Fault diagnosis based on identified discrete-event models

International audienceFault 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

A comparative study of three model-based FDI approaches for Discrete Event Systems

6 pagesInternational audienceIn this paper three model-based Fault Detection and Isolation (FDI) approaches for Discrete Event Systems (DES) are evaluated. The considered approaches are the diagnoser approach, the templates approach and the residual approach. The investigated methods have different characteristics like timed / non-timed behavior and fault-free / faulty system models with important impacts on the model-building process and the respective effectiveness. By applying the three methods to the same benchmark system, their respective performances are analyzed in terms of fault detection and fault isolation ability, complexity of implementation and avoidance of false alarms

A Discrete event model for multiple inhabitants location tracking

6 pagesInternational audienceSmart Home technologies are aiming to improve the comfort and safety of the inhabitants into their houses. To achieve this goal, online indoor location tracking of the inhabitants is often used to monitor the air conditioning, to detect dangerous situations and for many other applications. In this paper, it is proposed an approach to build a model allowing dynamic tracking of several persons in their house. A method to construct such a model by using finite automata and Discrete Event System (DES) paradigms is presented. An approach to reduce the size of the model is also introduced. Finally, an efficient algorithm for location tracking is proposed. For the sake of better understanding, an illustrative example is used throughout the paper

On Multi-Robot Path Planning Based on Petri Net Models and LTL specifications

This work considers the path planning problem for a team of identical robots evolving in a known environment. The robots should satisfy a global specification given as a Linear Temporal Logic (LTL) formula over a set of regions of interest. The proposed method exploits the advantages of Petri net models for the team of robots and B\"uchi automata modeling the specification. The approach in this paper consists in combining the two models into one, denoted Composed Petri net and use it to find a sequence of action movements for the mobile robots, providing collision free trajectories to fulfill the specification. The solution results from a set of Mixed Integer Linear Programming (MILP) problems. The main advantage of the proposed solution is the completeness of the algorithm, meaning that a solution is found when exists, this representing the key difference with our previous work in [1]. The simulations illustrate comparison results between current and previous approaches, focusing on the computational complexity.Comment: submitted to IEEE Transactions on Automatic Control, 202

Model of mechanism behavior for verification of PLC programs

More extensive work on formal methods is now available for checking PLC (Programmable Logic Controller) programs. To verify a PLC program, it is necessary to consider a set of properties to prove and one of the most interesting problems that the designers must deal is to deduce a set of properties that traduces all the safety requirements of the system behavior. In this paper, we explore the contribution of such a plant model within the context of deduction, in a systematized way, of a set of properties to prove, verifying the PLC program. Our study is primarily experimental in nature and based on a case study. A set of properties to be checked based on detailed plant model is proposed. We then analyze how a Symbolic Model-Checking tool (the NuSMV has been selected) ensures verification of these properties either with or without the considered plant model

Black-box identification of discrete event systems with optimal partitioning of concurrent subsystems

Abstract-This paper proposes a data-driven method to determine concurrent parts in Discrete Event Systems (DES). The aim is to improve the results of black-box identification methods without considering any system information except of observed data. To allow an analysis of the collected data, the impact of concurrency on the exhibited system data is determined by two criteria. We propose to use an optimization algorithm that isolates concurrent parts of the system by minimizing concurrency expressed by the two proposed criteria within the determined subsystems. A lab-size application shows the potential of the method for real-world manufacturing systems. The aim is to deliver optimal identified models for fault detection and isolation

Discovering Petri Net Models of Discrete-Event Processes by Computing T-Invariants

International audienceThis paper addresses the problem of discovering a Petri Net (PN) from a long event sequence representing the behavior of discrete-event processes. A method for building a 1-bounded PN able to execute the events sequence S is presented; it is based on determining causality and concurrence relations between events and computing the t-invariants. This novel method determines the structure and the initial marking of an ordinary PN, which reproduces the behavior in S. The algorithms derived from the method are efficient and have been implemented and tested on numerous examples of diverse complexity. Note to Practitioners—Model discovery is useful to perform reverse engineering of ill-known systems. The algorithms proposed in this paper build 1-bounded PN models, which are enough powerful to describe many discrete-event processes from industry. The efficiency of the method allows processing very large sequences. Thus, an automated modeling tool can be developed for dealing with data issued from real systems

Black-box identification of discrete event systems with optimal partitioning of concurrent subsystems

The work has been supported by a grant from “Région Ile de France”International audienceThis paper proposes a data-driven method to determine concurrent parts in Discrete Event Systems (DES). The aim is to improve the results of black-box identification methods without considering any system information except of observed data. In order to allow an analysis of the collected data, the impact of concurrency on the exhibited system data is determined by two criteria. We propose to use an optimization algorithm that isolates concurrent parts of the system by minimizing concurrency expressed by the two proposed criteria within the determined subsystems. A lab-size application shows the potential of the method for real-world manufacturing systems. The aim is to deliver optimal identified models for fault detection and isolation

Determination of timed transitions in identified discrete-event models for fault detection

International audienceModel-based fault detection compares modeled and observed behavior to decide whether a system operates properly or not. The key issue in this paper is to model large-cale Discrete Event Systems (DESs) with little a-priori knowledge. For this class of systems a new approach to black-box determination of timed transitions for timed automata is proposed. The method identifies a set of time guards leading to an advantageous trade-off between the fault detection errors: false alarms and missed detections. A real industrial system is applied for evaluation of time guard determination. It is shown that applying the proposed method results in a better trade-off between the fault detection errors than using common methods (e.g. Min/Max, Normal Distribution)