Feature selection and classification of mechanical fault of an induction motor using random forest classifier

摘要

Summary Fault detection and diagnosis is the most important technology in condition-based maintenance (CBM) system for rotating machinery. This paper experimentally explores the development of a random forest (RF) classifier, a recently emerged machine learning technique, for multi-class mechanical fault diagnosis in bearing of an induction motor. Firstly, the vibration signals are collected from the bearing using accelerometer sensor. Parameters from the vibration signal are extracted in the form of statistical features and used as input feature for the classification problem. These features are classified through RF classifiers for four class problems. The prime objective of this paper is to evaluate effectiveness of random forest classifier on bearing fault diagnosis. The obtained results compared with the existing artificial intelligence techniques, neural network. The analysis of results shows the better performance and higher accuracy than the well existing techniques.