Second generation wavelet transform for incipient fault detection in double cage induction motors [articol]

Abstract

An increasing demand of availability improvement of industrial processes has been expressed in the last decades. Therefore, the fault detection and diagnosis has become considered indispensable to ensure high performances of the plant operation. Induction motors have dominated among all industrial drives. Their Condition Monitoring (CM) has therefore become critical. Despite their recurrent use, the CM of double cage induction motors (DC-IM) has not received sufficient research focus. This paper is inspired from the advanced signal processing techniques in order to diagnose one of the critical and most hardly detected faults in DC-IM. Actually, the challenge in DC-IM is the detection of the outer cage’s bar fault at an early stage. This study proposes a novel solution of this issue based on the Information Entropy of the Second Generation Wavelet Transform (SGWT). Using the Motor Current Signature Analysis, several rotor conditions including the incipient outer cage’s bar fault are analyzed under different severities and variable load levels. The results concluded by the experiments demonstrate the competence of this approach.