Tool remaining useful life prediction method based on LSTM under variable working conditions
Tool remaining useful life prediction is important to guarantee processing quality and efficient continuous production. Tool wear is directly related to the working conditions, showing a complex correlation and timing correlation, which makes it difficult to predict the tool remaining useful life un...
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| Published in | International journal of advanced manufacturing technology Vol. 104; no. 9-12; pp. 4715 - 4726 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer London
01.10.2019
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Tool remaining useful life prediction is important to guarantee processing quality and efficient continuous production. Tool wear is directly related to the working conditions, showing a complex correlation and timing correlation, which makes it difficult to predict the tool remaining useful life under variable conditions. In this paper, we seek to overcome this challenge. First, we establish the unified representation of the working condition, then extract the wear characteristics from the processing signal. The extracted wear features and corresponding working conditions are combined into an input matrix for predicting tool wear. Based on this, the complex spatio-temporal relationship under variable working conditions is captured. Finally, using the unique advantages of the long short-term memory (LSTM) model to solve complex correlation and memory accumulation effects, the tool remaining useful life prediction model under variable working conditions is established. An experiment illustrates the effectiveness of the proposed method. |
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| AbstractList | Tool remaining useful life prediction is important to guarantee processing quality and efficient continuous production. Tool wear is directly related to the working conditions, showing a complex correlation and timing correlation, which makes it difficult to predict the tool remaining useful life under variable conditions. In this paper, we seek to overcome this challenge. First, we establish the unified representation of the working condition, then extract the wear characteristics from the processing signal. The extracted wear features and corresponding working conditions are combined into an input matrix for predicting tool wear. Based on this, the complex spatio-temporal relationship under variable working conditions is captured. Finally, using the unique advantages of the long short-term memory (LSTM) model to solve complex correlation and memory accumulation effects, the tool remaining useful life prediction model under variable working conditions is established. An experiment illustrates the effectiveness of the proposed method. |
| Author | Zhao, Xu Zhou, Jing-Tao Gao, Jing |
| Author_xml | – sequence: 1 givenname: Jing-Tao surname: Zhou fullname: Zhou, Jing-Tao email: Zhoujt@nwpu.edu.cn organization: Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University – sequence: 2 givenname: Xu surname: Zhao fullname: Zhao, Xu organization: Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University – sequence: 3 givenname: Jing surname: Gao fullname: Gao, Jing organization: Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University |
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| Keywords | Long short-term memory Hilbert-Huang Transform Variable working conditions Tool remaining useful life prediction |
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| SubjectTerms | CAE) and Design Computer-Aided Engineering (CAD Continuous production Correlation Engineering Feature extraction Industrial and Production Engineering Life prediction Mechanical Engineering Media Management Original Article Prediction models Signal processing Tool life Tool wear Useful life Working conditions |
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| Title | Tool remaining useful life prediction method based on LSTM under variable working conditions |
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