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