Comparison of Spectral Estimation Techniques Applied to Induction Motor Broken Bars Detection

This paper presents a performance comparison among some of the most effective spectral estimation techniques applied to the fault diagnosis of induction machines. The diagnostic test is based on the analysis of the current space vector during motor starting via short-time analysis, using a sliding window and different spectral estimation algorithms. Differently from most of the diagnostic techniques already proposed in the technical literature, the approach, presented in this work, is effective regardless of the load condition of the machine. Algorithms based on the FFT or optimal band-pass filters (nonparametric methods), on the estimation of a linear time-invariant model of the signal (parametric methods), and on the eigenanalysis of the autocorrelation matrix (high-resolution methods) have been used to process the motor current space-vector. Experiments prove that both parametric and high-resolution methods overcome the FFT-based approaches, keep only the principal frequency components of the signal and decrease the noise influence, thus permitting a better interpretation of the current vector spectrum and an automatic fault detection procedure