Fault diagnosis with high accuracy and timeliness for semi-batch crystallization process based on deep learning with multiple pattern representation

The research on chemical process fault diagnosis has made significant progress, but there is still a big gap in its application to complex practical industrial processes. As for the fault diagnosis of batch crystallization processes, the recently-proposed dynamic time warping–convolutional neural network (DTW-CNN) model has achieved a great improvement in the fault diagnosis. However, its fault diagnosis rate (FDR) and timeliness of fault diagnosis are still low, and thus, it needs to improve further before being applied to the practical application. In this paper, a multiple pattern representation–convolutional neural network (MPR-CNN) model is proposed and applied for the fault diagnosis of a semi-batch crystallization process. The MPR-CNN model enables the manual extraction of features with four pattern representation algorithms in the data pre-processing stage, and generates a three-dimensional matrix which is used as the training sample and input to the CNN for the formal feature extraction and weight learning. An excellent classification performance, with an average FDR of 97.5%, is achieved. This model is also applied for the fault diagnosis of process data within a shorter period of time after the occurrence of faults. The results indicate that the model could make timely fault diagnosis with a highly stable and accurate performance after the occurrence of a fault.