Application of high-resolution spectral analysis for identifying faults in induction motors by means of sound

Induction motors are critical components for most industries. Induction motor failures may yield an unexpected interruption at the industry plant. Several conventional vibration and current analysis techniques exist by which certain faults in rotating machinery can be identified. Ever since the first motor was built, plant personnel have listened to the noises emanating from machines; with enough experience, a listener may make a fairly accurate estimate of the condition of a machine. Although there are several works that deal with vibration and current analysis for monitoring and detection of faults in induction motors, the analysis of sound signals has not been sufficiently explored as an alternative non-invasive monitoring technique. The contribution of this investigation is the development of a condition monitoring strategy than can make a reliable assessment of the presence of specific fault condition in an induction motor with a single fault present through the analysis of a sound signal. The proposed methodology is based on the multiple-signal classification algorithm for high-resolution spectral analysis. Results show that the proposed methodology of sound analysis could improve standard techniques for induction motor fault detection, enhancing detectability.