定子
人工神经网络
转子(电动)
断层(地质)
感应电动机
计算机科学
模式识别(心理学)
人工智能
深度学习
方位(导航)
编码器
故障检测与隔离
功率(物理)
控制理论(社会学)
工程类
电气工程
执行机构
电压
物理
控制(管理)
量子力学
地震学
地质学
操作系统
作者
Saeid Jorkesh,Azadeh Gholaminejad,Javad Poshtan
标识
DOI:10.1177/09596518221125960
摘要
In this article, deep neural network and stacked sparse auto-encoder deep neural network performances in fault diagnosis are compared. Methods are employed experimentally for the detection and isolation of an induction motor’s condition (healthy, bearing outer race fault, stator winding short circuit, and rotor broken bar) in the presence of unbalanced power supply and pump dry running disturbances. Pre-processing and de-noising is performed on three-phase electrical current signals using fast Fourier transform and independence component analysis algorithm, respectively. Experimental results show that sparse auto-encoder deep neural network method has outperformed and diagnosed the aforementioned faults in the presence of disturbances with a highly reliable accuracy rate of 90.65%.
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