Robust condition monitoring for early detection of broken rotor bars in induction motors

Cage rotor induction machines are a vital component in many industrial processes and other economic sectors where an unpredicted shutdown can be very costly. Therefore, an adequate warning of incipient faults via condition monitoring is very interesting for these applications. In this paper, we propose a condition monitoring technique based on robust statistical tools to detect incipient faults in induction motors related to asymmetries in the rotor cage. This technique uses the Fast Fourier Transform to obtain the spectrum of the motor line current and then, a multiresolution technique using wavelet functions is applied to this spectrum in order to detect significant peaks and to measure the height of these peaks with respect to the “baseline” signal. Finally, a Quality Control approach based on robust multivariate control charts is applied to detect a progressive deterioration of the rotor cage. To show the usefulness of the proposed method, we present a case-study in which a cage fault condition was provoked by drilling a hole in one of the bars of an induction motor. The different fault conditions were obtained by progressively making the hole deeper and a great deal of laboratory tests were performed for each fault condition.