Application of Random Forest Algorithm in Machine Fault Diagnosis

Abstract

The purpose of this paper is to present a methodology by which rotating machinery faults can be diagnosed. The proposed method is based on random forests algorithm (RF), a novel assemble classifier which builds a large amount of decision trees to improve on the single tree classifier. Although there are several existed techniques for faults diagnosis, such as artificial neural network, support vector machines etc, the research on RF is meaningful and necessary because of its fast executed speed, the characteristic of tree classifier, and high performance in machine faults diagnosis. Evaluation of the RF based method has been demonstrated by a case study on induction motors faults diagnosis. Experimental results indicate the validity and reliability of RF based fault diagnosis methodology. Furthermore, an optimized form of RF is also provided in this paper. We employ the genetic algorithm to strengthen RF. The capability of optimized RF algorithm is proved with same experimental data. It is the evidence that RF based diagnosis methodology can generate more accurate outcome by combining with other optimization method.