Abstract
Abstract Diagnosing faults during the operation of a system is an essential task when investigating technological systems. In this paper, a new online fault identification method is proposed which is based on the occurrence graph of the coloured Petri net model of the system. The model is able to simulate the normal and faulty operations of the system given in the form of event lists, so called traces. The diagnosis is based on the search for deviations between the traces of the normal and the actual operations. In the case of complex technological systems, the occurrence graph can contain hundreds of nodes; therefore, the computational effort and searching-time increase significantly. Our proposed structural decomposition method can manage these demands so it has a crucial impact on the practical application of diagnostic processes. The main idea of our method is that the complex systems can be decomposed into technological units. Therefore, the diagnosis can be done by components separately and the diagnostic result of a unit can be used for the diagnosis of the other units connected to it. Because of the structural decomposition, the diagnosis has to be performed on much smaller occurrence graphs but the effect of faults in previous units is taken into account. The proposed method is illustrated by a simple case study.
Highlights
Identifying faults and analysing their consequences are important tasks during the investigation of technological systems
Coloured Petri nets are often used for modelling production lines [3]
Qualitative models can be used in this case and it is enough to know whether the value of a signal belongs to a specified range space or not
Summary
Identifying faults and analysing their consequences are important tasks during the investigation of technological systems. The main advantage of the proposed algorithm is that in the case of bounded Petri nets the basis occurrence graph can be computed offline It reduces the computational effort of the online diagnosis. Sufficient conditions of diagnosability of faulty transitions are given in the form of a system of inequalities [8] In this method, the marking of places is observable. An online fault detection algorithm has been developed [9], which is based on solving integer linear programming problems and checking the diagnosability conditions. The fault detection is based on the generation of residuals, which are computed by comparing the markings of observable places with the reference model. An online algorithm based on the difference between the system output and the diagnoser model output is developed for detecting faulty markings. Transformation techniques for the inversion of CPN are presented here
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
