Open AccessProposal of noninvasive failure diagnosis of electrical motor using googlenet
Author(s) -
Hoang Van Tung,
Nguyễn Văn Khánh,
Nguyễn Chí Ngôn
Publication year - 2021
Publication title -
technical education science/giáo dục kỹ thuật
Language(s) - English
Resource type - Journals
eISSN - 2615-9740
pISSN - 1859-1272
DOI - 10.54644/jte.66.2021.1070
Subject(s) - convolutional neural network , fault (geology) , induction motor , computer science , bearing (navigation) , signal (programming language) , artificial intelligence , condition monitoring , wavelet , pattern recognition (psychology) , deep learning , engineering , electrical engineering , voltage , seismology , programming language , geology
Fault diagnosis is a useful tool that reduces system maintenance risks and costs. However, data related to the system's nominal and fault operating behavior is often not collected and stored adequately, it is difficult to identify and suggest automated fault detection methods. This study proposes a solution to apply deep learning technique on the convolutional neural network (CNN) to identify some common errors on induction motors based on operation sound. The opreration sound signal emitted from on a 0.37 kW two-pole induction motor is collected in some cases such as normal operation, phase loss, phase difference and bearing breakage. Their 2-D scalogram images are analyzed by continuous Wavelet transformation which is used to train and evaluate the deep learning CNN (i.e. GoogLeNet) to identify the above faults. Experimental results show that this method can diagnose induction motor faults with accuracy up to 98.8%.