised load adaptive framework for induction motor fault classification utilising MFPT bearing dataset

Hejazi, Shahd Ziad, Packianather, Michael ORCID: https://orcid.org/0000-0002-9436-8206 and Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 2024. A novel customised load adaptive framework for induction motor fault classification utilising MFPT bearing dataset. Machines 12 (1) , 44. 10.3390/machines12010044

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Abstract

This research presents a novel Customised Load Adaptive Framework (CLAF) for fault classification in Induction Motors (IMs), utilising 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 customisation. Through a meticulous two-phase process, it unveils load-dependent fault subclasses that have not been readily identified in traditional approaches. Additionally, new classes are created to accommodate the dataset’s unique characteristics. Phase 1 involves exploring load-dependent patterns in time and frequency domain features using one-way Analysis of Variance (ANOVA) ranking and validation via bagged tree classifiers. In Phase 2, CLAF is applied to identify mild, moderate, and severe load-dependent fault subclasses through optimal Continuous Wavelet Transform (CWT) selection through Wavelet Singular Entropy (WSE) and CWT energy analysis. The results are compelling, with a 96.3% classification accuracy achieved when employing a Wide Neural Network to classify proposed load-dependent fault subclasses. This underscores the practical value of CLAF in enhancing fault diagnosis in IMs and its future potential in advancing IM condition monitoring.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	MDPI
ISSN:	2075-1702
Date of First Compliant Deposit:	9 February 2024
Date of Acceptance:	3 January 2024
Last Modified:	09 Feb 2024 16:45
URI:	https://orca.cardiff.ac.uk/id/eprint/166161

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