A CPPS based on GBDT for predicting failure events in milling
| Autor: | Svetan Ratchev, J. Argandoña, Y. Zhang, Xavier Beudaert, Jokin Munoa |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
business.industry Process (engineering) Computer science Mechanical Engineering 020208 electrical & electronic engineering Decision tree 02 engineering and technology Mechatronics Machine learning computer.software_genre Industrial and Manufacturing Engineering Computer Science Applications 020901 industrial engineering & automation Software Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Production (economics) Artificial intelligence Industrial and production engineering business computer |
| Zdroj: | The International Journal of Advanced Manufacturing Technology. 111:341-357 |
| ISSN: | 1433-3015 0268-3768 |
| DOI: | 10.1007/s00170-020-06078-z |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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