Random Forest 기반의 결함진단 알고리듬과 유도전동기에서의 응용

Abstract

In this thesis, ensemble theory is represented as a powerful and effective methodology. This theory plays the role as tache between the Classification and Regression Tree (CART) and machine fault diagnosis theory. This combination shows its highlight on the induction motor faults diagnosis which is name Random Forest Algorithm. This is 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 of induction motors faults diagnosis. Experiment 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, and valid this optimized RF algorithm’s enhanced performance by the same experiment data. It is the evidence that RF based diagnosis methodology can touch more accurate outcome by combining with other optimization method.