Tool remaining useful life prediction using deep transfer reinforcement learning based on long short-term memory networks. Yao, J., Lu, B., & Zhang, J. The International Journal of Advanced Manufacturing Technology, 118(3):1077–1086, January, 2022. ![Tool remaining useful life prediction using deep transfer reinforcement learning based on long short-term memory networks [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Tool wear and faults will affect the quality of machined workpiece and damage the continuity of manufacturing. The accurate prediction of remaining useful life (RUL) is significant to guarantee the processing quality and improve the productivity of automatic system. At present, the most commonly used methods for tool RUL prediction are trained by history fault data. However, when researching on new types of tools or processing high value parts, fault datasets are difficult to acquire, which leads to RUL prediction a challenge under limited fault data. To overcome the shortcomings of above prediction methods, a deep transfer reinforcement learning (DTRL) network based on long short-term memory (LSTM) network is presented in this paper. Local features are extracted from consecutive sensor data to track the tool states, and the trained network size can be dynamically adjusted by controlling time sequence length. Then in DTRL network, LSTM network is employed to construct the value function approximation for smoothly processing temporal information and mining long-term dependencies. On this basis, a novel strategy of Q-function update and transfer is presented to transfer the deep reinforcement learning (DRL) network trained by historical fault data to a new tool for RUL prediction. Finally, tool wear experiments are performed to validate effectiveness of the DTRL model. The prediction results demonstrate that the proposed method has high accuracy and generalization for similar tools and cutting conditions.
@article{yao_tool_2022,
title = {Tool remaining useful life prediction using deep transfer reinforcement learning based on long short-term memory networks},
volume = {118},
issn = {1433-3015},
url = {https://doi.org/10.1007/s00170-021-07950-2},
doi = {10.1007/s00170-021-07950-2},
abstract = {Tool wear and faults will affect the quality of machined workpiece and damage the continuity of manufacturing. The accurate prediction of remaining useful life (RUL) is significant to guarantee the processing quality and improve the productivity of automatic system. At present, the most commonly used methods for tool RUL prediction are trained by history fault data. However, when researching on new types of tools or processing high value parts, fault datasets are difficult to acquire, which leads to RUL prediction a challenge under limited fault data. To overcome the shortcomings of above prediction methods, a deep transfer reinforcement learning (DTRL) network based on long short-term memory (LSTM) network is presented in this paper. Local features are extracted from consecutive sensor data to track the tool states, and the trained network size can be dynamically adjusted by controlling time sequence length. Then in DTRL network, LSTM network is employed to construct the value function approximation for smoothly processing temporal information and mining long-term dependencies. On this basis, a novel strategy of Q-function update and transfer is presented to transfer the deep reinforcement learning (DRL) network trained by historical fault data to a new tool for RUL prediction. Finally, tool wear experiments are performed to validate effectiveness of the DTRL model. The prediction results demonstrate that the proposed method has high accuracy and generalization for similar tools and cutting conditions.},
language = {en},
number = {3},
urldate = {2022-03-03},
journal = {The International Journal of Advanced Manufacturing Technology},
author = {Yao, Jiachen and Lu, Baochun and Zhang, Junli},
month = jan,
year = {2022},
pages = {1077--1086},
}
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However, when researching on new types of tools or processing high value parts, fault datasets are difficult to acquire, which leads to RUL prediction a challenge under limited fault data. To overcome the shortcomings of above prediction methods, a deep transfer reinforcement learning (DTRL) network based on long short-term memory (LSTM) network is presented in this paper. Local features are extracted from consecutive sensor data to track the tool states, and the trained network size can be dynamically adjusted by controlling time sequence length. Then in DTRL network, LSTM network is employed to construct the value function approximation for smoothly processing temporal information and mining long-term dependencies. On this basis, a novel strategy of Q-function update and transfer is presented to transfer the deep reinforcement learning (DRL) network trained by historical fault data to a new tool for RUL prediction. Finally, tool wear experiments are performed to validate effectiveness of the DTRL model. The prediction results demonstrate that the proposed method has high accuracy and generalization for similar tools and cutting conditions.","language":"en","number":"3","urldate":"2022-03-03","journal":"The International Journal of Advanced Manufacturing Technology","author":[{"propositions":[],"lastnames":["Yao"],"firstnames":["Jiachen"],"suffixes":[]},{"propositions":[],"lastnames":["Lu"],"firstnames":["Baochun"],"suffixes":[]},{"propositions":[],"lastnames":["Zhang"],"firstnames":["Junli"],"suffixes":[]}],"month":"January","year":"2022","pages":"1077–1086","bibtex":"@article{yao_tool_2022,\n\ttitle = {Tool remaining useful life prediction using deep transfer reinforcement learning based on long short-term memory networks},\n\tvolume = {118},\n\tissn = {1433-3015},\n\turl = {https://doi.org/10.1007/s00170-021-07950-2},\n\tdoi = {10.1007/s00170-021-07950-2},\n\tabstract = {Tool wear and faults will affect the quality of machined workpiece and damage the continuity of manufacturing. 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On this basis, a novel strategy of Q-function update and transfer is presented to transfer the deep reinforcement learning (DRL) network trained by historical fault data to a new tool for RUL prediction. Finally, tool wear experiments are performed to validate effectiveness of the DTRL model. 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