TY - GEN
T1 - A data-driven prognostics framework for tool remaining useful life estimation in tool condition monitoring
AU - Zhang, Chong
AU - Hong, Geok Soon
AU - Xu, Huan
AU - Tan, Kay Chen
AU - Zhou, Jun Hong
AU - Chan, Hian Leng
AU - Li, Haizhou
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/28
Y1 - 2017/6/28
N2 - Tool Condition Monitoring (TCM) is an important topic in manufacturing industry, which improves product quality, production efficiency, reduces costs and downtime. This paper develops a new data-driven framework for estimating tool remaining useful life (RUL) in TCM. The framework includes the following modular components: data preprocessing with a proposed adaptive Baysian change point detection (ABCPD) for automatic data alignment, time window process, feature extraction, feature selection and a multi-layer neural network as the main machine learning algorithm. The proposed framework is evaluated on a real-world gun drilling experimental dataset with multiple sensor measurements (i.e. thrust force, torque, 12 vibration signals). Different model selection, sensor selection, feature selection methods have been investigated in this paper. The simulation performance of the proposed framework is studied with the gun drilling dataset and it has been shown that the proposed framework has good performance.
AB - Tool Condition Monitoring (TCM) is an important topic in manufacturing industry, which improves product quality, production efficiency, reduces costs and downtime. This paper develops a new data-driven framework for estimating tool remaining useful life (RUL) in TCM. The framework includes the following modular components: data preprocessing with a proposed adaptive Baysian change point detection (ABCPD) for automatic data alignment, time window process, feature extraction, feature selection and a multi-layer neural network as the main machine learning algorithm. The proposed framework is evaluated on a real-world gun drilling experimental dataset with multiple sensor measurements (i.e. thrust force, torque, 12 vibration signals). Different model selection, sensor selection, feature selection methods have been investigated in this paper. The simulation performance of the proposed framework is studied with the gun drilling dataset and it has been shown that the proposed framework has good performance.
UR - https://www.scopus.com/pages/publications/85044481435
U2 - 10.1109/ETFA.2017.8247659
DO - 10.1109/ETFA.2017.8247659
M3 - Conference article published in proceeding or book
AN - SCOPUS:85044481435
T3 - IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
SP - 1
EP - 8
BT - 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2017
Y2 - 12 September 2017 through 15 September 2017
ER -