A Health Monitoring Method of Machine Tool Spindle Based on Multi-domain Analysis and Convolutional Neural Network. Issue 1 (April 2021)

• Record Type: Journal Article
• Authors: Wang, Xiaoming; Wan, Daowen; Liu, Yongmeng; Wang, Dawei; Cao, Zifei; Wang, Junfeng; Sun, Chuanzhi; Zhou, Dong; Chen, Xue; Ma, Hongliang
• Is Part Of: Journal of physics. Volume 1877:Issue 1(2021)
• Publication Date: 2021-04
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1877/1/012012
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: This paper presents a method for monitoring machine tool spindle health based on multi-domain analysis and convolutional neural network. By extracting the characteristics of data from time domain, frequency domain and time frequency domain, the data information is fully excavated automatically. Meanwhile, Convolutional neural network is used to realize fault diagnosis and classification. As a result, the fault classification results reveal high accuracy by datasheet. It proves that the method in this paper which has strong practicability and application value.