A CPPS based on GBDT for predicting failure events in milling

Cyber-physical production systems (CPPS) are mechatronic systems monitored and controlled by software brains and digital information. Despite its fast development along with the advancement of Industry 4.0 paradigms, an adaptive monitoring system remains challenging when considering integration with...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2020-11, Vol.111 (1-2), p.341-357
Main Authors: Zhang, Y., Beudaert, X., Argandoña, J., Ratchev, S., Munoa, J.
Format: Article
Language:English
Subjects:
Adaptive systems
CAE) and Design
Computer-Aided Engineering (CAD
Decision analysis
Decision trees
Engineering
Industrial and Production Engineering
Industrial plants
Mechanical Engineering
Media Management
Original Article
Principal components analysis
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Summary:Cyber-physical production systems (CPPS) are mechatronic systems monitored and controlled by software brains and digital information. Despite its fast development along with the advancement of Industry 4.0 paradigms, an adaptive monitoring system remains challenging when considering integration with traditional manufacturing factories. In this paper, a failure predictive tool is developed and implemented. The predictive mechanism, underpinned by a hybrid model of the dynamic principal component analysis and the gradient boosting decision trees, is capable of anticipating the production stop before one occurs. The proposed methodology is implemented and experimented on a repetitive milling process hosted in a real-world CPPS hub. The online testing results have shown the accuracy of the predicted production failures using the proposed predictive tool is as high as 73% measured by the AUC score.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-020-06078-z