Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder

• 作者: Jiaxin Wu Lihua Wang Xiaoping Zhao Yonghong Zhang Yunqing Shi
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 工学
• 关键词: BIG data deep learning stacked DE-NOISING auto-encoder FOURIER TRANSFORM
• 年，卷（期）: 2018,（11）
• 页码范围: 223-242
• 页数: 20页
• 分类号: TP3

摘要

With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent.