A neural prediction model for monitoring and fault diagnosis of a plastic injection moulding process

In engineering systems, early detection of the occurrence of faults is critical in avoiding product defects. This problematic is here discussed in the framework of an industrial process, namely, an injection moulding plastic machine. The relationships between the process state and the product quality are achieved through Principal Component Analysis. After having identified the main variables, two neural network architectures were investigated, TDNN and Elman networks, with respect to one-step ahead prediction. The results show that TDNN exhibited lower training times with respect to a desired performance criteria. However, for time series in which temporal dependency is large, the recurrent networks with time delayed inputs could lead to better results.