Preprint Article Version 1 This version is not peer-reviewed

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

Version 1 : Received: 31 July 2019 / Approved: 2 August 2019 / Online: 2 August 2019 (10:44:06 CEST)

How to cite: Dineva, A.; Mosavi, A.; Gyimesi, M.; Vajda, I. Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines. Preprints 2019, 2019080029 (doi: 10.20944/preprints201908.0029.v1). Dineva, A.; Mosavi, A.; Gyimesi, M.; Vajda, I. Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines. Preprints 2019, 2019080029 (doi: 10.20944/preprints201908.0029.v1).

Abstract

Primary importance is devoted to Fault Detection and Diagnosis (FDI) of electrical machine and drive systems in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. The contribution of this work is to propose a novel methodology using multi-label classification method for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. Performance of various multi-label classification models are compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

Subject Areas

multiple fault detection; rotating electrical machines; drive systems; multi-label classification; machine learning; fault severity; fault classifiers

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