Intrinsic Causality Embedded Concurrent Quality and Process Monitoring Strategy

The causality between different variables can reveal the flows of material, energy, and information in the process system. It is beneficial to reflect the relationship between quality variables and process variables. In this study, a concurrent quality and process monitoring method is proposed with intrinsic causality analytics. The proposed method explores the causality between different variables using transfer entropy. Then, the directly related variables and their corresponding time lags are combined to extract convolutional features, which are used to generate feature matrices for process and quality variables. In this way, the quality related information is extracted from the process variables which are directly related to the quality variables. After that, monitoring models are established for each pair of feature matrices, and the monitoring results are integrated to provide a final monitoring result. Since the process disturbances usually smear to directly related variables, the fault signature can be amplified to improve the detection sensitivity when the directly related variables are combined. Finally, the operation status of the process system is identified through the designed monitoring policy, which combines the decisions of different statistics. It is noted that the proposed strategy can be readily generalized to many other existing quality related monitoring methods. Experiments on a real industrial condenser show that the proposed method can distinguish the quality related faults from the process related faults in the condenser. Besides, it has better detection sensitivity than some commonly used quality related monitoring methods.