A method based on combinations of forecaster and weighing matrix to detect fault of components in diecasting process

This work presents a flexible method to detect the fault of components in a diecasting machine. The core of this method is the combination of sensor-based statistical predictions with the expert knowledge using a series of weights determined in formal interviews. Each feature is extracted from the machine’s sensor time history using a least square regression and paired with an uncertainty estimator. Then, each uncertainty estimator is combined with the uncertainty of the relative transducer in order to obtain a combined uncertainty of the two contributions. The final result is a score index representing the distribution of different types of faults in the diecasting machine. A dataset of 451 injections was analyzed to test the method. The historical records of maintenance service recorded 19 events corresponding to a fault of a valve. All the events were correctly detected by the algorithm as well. The uncertainty estimators of the parameters have allowed performing an analysis of the effect of transducers’ uncertainty on the final prediction. A higher uncertainty is negligible in the final prediction of fault. This means that the method can work also with transducers with lower accuracy.