High-Resolution Parameter Estimation Method to Identify Broken Rotor Bar Faults in Induction Motors

• Published in: IEEE transactions on industrial electronics (1982) 2013-09, Vol.60 (9), p.4103-4117
• Main Authors: Kim, Yong-Hwa, Youn, Young-Woo, Hwang, Don-Ha, Sun, Jong-Ho, Kang, Dong-Sik
• Format: Article
• Language: English
• Subjects: Algorithms; Amplitude estimation; Amplitudes; broken rotor bar (BRB); current measurement; Eigenvalues and eigenfunctions; Estimators; fault detection; Fault diagnosis; Faults; Frequency estimation; Harmonic analysis; Harmonics; Induction motors; Modules; Multiple signal classification; Rotors; signal analysis; signal processing; Studies; Time frequency analysis
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2012.2227912

The classical multiple signal classification (MUSIC) method has been widely used in induction machine fault detection and diagnosis. This method can extract meaningful frequencies but cannot give accurate amplitude information of fault harmonics. In this paper, we propose a new frequency analysis of stator current to estimate fault-sensitive frequencies and their amplitudes for broken rotor bars (BRBs). The proposed method employs a frequency estimator, an amplitude estimator, and a fault decision module. The frequency estimator is implemented by a zoom technique and a high-resolution analysis technique known as the estimation of signal parameters via rotational invariance techniques, which can extract frequencies accurately. For the amplitude estimator, a least squares estimator is derived to obtain amplitudes of fault harmonics, without frequency leakage. In the fault decision module, the fault diagnosis index from the amplitude estimator is used depending on the load conditions of the induction motors. The fault index and corresponding threshold are optimized by using the false alarm and detection probabilities. Experimental results obtained from induction motors show that the proposed diagnosis algorithm is capable of detecting BRB faults with an accuracy that is superior to the zoom-based MUSIC algorithm.