Identification of acoustic spectra for fault detection in induction motors

In this paper, we study fault detection problem for induction motors by using a recently developed cross-power spectral density estimation algorithm from sound measurements. In a test rig, from multiple experiments the sound data were collected by an array of five-microphones placed hemispherically around motors in a reverberant and noisy room. After an experiment was performed, each motor was removed from the test rig and was reinstalled for the next experiment to verify the consistency of the experimental procedure. The mechanical and electrical faults frequently encountered in induction motors were isolated by the identification algorithm, which is a non-iterative high resolution spectral estimator. The estimated acoustic spectra, or more compactly statistics extracted from them, can be used in the development of preventive maintenance programs for induction motors in service.