Component Models Based Approach for Failure Diagnosis of Discrete Event Systems

Component Models Based Approach for Failure Diagnosis of Discrete Event Systems

Alexandre Philippot (Université de Reims Champagne-Ardenne, France), Moamar Sayed-Mouchaweh (Université de Reims Champagne-Ardenne, France) and Véronique Carré-Ménétrier (Université de Reims Champagne-Ardenne, France)
DOI: 10.4018/978-1-61520-849-4.ch016
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Abstract

This chapter addresses the problem of diagnosing Discrete Event Systems (DESs), specifically manufacturing systems with discrete sensors and actuators. Manufacturing systems are generally composed of several components which can evolve with the course of time (new components, new technologies …). Their diagnosis requires the computation of a global model of the system. This is not realistic due to the great number of components. In this chapter, we propose to perform the diagnosis by using component models. Each component model is constructed using different information sources represented by sensor-actuator spatial structure (plant model), controller specifications (desired behaviour) and temporal information about the actuators reactivity. In addition, components’ technological constraints and characteristics are considered for this construction. For each model, a local diagnoser is computed. Its complexity is polynomial because the diagnosis is computed only for the faults that it can diagnose. Limited information about the global system functioning is required to synchronize the functioning of local diagnosers. This synchronisation is considered using a set of expert rules representing the symbolic information about the global desired behaviour. The local diagnosers are then used to perform diagnosis online. They validate, in the case of normal functioning, the transmission of control signals and incoming sensor data between the controller and the plant.
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Background

The increasing complexity of processes rises their potential to fail regardless how safe the control design is and how better trained the operators are (Perrow, 1984). Thus, diagnosis of industrial systems is a subject that has received a great attention in the past few decades (Boel & Jiroveanu, 2004), (Boufaïed, 2003), (Genc & Lafortune, 2003), (Holloway & Krogh, 1990), (Klein et al., 2005), (Lafortune et al., 2005), (Su & Wonham, 2000). It is defined as the process of detecting and isolating faults. Fault detection is the operation of deciding weather a failure has occurred or not. It is followed by the fault isolation in order to determine the kind and the location of the failure. Any abnormal change in the system’s behaviour is caused by a fault whereas a complete operational breakdown is denoted as a failure. In this chapter, the two terms are used synonymously.

Discrete Event Systems (DESs) are dynamic systems equipped with a discrete state space and a state-transition structure (Cassandras & Lafortune, 1999), (Ferrier & Boimond, 2004), (Wonham, 1995). They are discrete in time and in state space. They can be derived by the tick of a clock and may be nondeterministic, e.g., they have several transitional choices due to internal events or other mechanisms that are not necessarily modelled by the system analyst. Manufacturing systems are one of the major applications of DESs. They are a collection of integrated equipment and human resources, whose function is to perform one or more processing and/or assembly operations on a starting raw material, part, or set of parts. DESs are often modelled using a finite-state automaton (IEC, 2002), a GRAFCET (David, 1992), a Petri net (Cassandras & Lafortune, 1999) or process algebra (Console, 2002, Wang, 2000). Each modelling tool has its advantages and disadvantages depending on the objectives of modelling, e.g., model complexity and formalization facilities.

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