Improving the Modelling Capability of an Integrated Fault Detection and Diagnostic Petri Net Methodology for Dynamic Systems

Abstract

The existing Generalised Stochastic Petri Net and modified Bayesian Stochastic Petri Net (GSPN-mBSPN) methodology has demonstrated improved modelling capabilities for fault diagnosis in dynamic systems with feedback control loops. However, the GSPN-mBSPN approach uses predefined input conditional probability tables (iCPTs) for fault diagnosis, limiting its usage in dynamic fault diagnosis due to the time required to define and populate the iCPT entries accurately. This paper presents an algorithm to automatically generate iCPT tables, enhancing the modelling capability of the GSPN-mBSPN approach for fault diagnosis of dynamic systems under time-varying conditions. The GSPN module in a GSPN-mBSPN model of a dynamic system is analysed and structured into sub-net modules, representing system components, monitoring parameters, and interactions. These sub-net modules provide data structures for the iCPT tables, describing the working and failure states/modes of system components, states of the monitoring parameter, and causal relationships between system component states and observable process parameters. The algorithm populates the entries of the iCPT tables based on the analysis of the sub-net modules. Application of the algorithm to a water tank level control system demonstrates improved speed and accuracy in generating iCPTs for dynamic fault detection and diagnosis applications using GSPN-mBSPN approach.