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Knowledge elicitation for fault diagnostics in plastic injection moulding : a case for machine-to-machine communication
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In most manufacturing processes the defect rate is very low. Sometimes, only a few parts per million are defective because of a faulty process. For this reason, fault diagnostics is faced with extremely imbalanced data sets and requires large volumes of data to achieve a reasonable performance. This paper explores whether a machine-to-machine approach can be used, in which several work systems share the process data to improve the accuracy of the fault-detection model. The model is based on machine learning and is applied to industrial data from approximately two million process cycles performed on several injection moulding work systems.
1.01 - Original Scientific Article
FS - Faculty of Mechanical Engineering
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Vol. 66, iss. 1
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ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
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