Subspace-Based Identification of Acoustic Noise Spectra in Induction Motors

Abstract

In this paper, we study the identification of acoustic noise spectra in induction motors by using a recently developed frequency-domain cross-power spectrum estimation algorithm. This algorithm is a noniterative high-resolution spectral estimator. In a test rig, from multiple experiments sound data are collected by an array of five-microphones placed hemispherically around motors in a reverberant and noisy room. In order to explore the issue of assembly micromisalignments, each motor is removed from the test rig and then replaced, after which the experiment is then repeated. The identification algorithm is used to detect changes in acoustic noise spectra of induction motors due to mechanical and electrical faults most frequently encountered in industry. Not only the autopower spectra of the individual microphones, but also the cross-power spectra of the microphone pairs are estimated. As a byproduct, it is demonstrated that one microphone is sufficient to identify noise spectra. 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.