Wavelet entropy to estimate the winding insulation healthiness in induction motors

Abstract

Induction motors are fundamental elements in the industry field since they perform several tasks under a broad variety of conditions, a situation that affects its performance making them susceptible to fail despite its robustness. Over several decades, many techniques and methodologies to assess the healthiness state of electrical motors have been proposed and implemented successfully. In this regard new advances in the signal processing field has taken a great importance since these emerging tools are used to accomplish fault diagnosis tasks in order to increase the reliability of such processes. So, it is very important to explore the use of new signal processing techniques to detect and diagnose in a timely manner faults in electrical motors. In this work, it is proposed to use the wavelet entropy of the stray flux signal captured by a coil sensor to estimate the winding insulation status of induction motors, a very common failure presented on this type of drives, that if not attended on time, it can end in a catastrophic and irreversible fault in a matter of minutes. The proposal uses suitable time-frequency decomposition (TFD) tools to isolate the studied fault. The results obtained show that the methodology proposed here can be considered as an excellent alternative for estimating the winding insulation healthiness state of an induction motor online and efficiently, as well as being possible to implement it in a programmable logic device.