A Health Monitoring Method of Machine Tool Spindle Based on Multi-domain Analysis and Convolutional Neural Network

Xiaoming Wang; Daowen Wan; Yongmeng Liu; Dawei Wang; Zifei Cao; Junfeng Wang; Chuanzhi Sun; Dong Zhou; Xue Chen; Hongliang Ma

doi:10.1088/1742-6596/1877/1/012012

A Health Monitoring Method of Machine Tool Spindle Based on Multi-domain Analysis and Convolutional Neural Network

Xiaoming Wang
, Daowen Wan
, Yongmeng Liu
, Dawei Wang
, Zifei Cao
, Junfeng Wang
, Chuanzhi Sun
, Dong Zhou
, Xue Chen
, Hongliang Ma

School of Instrumentation Science and Engineering

Harbin Institute of Technology
Qiqihar Heavy CNC Equipment Corporation Limited

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

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.

Original language	English
Article number	012012
Journal	Journal of Physics: Conference Series
Volume	1877
Issue number	1
DOIs	https://doi.org/10.1088/1742-6596/1877/1/012012
State	Published - 19 Apr 2021
Event	4th International Conference on Aeronautical, Aerospace and Mechanical Engineering, AAME 2021 - Sanya, Virtual, China Duration: 26 Feb 2021 → 28 Feb 2021

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10.1088/1742-6596/1877/1/012012

Cite this

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title = "A Health Monitoring Method of Machine Tool Spindle Based on Multi-domain Analysis and Convolutional Neural Network",

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.",

author = "Xiaoming Wang and Daowen Wan and Yongmeng Liu and Dawei Wang and Zifei Cao and Junfeng Wang and Chuanzhi Sun and Dong Zhou and Xue Chen and Hongliang Ma",

note = "Publisher Copyright: {\textcopyright} Published under licence by IOP Publishing Ltd.; 4th International Conference on Aeronautical, Aerospace and Mechanical Engineering, AAME 2021 ; Conference date: 26-02-2021 Through 28-02-2021",

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TY - JOUR

T1 - A Health Monitoring Method of Machine Tool Spindle Based on Multi-domain Analysis and Convolutional Neural Network

AU - Wang, Xiaoming

AU - Wan, Daowen

AU - Liu, Yongmeng

AU - Wang, Dawei

AU - Cao, Zifei

AU - Wang, Junfeng

AU - Sun, Chuanzhi

AU - Zhou, Dong

AU - Chen, Xue

AU - Ma, Hongliang

PY - 2021/4/19

Y1 - 2021/4/19

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85104860376

U2 - 10.1088/1742-6596/1877/1/012012

DO - 10.1088/1742-6596/1877/1/012012

M3 - 会议文章

AN - SCOPUS:85104860376

SN - 1742-6588

VL - 1877

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

IS - 1

M1 - 012012

T2 - 4th International Conference on Aeronautical, Aerospace and Mechanical Engineering, AAME 2021

Y2 - 26 February 2021 through 28 February 2021

ER -

A Health Monitoring Method of Machine Tool Spindle Based on Multi-domain Analysis and Convolutional Neural Network

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