Making Factories Smarter Through Machine Learning
This failure happened to a production line with a turnover capability of 30 M € per day with 50 operators directly working on the line per shift . When the failure occurred , the complete line had to be stopped during 3 shifts until the repair was completed . However , these failures may last for at least 30 shifts and the repair time is directly related to the availability of spare parts . In this case , the workforce cost and the lost turnover should be added to the complete cost .
It is important to state that a machine learning-based monitoring system could detect the first failure peak , giving enough time to stop the line in a controlled manner . A production and workforce could then be reassigned to reduce the failure impact over production line productivity . In this case , a relatively simple machine learning algorithm is able to detect these types of anomalies within a variable , effectively warning about the problem when a first peak is detected or before .
Figure 3 : Summary of costs for damages due to bearing malfunction
4 . MACHINE LEARNING FOR PREDICTIVE MAINTENANCE
Fast forward a few years to a new system where machine learning is being used to analyze the data to predict potential system failure 14 . First an understanding of the machine learning approach is needed to gain full appreciation of the scope of the predictive analytics performed to predict when the component / system may fail and even more importantly why the failure may occur . This , in turn , allows system optimizations that can extend the lifetime of the asset and the overall system .
14
D . GOYAL y B . S . PABLA , « Condition based maintenance of machine tools — A review ,» CIRP Journal of Manufacturing Science and Technology , vol . 10 , pp . 24-35 , 2015
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