Monitoring of induction Machines by maximum covariance method for frequency tracking

Abstract

Motor current signature analysis (MCSA) has been widely investigated in order to monitor fault conditions of induction machines. On the other hand several solutions were proposed for the detection of rotor speed of induction motor for sensorless control. Another deeply investigated field of research is the detection of supply frequency of power lines, for the diagnosis of the distribution network. A common root of these three key topics is the need of accurately stating specific spectrum frequencies. Several techniques were presented in the literature in order to perform accurate tracking of frequencies for different purposes. They are modified versions of the traditional discrete Fourier transformation (DFT), or novel spectrum estimation techniques. This paper presents a novel procedure based on the statistical analysis of the current signal in the time domain, referred to as maximum covariance method for frequency tracking (MCMFT), that allows to obtain high frequency resolution accuracy independently of the sampling frequency and of the time acquisition period. Therefore those spectrum lines related to supply frequency or to slip can be detected with extreme accuracy within a wide range of sampled data conditions. Then either an accurate diagnosis of the machine electric faults or sensorless control, or distribution network diagnosis can be performed. Comparison between the proposed method and the literature are reported, in order to critically analyze its performances. An induction machine with two artificially broken bars was used for the experiments.