MUSIC-based Diagnosis Algorithm for Identifying Broken Rotor Bar Faults in Induction Motors Using Flux Signal

Abstract

-The diagnosis of motor failures using an on-line method has been the aim of many researchers and studies. Several spectral analysis techniques have been developed and are used to facilitate on-line diagnosis methods in industry. This paper discusses the first application of a motor flux spectral analysis to the identification of broken rotor bar (BRB) faults in induction motors using a multiple signal classification (MUSIC) technique as an on-line diagnosis method. The proposed method measures the leakage flux in the radial direction using a radial flux sensor which is designed as a search coil and is installed between stator slots. The MUSIC technique, which requires fewer number of data samples and has a higher detection accuracy than the traditional fast Fourier transform (FFT) method, then calculates the motor load condition and extracts any abnormal signals related to motor failures in order to identify BRB faults. Experimental results clearly demonstrate that the proposed method is a promising candidate for an on-line diagnosis method to detect motor failures.

Finite Element Analysis of Temperature Field of Motor Coil and Carbon Brush Couple

“…In the same time, the contact materials of contact couple will be oxidized in high temperature. Oxidative wear and abrasive wear have influence on the service life of the motor duet to materials wear [6][7][8].…”

Robust Diagnosis Algorithm for Identifying Broken Rotor Bar Faults in Induction Motors

“…The MUSIC algorithm with zooming methods was proposed to detect fault sensitive frequencies based on a frequency analysis of the stator current [15]. Using the motor leakage flux spectral analysis, the MUSIC is applied to identify BRB faults in induction motors [16]. The ESPRIT with the zooming technique has been proposed to estimate both the frequencies and amplitudes of BRB fault harmonics using the stator current analysis [17].…”

“…The MUSIC and ESPRIT compute an autocorrelation matrix of the signals, estimate a frequency signal dimension order (FSDO), use signal and noise subspaces, and convert the fault detection problem into a generalized eigenvalue problem [13][14][15][16][17]. The MUSIC was applied to estimate the harmonic components in order to improve induction motor fault detection [13].…”

“…Subspace methods such as a multiple signal classification (MUSIC) algorithm and an estimation of signal parameters via rotational invariance techniques (ESPRIT) have been applied to diagnose induction motor faults while overcoming the frequency resolution limitation of the FFT method in low signal-to-noise ratios (SNRs) [13][14][15][16][17]. The MUSIC and ESPRIT compute an autocorrelation matrix of the signals, estimate a frequency signal dimension order (FSDO), use signal and noise subspaces, and convert the fault detection problem into a generalized eigenvalue problem [13][14][15][16][17].…”

Intelligent System Design for Stator Windings Faults Diagnosis: Suitable for Maintenance Work

“…The magnetic flux spectrum technique is also an invasive method, once it studies the signals from sensors that need to be installed inside the motor, thus increasing the initial cost of the machine and making the system less robust [18]. The methods based on electrical current analysis are not invasive and they do not require system interruption [2,3,7,19].…”