Shared Parameter Network: An efficient process monitoring model

Abstract

The use of deep learning methods for fault detection and diagnosis (FDD) has continued to gain research attention due to the continuous availability of data and the need for reliable FDD methods. In our previous work, we proposed a probabilistic bidirectional recurrent network (PBRN) for process prediction and detection of faults. A limitation of PBRN is the model size. In this paper, we reduce the computational requirements of the original PBRN by introducing a shared parameter network (SPN). The SPN uses a shared parameter space to learn relevant features for process prediction and fault detection. This shared parameter setting reduces the model size and training time significantly. Using the SPN, we achieved a 76 percent reduction in model size and a 48 percent reduction in training time. The training validation loss profile and fault detection performances of both models also demonstrate the superiority of the SPN.