Advanced diagnosis of outer cage damage in double-squirrel-cage induction motors under time-varying conditions based on wavelet analysis

It is known that classical fast-Fourier-transform-based steady-state spectrum analysis, such as motor current signature analysis, may fail to detect outer cage damage in double-squirrel-cage induction motors. This is because the magnitude of the rotor fault frequency components (RFFCs) in the current spectrum of faulty motors is small, due to the low-magnitude current circulation in the outer cage under a steady-state operation. The probability of misdetection is higher in time-varying load applications, such as conveyor belts, pulverizers, etc., for which double-cage motors are frequently employed. In case of load variation, the small RFFCs are spread in a bandwidth proportional to the speed variation, which makes them even more difficult to detect. A diagnosis method based on discrete wavelet transform and optimized for sensitive detection under transient operating conditions is proposed in this paper. An experimental study on a custom-built fabricated Cu double-cage-rotor induction motor shows that the proposed method can provide improved detection of outer cage faults particularly used in time-varying load applications.