title: Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation.
abstract: Condition monitoring of electric drives is of paramount importance since it contributes to enhance the system reliability and availability. Moreover, the knowledge about the fault mode behavior is extremely important in order to improve system protection and fault-tolerant control. Fault detection and diagnosis in squirrel cage induction machines based on motor current signature analysis (MCSA) has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. This paper focuses on the application of MCSA for the detection of abnormal mechanical conditions that may lead to induction machines failure. In fact, this paper is devoted to the detection of single-point defects in bearings based on parametric spectral estimation. A multi-dimensional MUSIC (MD MUSIC) algorithm has been developed for bearing faults detection based on bearing faults characteristic frequencies. This method has been used to estimate the fundamental frequency and the fault related frequency. Then, an amplitude estimator of the fault characteristic frequencies has been proposed and fault indicator has been derived for fault severity measurement. The proposed bearing faults detection approach is assessed using simulated stator currents data, issued from a coupled electromagnetic circuits approach for air-gap eccentricity emulating bearing faults. Then, experimental data are used for validation purposes.
authors.affiliation: ISEN Brest, FRE CNRS 3744 IRDL, 29200 Brest, France. Electronic address: elbouchikhi@isen-bretagne.fr.
authors.forename: Elhoussin
authors.initials: E
authors.lastname: Elbouchikhi
authors.affiliation: University of Brest, FRE CNRS 3744 IRDL, Rue de Kergoat, CS 93837, 29238 Brest Cedex 03, France. Electronic address: Vincent.Choqueuse@univ-brest.fr.
authors.forename: Vincent
authors.initials: V
authors.lastname: Choqueuse
authors.affiliation: University of Brest, FRE CNRS 3744 IRDL, Rue de Kergoat, CS 93837, 29238 Brest Cedex 03, France; Shanghai Maritime University, 201306 Shanghai, China. Electronic address: Mohamed.Benbouzid@univ-brest.fr.
authors.forename: Mohamed
authors.initials: M
authors.lastname: Benbouzid
keywords: Bearing fault detection; Condition monitoring; Induction machine; MD MUSIC; Maximum likelihood estimation; Signal processing