Fault detection and diagnosis to enhance safety in digitalized process system

The increased complexity of digitalized process systems requires advanced tools to detect and diagnose faults early maintain safe operations. This study proposed a hybrid model that consists Kernel Principal Component Analysis (kPCA) DNNs can be applied in various processes. complex data is processed by kPCA reduce its dimensionality; then, simplified used for two separate training (detection diagnosis). relative performance the compared with conventional methods. Tennessee Eastman Process was confirm efficacy model. results show reduction input dimensionality increases classification accuracy. In addition, splitting detection diagnosis into reduced times serves as an important tool fault take corrective actions, thus enhancing safety.