A support vector machine based fault diagnostics of Induction motors for practical situation of multi-sensor limited data case

Abstract

This paper is focused on the development of a new SVM based fault diagnosis methodology for Induction Motors (IMs) in practical situation of limited data or information case. This work is of practical significance as the data is not always available at all operating conditions or in other word the data is limited for fault diagnosis. The vibration and current signals have been considered in this study since these signals are the most efficient to detect the electrical and mechanical faults as well as their severity in IMs. Ten different IM fault conditions, e.g. five electrical faults (i.e., the broken rotor bar, phase unbalance fault with two severity levels, and stator winding fault with two severity levels) and four mechanical faults (i.e., the bearing fault, bowed rotor, unbalanced rotor and misaligned rotor) with a healthy motor are considered. In order to develop the proposed diagnostic methodology, first the vibration and current data are acquired at various IM working conditions (i.e., the load and the speed) from an experimental setup. A number of fault features are then extracted using the raw time domain data. Further these features are fed into the SVM as inputs to diagnose IM faults. The fault diagnosis is first developed and checked for same speed cases, for the situation when the data is available at required loads and speeds. Diagnosis is then extended for an intermediate speed case and an intermediate load case for taking care of the situations, where the required information or data is not readily available at required speeds and loads. The aim of this study is to check the prediction performance of the proposed diagnostic methodology for the limited information. This study is very significant for the practical point of view since symptoms database are not available for all the cases or sometimes difficult to obtain at all IM working conditions. To investigate the robustness of the present diagnostic methodology, it is checked at several working conditions. It is found that the performance is very effective for the same speed and load cases, and very encouraging for the intermediate speed case and the intermediate load case.