Bayesian Fault Diagnosis With Asynchronous Measurements and Its Application in Networked Distributed Monitoring

摘要

Conventional Bayesian fault diagnosis assumes that all measurements are available synchronously; however, this condition does not always hold in practical industry because a process can be characterized by multiple sampling or transmitting rates. This paper introduces a Bayesian fault diagnosis system incorporating both historical and online information to address the asynchronous measurement problem. First, the Expectation Maximization approach is utilized to deal with the historical asynchronous measurements; second, the online incomplete measurements are handled through a Bayesian marginalization method within a moving horizon. Then, a Bayesian diagnosis system revealing both the underlying fault status of the whole plant and the unavailable statuses of the corresponding local units is established, which is more robust for practical application. The proposed scheme is tested on a numerical example, the distributed monitoring problem of Tennessee Eastman benchmark process, and the distributed monitoring problem of an industrial tail gas treatment plant. Monitoring results demonstrate the feasibility and efficiency of the proposed approach.