Open AccessPerformance Analysis of Different Machine Learning Algorithms for Identifying and Classifying the Failures of Traction Motors
Author(s) -
Xiaoyu Xian,
Haichuan Tang,
Tian Yin,
Qi Liu,
Ye Fan
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2095/1/012058
Subject(s) - machine learning , artificial intelligence , computer science , fault (geology) , artificial neural network , support vector machine , traction motor , random forest , identification (biology) , binary classification , algorithm , fault detection and isolation , traction (geology) , engineering , mechanical engineering , botany , seismology , actuator , biology , geology
This paper addresses electric motor fault diagnosis using supervised machine learning classification. A total of 15 distinct fault types are classified and multilabel strategies are used to classify concurrent faults. we explored, developed, and compared the performance of different types of binary (fault/non-fault), multi-class (fault type) and multi-label (single fault versus combination fault) classifiers. To evaluate the effectiveness of fault identification and classification, we used different supervised machine learning methods, including Random forest classification, support vector machine and neural network classification. Through experiment, we compared these methods over 4 classification regimes and finally summarize the most suitable machine learning algorithms for different aspects of health diagnosis in traction motors area.