Fault Diagnosis Of Discrete Event Systems Research Articles

An effective approach for fault diagnosis of Discrete-Event Systems modeled as safe labeled Petri nets

In this paper, a fault diagnosis method for Discrete-Event Systems (DES) modeled as safe labeled Petri nets (LPN) is proposed. We assume that some transitions of the Petri net are unobservable, including the fault transitions. The diagnosis method consists of two steps: (i) in the first step, online fault detection is carried out; and (ii) then, by using the observed sequence of events and the labeled Petri net system model, the fault candidates are isolated. The online fault detection is based on the construction of an LPN from the fault-free system behavior model, called observable behavior Petri net (OBPN), whose transitions are all observable, and whose generated language is guaranteed to be equal to the observable language of the fault-free system model when some conditions are satisfied. It is also shown that the OBPN can be implemented for fault detection instead of implementing the reachability graph, or even part of it, which leads to a fast fault detection method without requiring the use of a large amount of memory. Two case studies are presented to illustrate the proposed method.

Fault Diagnosis of Discrete-Event Systems from Abstract Observations

Fault Diagnosis of Discrete-Event Systems from Abstract Observations

Fault diagnosis in labelled Petri nets: A Fourier–Motzkin based approach

We propose techniques for fault diagnosis in discrete-event systems modelled by labelled Petri nets, where fault events are modelled as unobservable transitions. The proposed approach combines an offline and an online algorithm. The offline algorithm constructs a diagnoser in the form of sets of inequalities that capture the legal, normal and faulty behaviour. To implement the offline algorithm, we adopt the Fourier–Motzkin method for elimination of variables from these sets of inequalities. Upon observing an event, the diagnoser is used to determine whether a fault occurred or might have occurred. The occurrence of a fault can be verified by checking the observed sequence against the sets of inequalities. This approach has the advantage that the tradeoff between the size of the diagnoser and the time for computing the diagnosis is achieved. In addition, fault diagnosis in both bounded and unbounded Petri nets can be addressed.

Fault diagnosis of discrete-event systems under a general architecture

Diagnosability is an important characteristic indicator to determine whether the system is stable and reliable. In this paper, the general architecture of event-state-combination diagnosability is investigated. The contributions are threefold. First, the notion of event-state-combination diagnosability is formalized. Roughly speaking, an event-state-combination diagnosable system means that not only each combined fault can be detected, but also the system can determine whether it will work permanently in the failure states after the combined fault occurs. Then, an automaton with new information structure, called event-state-combination verifier, is constructed, which can be used for the verification of the event-state-combination diagnosability. Finally, the necessary and sufficient conditions for verifying whether the system is event-state-combination diagnosable is presented, that is, the event-state-combination verifier does not have any failure confused cycle.

State-based fault diagnosis of discrete-event systems with partially observable outputs

This paper presents a state-based method for solving the problems of diagnosis and diagnosability of discrete-event systems (DES) with partially observable outputs, due to the lack or limitations of sensors. The diagnoser used for diagnosis consists of two parts: a state estimator and a failure decision-maker. The state estimator makes the state estimation of a system based on the observed output sequence and transfers the estimation to the failure decision-maker that determines whether a fault occurs or not. Moreover, the state or condition (failure status) of the system is not required to be known when launching the diagnoser; thus the system and the diagnoser do not have to be initialized simultaneously, i.e., the diagnoser may be initialized at any moment while the system is operational. Under the premise that the outputs of a system are partially observable, the notion of diagnosability is given and an efficient algorithm for verification of diagnosability is designed with the polynomial computational complexity with respect to the number of system states. Finally, the proposed algorithm is applied to a pump-valve-controller system.

Fault diagnosis based on identified discrete-event models

Fault diagnosis of Discrete-Event Systems consists of detecting and isolating the occurrence of faults within a bounded number of event occurrences. Recently, a new model for discrete-event system identification with the aim of fault detection, called Deterministic Automaton with Outputs and Conditional Transitions (DAOCT), has been proposed in the literature. The model is computed from observed fault-free paths, and represents the fault-free system behavior. In order to obtain compact models, loops are introduced in the model, which implies that sequences that are not observed can be generated leading to an exceeding language. This exceeding language is associated with possible non-detectable faults, and must be reduced in order to use the model for fault detection. After detecting the fault occurrence, its isolation is carried out by analyzing residuals. In this paper, we present a fault diagnosis scheme based on the DAOCT model. We show that the proposed fault diagnosis scheme is more efficient than other approaches proposed in the literature, in the sense that the exceeding language can be drastically reduced, reducing the number of non-detectable fault occurrences, and, in some cases, reducing also the delay for fault diagnosis. A practical example, consisting of a plant simulated by using a 3D simulation software controlled by a Programmable Logic Controller, is used to illustrate the results of the paper.

Fault Diagnosis of Discrete-Event systems Based on the Symbolic Observation Graph

Fault diagnosis of discrete-event systems (DESs) has received a lot of attention in industry and academia during the last two decades. In DES based diagnosis, the two main discussed topics are offline diagnosability analysis and online diagnosis. A pioneering approach that led to the development of various techniques is based on the so-called diagnose. However, this approach suffers from the combinatorial explosion problem due to the exponential complexity of construction. To partially overcome this problem, an efficient approach to construct a symbolic diagnoser is proposed in this paper. The proposed approach consists in constructing a diagnoser based on the symbolic observation graph (SOG), which combines symbolic and enumarative representations. The construction of the diagnoser as well as the verification of diagnosability are performed simultaneously on the fly, which can considerably reduce the state space of the diagnoser and thus the overall running time. To evaluate the efficiency and the scalability of the approach, some experimental results are presented and discussed based on a DES benchmark.

Fault diagnosis of discrete-event systems based on the symbolic observation graph

Fault diagnosis of discrete-event systems (DESs) has received a lot of attention in industry and academia during the last two decades. In DES based diagnosis, the two main discussed topics are offline diagnosability analysis and online diagnosis. A pioneering approach that led to the development of various techniques is based on the so-called diagnose. However, this approach suffers from the combinatorial explosion problem due to the exponential complexity of construction. To partially overcome this problem, an efficient approach to construct a symbolic diagnoser is proposed in this paper. The proposed approach consists in constructing a diagnoser based on the symbolic observation graph (SOG), which combines symbolic and enumarative representations. The construction of the diagnoser as well as the verification of diagnosability are performed simultaneously on the fly, which can considerably reduce the state space of the diagnoser and thus the overall running time. To evaluate the efficiency and the scalability of the approach, some experimental results are presented and discussed based on a DES benchmark.

Modular fault diagnosis in discrete-event systems with a CPN diagnoser

This paper addresses the problem of fault diagnosis in discrete-event system. The system under investigation is modelled as a labelled Petri net. We first propose an equivalent encoding of the classical diagnoser with the help of a CPN diagnoser. We then apply this encoding to define a modular CPN diagnoser.

Fault Diagnosis in Discrete-Event Systems with Incomplete Models: Learnability and Diagnosability.

Most model-based approaches to fault diagnosis of discrete-event systems require a complete and accurate model of the system to be diagnosed. However, the discrete-event model may have arisen from abstraction and simplification of a continuous time system, or through model building from input-output data. As such, it may not capture the dynamic behavior of the system completely. In a previous paper, we addressed the problem of diagnosing faults given an incomplete model of the discrete-event system. We presented the learning diagnoser which not only diagnoses faults, but also attempts to learn missing model information through parsimonious hypothesis generation. In this paper, we study the properties of learnability and diagnosability. Learnability deals with the issue of whether the missing model information can be learned, while diagnosability corresponds to the ability to detect and isolate a fault after it has occurred. We provide conditions under which the learning diagnoser can learn missing model information. We define the notions of weak and strong diagnosability and also give conditions under which they hold.

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

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.

Diagnose de falhas em sistemas a eventos discretos modelados por autômatos finitos

Este tutorial apresenta os fundamentos necessários para o estudo e a pesquisa em diagnose de falhas de sistemas a eventos discretos modelados por autômatos. Tanto a diagnose centralizada quanto a descentralizada com coordenação (codiagnose) são consideradas. Além de apresentar as condições necessárias e suficientes para a verificação da diagnosticabilidade e codiagnosticabilidade, este artigo também apresentada testes utilizando diagnosticadores e verificadores. Resultados recentes envolvendo a diagnose centralizada em sistemas sob observação parcial são também considerados.

Fault Diagnosis in Discrete-Event Systems: Incomplete Models and Learning

Most model-based approaches to fault diagnosis of discrete-event systems (DESs) require a complete and accurate model of the system to be diagnosed. However, the discrete-event model may have arisen from abstraction and simplification of a continuous time system or through model building from input-output data. As such, it may not capture the dynamic behavior of the system completely. In this paper, we address the problem of diagnosing faults, given an incomplete model of the discrete-event system. When the model is incomplete, discrepancies will arise between the actual output and the output predicted by the model. We introduce learning into the diagnoser construction by forming hypotheses that explain these discrepancies. We view the process of generating and evaluating hypotheses about the model of the system as an instance of the set-cover problem, which we formalize using parsimonious covering theory. We describe in detail the construction of the learning diagnoser, which not only performs fault diagnosis but also attempts to learn the missing model information. If the model is complete, the learning diagnoser reduces to the standard state-based diagnoser. Examples are provided to illustrate how learning and diagnosis can be simultaneously achieved through the learning diagnoser.

Robust diagnosis of discrete-event systems subject to permanent sensor failures

One approach to online fault diagnosis of discrete-event systems is through the use of the diagnosers. Diagnosers are deterministic automata whose states are sets formed with the states of the plant together with labels that indicate if the trace that has occurred so far possesses or not the fault event. The decision regarding fault occurrence is taken based solely on observable events, i.e., events whose occurrences can be recorded by sensors. However, if one or more sensors that provide information on event occurrences fail, the diagnoser may either come to a halt or may even provide wrong information regarding fault occurrence. In order to overcome this deficiency, this paper proposes a robust diagnoser that deploys the redundancy that may exist in a set formed of diagnosis bases (set of events that guarantee fault diagnosability) with a view to ensure the fault diagnosis even in the occurrence of permanent sensor failures.

Hierarchical Fault Diagnosis for Discrete-Event Systems under Global Consistency

In previous work the authors proposed a distributed diagnosis approach consisting of two phases--preliminary diagnosis in each local diagnoser and inter-diagnoser communication. The objective of communication is to achieve either global or local consistency among local diagnoses, where global consistency is captured by the equilibrium concept of supremal global support. To achieve this equilibrium, an algorithm called Computational Procedure for Global Consistency (CPGC) was proposed. But it turns out that CPGC has high time complexity and weak scalability. To rectify these shortcomings, we propose a hierarchical computational procedure. A further advantage of this procedure is demonstrated, based on multiresolutional diagnosis. With the latter, fault detection is conducted at each hierarchical level, so that computation can be confined to those modules likely to possess faults, while fault-free modules are safely disregarded. A simplified industrial example is provided in illustration.

TOWARDS LOW-COST FAULT DIAGNOSIS IN LARGE COMPONENT-BASED SYSTEMS 1

We address the problem of fault diagnosis in discrete-event systems. Our contribution is the development of a set of specialised diagnosers whose computation is much more realistic than that of the classical diagnoser. A specialised diagnoser is devoted to the diagnosis of one particular type of fault and is based on the observation of only a subpart of the system.

Global and local consistencies in distributed fault diagnosis for discrete-event systems

In this paper, we present a unified framework for distributed diagnosis. We first introduce the concepts of global and local consistency in terms of supremal global and local supports, then present two distributed diagnosis problems based on them. After that, we provide algorithms to achieve supremal global and local supports respectively, and discuss in detail the advantages and disadvantages of each. Finally, we present an industrial example to demonstrate our distributed diagnosis approach.

Fault diagnosis in discrete-event systems: incorporating timing information

A framework is introduced for fault diagnosis in timed discrete-event systems. In this approach, the required estimates for system condition are updated only when the output changes or when deadlines associated with output changes expire. Thus updates at every clock tick are not required. This in many cases results in reduction in online computing requirements and in the size of the diagnosis system, at the expense of more offline design calculations. The issue of failure diagnosability is also discussed.

Diagnosis of timed automata: Theory and application to the DAMADICS actuator benchmark problem

This paper concerns the problem of fault diagnosis in discrete-event systems which are represented by timed automata. The diagnostic algorithm for timed automata detects and identifies faults in the system based on the investigation whether the measured input and output sequences are consistent with the timed automaton. This diagnostic approach can be applied spontaneously to the discrete-event system since no a priori information about the initial state of the system is required. It is shown in the paper how the timed automaton which represents the DAMADICS actuator can be obtained and how the diagnostic algorithm based on the timed automaton is applied to detect and identify actuator faults. A representative diagnostic result is presented and discussed to illustrate the effectiveness of the method.

Fault diagnosis in discrete-event systems: Framework and model reduction

A state-based approach for online passive fault diagnosis in systems modeled as finite-state automata is presented. In this framework, the system and the diagnoser (the fault detection system) do not have to be initialized at the same time. Furthermore, no information about the state or even the condition (failure status) of the system before the initiation of diagnosis is required. The design of the fault detection system, in the worst case, has exponential complexity. A model reduction scheme with polynomial time complexity is introduced to reduce the computational complexity of the design. Diagnosability of failures is studied, and necessary and sufficient conditions for failure diagnosability are derived.