Motor current analysis has been utilized for fault detection and diagnosis in induction motors. The analysis of the current spectrum is normally done using Fast Fourier Transform (FFT). The main shortcoming of the FFT approach is the need to have higher resolution (high sampling frequency and large data). Moreover, it requires further post-processing to extract fault features in particular in noisy environments and with speed/load fluctuations.This paper presents a new algorithm to detect two types of faults in bearings and broken rotor bars in induction machines. The proposed method is based on Rank Revealing QR Factorization method (RRQR) in conjunction with MUSIC algorithm. The stator current measurements are considered for faults detection. The key points of the proposed method are the following: 1) It gives precise information about numerical rank and null space which results in accurate estimation for fundamental frequency and fault related frequencies. 2) it detects the frequency components such as fundamental frequency, harmonics and inter-harmonics components. 3) it does not require the complex eigenvalue decomposition (EVD) of the cross-spectral matrix (CSM) or singular value decomposition (SVD) of measured data. The experimental results demonstrate the effectiveness of the proposed method in detecting the faults and identifying their severity.
