Modular Fault Diagnosis Based on Discrete Event Systems (original) (raw)

Chapter 16 Component Models Based Approach for Failure Diagnosis of Discrete Event Systems

2015

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...

Implementation of diagnosis approach for Discrete Event Systems

2012

This paper presents an approach of diagnosis for manufacturing system considered as Discrete Event Systems. It uses plant decomposition and a decentralized diagnosis structure to reduce the combinatory explosion found in centralized structures. The local behavior is extracted using decentralized plant modeling. It is from this behavior that possible faults are identified to construct abnormal behavior models. The approach is illustrated around a manufacturing benchmark.

Diagnosability of Discrete Event Systems with Modular Structure

Discrete Event Dynamic Systems, 2006

The diagnosis of unobservable faults in large and complex discrete event systems modeled by parallel composition of automata is considered. A modular approach is developed for diagnosing such systems. The notion of modular diagnosability is introduced and the corresponding necessary and sufficient conditions to ensure it are presented. The verification of modular diagnosability is performed by a new algorithm that incrementally exploits the modular structure of the system to save on computational effort. The correctness of the algorithm is proved. Online diagnosis of modularly diagnosable systems is achieved using only local diagnosers.

Failure diagnosis of dynamic systems: an approach based on discrete event systems

Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), 2001

We present the salient features of a methodology for failure diagnosis of dynamic systems that can be modeled as discrete event systems. This methodology was introduced by Sampath et al. for centralized systems and subsequently extended by Debouk et al. for certain classes of decentralized systems. We discuss how to perform detection and identification of unobservable fault events using diagnosers, which are finite-state automata that are built from the discreteevent model of the system under consideration. Examples of diagnosers are given. Comparisons with other methodologies for diagnosing dynamic systems are given.

Fault-diagnosis in discrete event systems: Improvements and new results

Alexandria Engineering Journal, 2011

The malfunction of sensors, actuators, and erroneous actions of human operators can have some disastrous consequences in high risk systems especially if these systems have multiple faults that can lead to undesirable shutdowns and consequently mass reduction. A reduced interpreted Petri net (IPN) diagnoser has been devised only for safe Petri net models with an output function that associates an output vector to each net marking. The main drawback of this approach is that the Petri net model of the system to be monitored should be diagnosable i.e. all faults can be detected that limits its application on a set of diagnosable models. For non diagnosable Petri net model, the conventional diagnoser incidence matrix has columns with null or similar values that fail to detect a single fault. The conventional diagnoser also cannot detect multiple faults even for diagnosable models. This paper introduces a new local diagnoser to overcome such problems. It decomposes the central IPN-diagnoser into a set of local diagnosers that are linked with multi sessions of the process to be monitored. This decomposition should guarantee that the developed local diagnosers have incidence matrices that their columns are different from each other. For null values contained in the incidence matrix of a local diagnoser, this paper proposes a set of rules based on the synchronic composition idea to overcome this problem. This proposed scheme allows multiple faults detection and isolation in quick and accurate manner for all Petri net models. Industrial processes are employed for testing the soundness of the proposed scheme.

Fault Diagnosis of Discrete Event Systems Using Components Fault-Free models

This paper presents a Boolean discrete event model-based approach for Fault Detection and Isolation of manufacturing systems. This approach considers a system as a set of components composed of discrete actuators and their associated discrete sensors. Each component model is only aware of its local desired, fault-free, behavior. The occurrence of any fault entailing the violation of the desired behavior is detected and the potential responsible candidates are isolated using event sequences, time delays between correlated events and state conditions, characterized by sensors readings and control signals issued by the controller. An application example is used to illustrate the approach.

Failure diagnosis using discrete-event models

IEEE Transactions on Control Systems Technology, 1996

Detection and isolation of failures in large, complex systems is a crucial and challenging task. The increasingly stringent requirements on performance and reliability of complex technological systems have necessitated the development of sophisticated and systematic methods for the timely and accurate diagnosis of system failures. We propose a discrete-event systems (DES) approach to the failure diagnosis problem. This approach is applicable to systems that fall naturally in the class of DES; moreover, for the purpose of diagnosis, continuous-variable dynamic systems can often be viewed as DES at a higher level of abstraction. We present a methodology for modeling physical systems in a DES framework and illustrate this method with examples. We discuss the notion of diagnosability, the construction procedure of the diagnoser, and necessary and sufficient conditions for diagnosability. Finally, we illustrate our approach using realistic models of two different heating, ventilation, and air conditioning (HVAC) systems, one diagnosable and the other not diagnosable. While the modeling methodology presented here has been developed for the purpose of failure diagnosis, its scope is not restricted to this problem; it can also be used to develop DES models for other purposes such as control. A detailed treatment of the theory underlying our approach can be found in a companion paper [27].

Fault Diagnosis in Discrete Event Systems using Multi-model Approach

2014

Seeing that the discrete event systems can generate undesirable sequences, several authors focused on the problem of diagnosis. However, few considered the management operating mode in an uncertain environment. In this context, we propose an approach allowing the diagnosis of unobservable events, taking into account the different operating modes in a physical system. Our approach is based on multi-model approach; where each model describes a system in a given operating mode. We will present, in this paper, architecture to ensure the diagnosis of multi-model system. For this, we propose an algorithm to resolve the ambiguity problem and to ensure the commutation between different operating modes where several failures can occur. Keywords— discrete event system, operating mode management, multi-model, diagnosis.

Unconditional decentralized structure for the fault diagnosis of discrete event systems

1st IFAC Workshop on Dependable Control of Discrete Systems (2007), 2007

This paper proposes an unconditional decentralized structure to realize the fault diagnosis of Discrete Event Systems (DES), specially manufacturing systems with discrete sensors and actuators. This structure is composed on the use of a set of local diagnosers, each one of them is responsible of a specific part of the plant. These local diagnosers are based on a modular modelling of the plant in order to reduce the state explosion. Each local diagnoser uses event-based, state based and timed models to take a decision about fault's occurrences. These models are obtained using the information provided by the plant, the controller and the actuators reactivity. All local diagnosis decisions are then merged by a Boolean operator in order to obtain one global diagnosis decision. Finally, the diagnosers are polynomial-time in the cardinality of the state space of the system. This approach is illustrated using an example of manufacturing system.

Automatic construction of diagnoser for complex discrete event systems

2011

This paper deals with the problem of fault diagnosis of complex discrete event systems in the context of communicating timed automata. Indeed, for the diagnosis, this kind of systems can be represented by timed models whose components communicate through channels. This paper starts with a description of our modelling methodology of discrete event systems as communicating timed automata. The proposed approach for diagnosis (detection and isolation) is based on the methodology known as the diagnoser approach. This paper extends the approach of diagnoser through the taking into account of the various communicating synchronized automata representing the components of the system. It proposes an automatic step of construction of the global model. The application of the proposed algorithm allows to obtain the diagnoser of the studied system. Starting from a model of the complex system, this approach computes a deterministic automaton, called a diagnoser, which uses observable events to detect the occurrence of a failure. The different steps of the proposed method are described by algorithms and illustrated through a batch process.