Open AccessOptimised approach of feature selection based on genetic and binary state transition algorithm in the classification of bearing fault in BLDC motor
Author(s) -
Lee ChunYao,
Le TruongAn
Publication year - 2020
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.815
H-Index - 97
eISSN - 1751-8679
pISSN - 1751-8660
DOI - 10.1049/iet-epa.2020.0168
Subject(s) - algorithm , feature selection , binary number , bearing (navigation) , genetic algorithm , artificial neural network , support vector machine , computer science , pattern recognition (psychology) , artificial intelligence , feature vector , fault detection and isolation , engineering , mathematics , machine learning , arithmetic , actuator
This study represents an effective approach for detection and classification of bearing faults in brushless DC (BLDC) motors based on hall‐sensor signal analysis. The envelope analysis and Hilbert–Huang transform are used to extract features from the time and frequency domains of each signal. A new feature selection technique is proposed based on the combination of the genetic algorithm strength and the advantage of the binary state transition algorithm. The genetic algorithm explores search space through cross‐over operator while the binary state transition algorithm is based on four special transformation operators in the local exploitation capabilities. The artificial neural network and support vector machine are used as the classifier. Each model is separately analysed and compared, leading to a high possibility to distinguish the bearing faults.