Centralized/Decentralized Fault Diagnosis of Event-Driven Systems Based on Probabilistic Inference

Abstract

Event-driven controlled systems based on the Programmable Logic Controller (PLC) are widely used in many industrial processes. The number of such a control system is said to occupy more than eighty percent of the entire existing control systems. Nowadays, the demands for production facilities are shifting from the high speed and highly efficiency to the safety and high reliability. In order to meet these requirements, several strategies for fault diagnosis of systems and the design of recovery procedure have been proposed. In the case of considering the PLC-based control systems, since they have discrete and event-driven characteristics inherently, system models based on discrete-event-system description give more efficient diagnostic algorithm than those based on continuous-time systems (for surveys cf. (A. Darwiche & G. Provan (1996); D. N. Pandalai& L. E. Holloway (2000); M. Sampath et al. (1995); S.H.Zad et al. (1999))). This aspect will be more emphasized when the number of components would be large. Based on these considerations, Lunze proposed a centralized fault diagnosis framework based on the system model with Timed Markov Model (TMM) (J.Lunze (2000)). This method especially becomes useful when numerous number of input and output data are collected through daily operation since the TMM is based on a stochastic expression of time interval between successive events. This approach also has some robustness against unevenness underlying in the ordinary production facilities. However, this kind of centralized diagnosis strategies will cause an explosion of the computational burden when they are applied to the large scale systems. In this case, the decentralized approach is highly recommended wherein the diagnosis is performed by each diagnose together with the communication with other diagnosers (O.Contant (2006); S.Debouk (2000); R.Su et al. (2002)). These approaches, however, were based on the deterministic model. Based on these backgrounds, the authors (S.Inagaki et al. (2007)) proposed a decentralized stochastic fault diagnosis strategy based on a combination of TMM and Bayesian Network (BN). The BN represents the causal relationship between the fault and observation in subsystems. Since the decentralized diagnosis architecture distributes the computational burden for the diagnosis to the subsystems, a large scale diagnosis problems in real-world application can be solved. In the decentralized approach, the computational burden and the diagnosis performance strongly depend on the complexity of the graph structure of BN.