A latent feature oriented dictionary learning method for closed-loop process monitoring

Industrial cyber-physical system (ICPS), by its powerful computing, communication, precise control and remote operation functions, has become the mainstream of modern industrial process. The observed variables of the closed-loop process in ICPS are subject to the degradation of equipment and other factors, resulting in exhibiting a stationary/nonstationary mixture feature and dynamic feature. Moreover, due to the frequent change of working conditions in the closed-loop process, the traditional open-loop process monitoring method always triggers false alarms, which will impose a negative impact on the safety and trustworthiness of ICPS. Therefore, for the closed-loop process in ICPS, a latent feature oriented dictionary learning (LFDL) method is proposed, which realizes the precise separation of latent features of raw data through three stages. First, closed-loop process variables are separated into stationary and nonstationary variables to mine the local information spatially. Then, from the temporal viewpoint, the static and dynamic features were extracted for stationary and nonstationary variables on the basis of the slow feature analysis method and cointegration analysis for local monitoring. Finally, the global monitoring results are obtained by utilizing the dictionary learning method to fuse respectively the local monitoring results of the static and dynamic features. Since the proposed method has taken the feature of the close-loop process from temporal and spatial viewpoints simultaneously, it can distinguish the normal change of operating conditions and actual faults accurately. Extensive experiments including the three-phase flow, the Tennessee Eastman process and an industrial roasting process are conducted to demonstrate the feasibility and effectiveness of the proposed method.