Unconditional decentralized structure for the fault diagnosis of discrete event systems (original) (raw)

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.

Fault diagnosis decentralized of manufacturing systems using Boolean models

International Journal of Power Electronics and Drive Systems (IJPEDS), 2024

This paper introduces an approach decentralized to fault detection and isolation (FDI) in manufacturing systems using a Boolean discrete event model. The method incorporates diverse information sources to create distinct models for plant systems and control. The objective is to enhance the understanding of process operations by employing various representation tools tailored to each information source. It is to reduce the number of explosion problems combinatorial and detect faults in the shortest possible time. This comprehensive representation facilitates the fulfillment of three crucial diagnosis functions: detection, localization, and identification. The approach involves Boolean modeling of each process actuator along with its corresponding sensors, a temporal model based on fuzzy expectations of event occurrences, and a set of if...then rules. The goal of this decentralized approach minimize both the complexity and the manual construction effort required for the model. The paper demonstrates the effectiveness of this approach through an illustrative example involving manufacturing systems.

New results on decentralized diagnosis of discrete-event systems

2004

The past decade has witnessed the development of a body of theory, with associated applications, for fault diagnosis of dynamic systems that can be modeled in a discrete event systems framework. This paper presents several new notions of diagnosability, together with on-line diagnosis decision rules, in the context of a general decentralized architecture that allows for the use of "conditional decisions" by local diagnosers. The properties of these new notions of diagnosability are presented and their relationship with existing work discussed. Verification algorithms and local diagnoser synthesis methods are briefly outlined.

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.

Modular Fault Diagnosis Based on Discrete Event Systems

Discrete Event Dynamic Systems, 2005

An awareness of failure type and location is an indispensable requirement for the establishment of adequate recovery strategies and the maintenance of Factory Automation and Process Control systems.

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.

Decentralized Failure Diagnosis of Discrete Event Systems

IEEE Transactions on Systems, Man, and Cybernetics, 2006

By decentralized diagnosis we mean diagnosis using multiple diagnosers, each possessing its own set of sensors, without involving any communication among diagnosers or to any coordinators. The notion of decentralized diagnosis is formalized by introducing the notion of codiagnosability that requires that a failure be detected by one of the diagnosers within a bounded delay. Algorithms of complexity polynomial in the size of the system and the nonfault specification are provided for: 1) testing codiagnosability, 2) computing the bound in delay of diagnosis, 3) offline synthesis of individual diagnosers, and 4) online diagnosis using them. The notion of codiagnosability and the above algorithms are initially presented in a setting of a specification language (violation of which represents a fault) and are later specialized to the case where faults are modeled as the occurrences of certain events. The notion of strong codiagnosability is also introduced to capture the ability of being certain about both the failure as well as the nonfailure conditions in a system within a bounded delay.

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...

Diagnosability Analysis of Discrete Event Systems with Autonomous Components

European Conference on Artificial Intelligence, 2010

Diagnosability is the property of a given partially observable system model to always exhibit unambiguously a failure behavior from its only available observations in finite time after the fault occurrence, which is the basic question that underlies diagnosis taking into account its requirements at design stage. However, for the sake of simplicity, the previous works on diagnosability analysis of discrete event systems (DESs) have the same assumption that any observable event can be globally observed, which is at the price of privacy. In this paper, we first briefly describe cooperative diagnosis architecture for DESs with autonomous components, where any component can only observe its own observable events and thus keeps its internal structure private. And then a new definition of cooperative diagnosability is consequently proposed. At the same time, we present a formal framework for cooperative diagnosability checking, where global consistency of local diagnosability analysis can be achieved by analyzing communication compatibility between local twin plants without any synchronization. The formal algorithm with its discussion is provided as well. 2 PRELIMINARIES In this section, we first describe how to model DESs with autonomous components and then give some important concepts before proposing cooperative diagnosis architecture for such systems. 2.1 System model We consider a distributed DES composed of a set of autonomous components {G 1 , G 2 ,..., G n } that communicate with each other by communication events. Moreover, any component can only observe its own observable events and thus can keep its internal structure private. This kind of system is modeled by a set of FSMs with each one representing the local model of one component.