Synthesis a Petri net based control model for a FMS cell
Computers in Industry
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Fault detection is a crucial and challenging task in the automatic control of complex systems, e.g. in flexible manufacturing systems (FMS's) as a representative class of discrete event systems (DES's). A discrete event system approach to the problem of failure diagnosis is presented. The property of diagnosability is introduced in the context of the failure diagnosis problem, e.g. in the context of the availability of the DES. We bring a DES approach to the problem of failure diagnosis of FMS's because most of them are modelled by DES's, and because continuous variable dynamic systems can often be viewed as DES's at a higher level of abstraction, respectively when their trajectories are determined by meaningful accumulations of dynamics e.g., are determined by events. The states of the discrete event model reflect the normal and the failed status of the system components, while the failure events form part of the event set. We propose a systematic procedure for detection of failure events using diagnoses implemented with stochastic coloured Petri nets (SCPN). The diagnoser is a SCPN which models the FSM. This model performs detection and isolation of failures (failure information and occurrences of failures can be detected by inspecting the states of the SCPN model), and it also permits the verification of the diagnosability properties of the system (e.g., permits the estimation of the availability of the system). In our assumption the availability of a production cell j (j=1.2...,n, where n is the total number of part cells in the FMS) is calculated with a Markov chain that includes the failure rates, repair rates, and coverability of the respective devices in the production cell j. An analytical approach for the availability evaluation of cellular manufacturing systems (as basic components of FMS's) is presented, where a FMS is considered operational as long as its production capacity requirements are satisfied. The approach is used to evaluate transient and steady-state performance of alternative designs based on an industrial.