A Comparison of Classifier Performance for Fault Diagnosis of Induction Motor using Multi-type Signals

Fault detection and diagnosis is the most important technology in condition-based maintenance (CBM) systems, which typically starts from collecting signatures of running machines by multiple sensors for subsequent accurate analysis. Recently, there has been an increasing requirement of selecting special sensors, which are cheap, robust, easily installed, and good classifiers that have accurate classification, stable performance, and short calculating time. This article carries out a comparative study of various classification algorithms for fault diagnosis of electric motors using different types of signals. The authors evaluate experimentally the relative performances of five classifiers using five types of steady-state signals based on three kinds of performance evaluation strategies: training-test, cross-validation, and similar measure. First, the raw signals are collected and features are extracted from the collected signals. Then, the extracted features are classified using the five classification algorithms. Next, an overall comparison of the five classifiers is described, and experiment results are discussed. Finally, conclusions are summarized and suggestions are offered.