Process Monitoring Based on Multivariate Causality Analysis and Probability Inference

System security is one of the key challenges of the cyber-physical systems. Bayesian approach can estimate and predict the potentially harmful factors of the general system, but it has many limitations that can lead to undesirable effects in the complex systems. This paper presents a new modeling and monitoring framework to avoid the traditional Bayesian network disadvantage. A multivariate causal analysis method is proposed to establish a compact system structure. Combined with network parameter learning, we constructed a corresponding multivariate alarm predict graph model, in which the qualitative and quantitative relationships among the process variables are revealed distinctly. Then this model is used to accurately predict the future possible alarm events via the probability inference. Similarly, it also can be used to detect faults and find the source of the fault. The effectiveness of the proposed method is verified in public data sets and the Tenessee Eastman process. Simulation results show that the established causal relationship is completely consistent with the actual mechanism, and the alarm state of the critical variable is accurately predicted.