A Multilogic Probabilistic Signed Directed Graph Fault Diagnosis Approach Based on Bayesian Inference

作者：Peng, Di; Geng, Zhiqiang; Zhu, Qunxiong^*

来源：Industrial & Engineering Chemistry Research, 2014, 53(23): 9792-9804.

DOI：10.1021/ie403608a

• SYSTEMATIC FRAMEWORK
• CHEMICAL-PROCESSES
• DIGRAPHS
• MODEL
• SDG

Signed directed graph (SDG), as a widely applied fault diagnosis approach, is unable to express complicated logic relations other than logic OR and usually results in spurious interpretations. To solve the problem, a semiquantitative fault diagnosis approach based on the model of multilogic probabilistic SDG (MPSDG) with Bayesian inference is proposed. The MPSDG model introduces the logic gates to describe multiple logic causalities between process variables, and the priori probabilistic parameters in MPSDG are decided by the historical malfunction frequencies and the deviation of variables. When a failure occurs, the backtracking algorithm using the consistent rule is conducted immediately, and the posterior probabilities of each searched fault are computed and sorted by a set of Bayesian inference mechanisms. Thus, the real reason is further distinguished. This MPSDG based fault diagnosis approach is applied to two examples: a continuous stirred tank heater (CSTH) process and a Tennessee Eastman (TE) process. The experimental results demonstrate that the proposed approach is superior to the conventional SDG approach and can diagnose the production faults more accurately.

• 出版日期2014-6-11
• 单位北京化工大学