A High-Resolution Frequency Estimation Method for Three-Phase Induction Machine Fault Detection

Abstract

Fault detection in alternating-current electrical machines that is based on frequency analysis of stator current has been the interest of many researchers. Several frequency estimation techniques have been developed and are used to help the induction machine fault detection and diagnosis. This paper presents a technique to improve the fault detection technique by using the classical multiple signal classification (MUSIC) method. This method is a powerful tool that extracts meaningful frequencies from the signal, and it has been widely used in different areas, which include electrical machines. In the proposed application, the fault sensitive frequencies have to be found in the stator current signature. They are numerous in a given frequency range, and they are affected by the signal-to-noise ratio. Then, the MUSIC method takes a long computation time to find many frequencies by increasing the dimension of the autocorrelation matrix. To solve this problem, an algorithm that is based on zooming in a specific frequency range is proposed with MUSIC in order to improve the performances of frequency extraction. Moreover, the method is integrated as a part of MUSIC to estimate the frequency signal dimension order based on classification of autocorrelation matrix eigenvalues. The proposed algorithm has been applied to detect a rotor broken bar fault in a three-phase squirrel-cage induction machine under different loads and in steady-state condition.

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

“…MCSA includes parametric analysis methods (signal analysis as a function of time, frequency spectrum), non-parametric methods (techniques such as Fast Fourier Transform) and high-resolution or sub-space methods (these obtain the autocorrelation matrix, and their eigenvalues separate the subspaces corresponding to the signal and noise). When the signal is not stationary, diagnostic techniques are mainly based on higher order spectral analysis (time-frequency), such as bispectrum and trispectrum, and higher order statistical methods to obtain the density of the frequency spectrum [34,38].…”

Modeling of Double Star Induction Motors with Dynamic Eccentricity

“…Like in the three-phase case, (DSIM) may be the subject of various defects like broken rotor bars [2], bearing defects [ 3], loss of phase [4], eccentricity problems [5] and stator inter-turn short circuit [6]. Rotor eccentricity is defined as a condition of the asymmetric air gap that exists between the stator and rotor.…”

Induction machine fault detection enhancement using a stator current high resolution spectrum

“…In order to overcome the poor resolution of these techniques, high resolution techniques have been proposed which require an a priori knowledge about the signal such as MUSIC and ESPRIT [13]- [16]. In non stationary environment, more so phisticated techniques have been investigated such as time frequency representations [17], [18] and time-scale techniques [19].…”