Multi-label Classification for Fault Diagnosis of Rotating Electrical Machines

TL;DR

This paper introduces a multi-label classification approach for diagnosing multiple concurrent faults in rotating electrical machines, improving early fault detection amidst noise and overlapping features using machine learning techniques.

Contribution

It proposes a novel multi-label classification methodology for simultaneous fault diagnosis and severity assessment in electrical machines under noisy conditions.

Findings

Multi-label models outperform traditional methods in fault detection accuracy.

The approach effectively detects multiple faults like unbalance and misalignment.

Experimental validation confirms robustness under noisy and diverse fault scenarios.

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.