Multi-Fault Diagnosis for Industrial Processes based on Hybrid Dynamic Bayesian Network

Fan Yang and Weijun Yu


Fault diagnosis, signed directed graph, hybrid dynamic Bayesian network


In industrial processes, complex faults or multi-faults can cause more comprehensive or wide-spread phenomenon than single-faults, leading to severe consequences. Thus complete fault diagnosis techniques should be able to handle multi-fault as well as single-fault cases although multi-fault cases have relatively low probability and high complexity. In order to describe the conditional relationship between process variables and known faults, a Bayesian network can be employed, where process variables are continuous while faults are discrete; this case is called hybrid. In addition, time factor or dynamics should also be included, leading to the hybrid dynamic Bayesian network (HDBN) framework, which has been used to describe and monitor dynamic systems. Under this framework, we describe fault diagnosis problems as a HDBN inference problem and propose an algorithm based on time iteration. A simulated 5-sink system and the Tennessee Eastman Process (TEP) are given to illustrate and validate the proposed methodology and some practical issues are discussed. The performance of the algorithm when treating the TEP fault diagnosis indicates a balance between isolation accuracy and computational complexity.

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