Process Monitoring Based on Multivariate Causality Analysis and Probability Inference
| Autor: | Jinglin Zhou, Jing Wang, Xiaolu Chen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Multivariate statistics
General Computer Science Computer science Bayesian probability 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences parameter learning Data modeling 020401 chemical engineering General Materials Science Probability inference 0204 chemical engineering 0105 earth and related environmental sciences Alarm prediction General Engineering Process (computing) Bayesian network Causality process monitoring modeling multivariate causality analysis Data mining lcsh:Electrical engineering. Electronics. Nuclear engineering computer lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 6, Pp 6360-6369 (2018) |
| ISSN: | 2169-3536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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