Sensor Network Attack Synthesis against Fault Diagnosis of Discrete Event Systems.

This paper investigates the problem of synthesizing network attacks against fault diagnosis in the context of discrete event systems (DESs). It is assumed that the sensor observations sent to the operator that monitors a system are tampered with by an active attacker. We first formulate the process of online fault diagnosis under attack. Then, from the attack viewpoint, we define a sensor network attacker as successful if it can degrade the fault diagnosis in the case of maintaining itself as undiscovered by the operator. To verify such an attacker, an information structure called a joint diagnoser (JD) is proposed, which describes all possible attacks in a given attack scenario. Based on the refined JD, i.e., stealthy joint diagnoser (SJD), we present an algorithmic procedure for synthesizing a successful attacker if it exists.

A Distributed Supervisor Architecture for a General Wafer Production System.

The current trend in the wafer production industry is to expand the production chain with more production stations, more buffers, and robots. The goal of the present paper is to develop a distributed control architecture to face this challenge by controlling wafer industrial units in a general production chain, with a parametric number of production stations, one robot per two stations where each robot serves its two adjacent production stations, and one additional robot serving a parametric number of stations. The control architecture is analyzed for individual control units, one per robot, monitoring appropriate event signals from the control units of the adjacent robots. Each control unit is further analyzed to individual supervisors. In the present paper, a modular parametric discrete event model with respect to the number of production stations, the number of buffers, and the number of robotic manipulators is developed. A set of specifications for the total system is proposed in the form of rules. The specifications are translated and decomposed to a set of local regular languages for each robotic manipulator. The distributed supervisory control architecture is developed based on the local regular languages, where a set of local supervisors are designed for each robotic manipulator. The desired performance of the total manufacturing system, the realizability, and the nonblocking property of the proposed architecture is guaranteed. Finally, implementation issues are tackled, and the complexity of the distributed architecture is determined in a parametric formula. Overall, the contribution of the present paper is the development of a parametric model of the wafer manufacturing systems and the development of a parametric distributed supervisory control architecture. The present results provide a ready-to-hand solution for the continuously expanding wafer production industry.

Modular Supervisory Control for the Coordination of a Manufacturing Cell with Observable Faults.

In the present paper, a manufacturing cell in the presence of faults, coming from the devices of the process, is considered. The modular modeling of the subsystems of the cell is accomplished using of appropriate finite deterministic automata. The desired functionality of the cell as well as appropriate safety specifications are formulated as eleven desired languages. The desired languages are expressed as regular expressions in analytic forms. The languages are realized in the form of appropriate general type supervisor forms. Using these forms, a modular supervisory design scheme is accomplished providing satisfactory performance in the presence of faults as well guaranteeing the safety requirements. The aim of the present supervisor control scheme is to achieve tolerance of basic characteristics of the process coordination to upper-level faults, despite the presence of low-level faults in the devices of the process. The complexity of the supervisor scheme is computed.

An Automated Machine Learning Approach for Real-Time Fault Detection and Diagnosis.

This work presents a novel Automated Machine Learning (AutoML) approach for Real-Time Fault Detection and Diagnosis (RT-FDD). The approach's particular characteristics are: it uses only data that are commonly available in industrial automation systems; it automates all ML processes without human intervention; a non-ML expert can deploy it; and it considers the behavior of cyclic sequential machines, combining discrete timed events and continuous variables as features. The capacity for fault detection is analyzed in two case studies, using data from a 3D machine simulation system with faulty and non-faulty conditions. The enhancement of the RT-FDD performance when the proposed approach is applied is proved with the Feature Importance, Confusion Matrix, and F1 Score analysis, reaching mean values of 85% and 100% in each case study. Finally, considering that faults are rare events, the sensitivity of the models to the number of faulty samples is analyzed.

A grid-shaped cellular modeling approach for wireless sensor networks.

WSN (Wireless Sensor Network) applications have been widely used in recent years. We introduce a new method for modeling WSN, based on the specification of the WSN using the Cell-Discrete-Event Systems Specification (DEVS) formalism: the space is partitioned into cells where each cell can be considered a sensor, an obstacle, or anything of a behavior with defined rules. This model is then converted automatically into DEVS model at runtime. We present two case studies analyzing the use of energy in WSN member nodes, which have impact on prolonging the overall network lifetime. We study to analyze energy consumption related to routing and data transmission at the node level, and topology residual energy control methods at the cluster level (i.e. group of sensors) level. The goal is to show how these spatial modeling methods can be used for building WSN models in a simple but efficient fashion.

Systemic Design Approach to a Real-Time Healthcare Monitoring System: Reducing Unplanned Hospital Readmissions.

Following hospital discharge, millions of patients continue to recover outside formal healthcare organizations (HCOs) in designated transitional care periods (TCPs). Unplanned hospital readmissions of patients during TCPs adversely affects the quality and cost of care. In order to reduce the rates of unplanned hospital readmissions, we propose a real-time patient-centric system, built around applications, to assist discharged patients in remaining at home or in the workplace while being supported by care providers. Discrete-event system modeling techniques and supervisory control theory play fundamental roles in the system's design. Simulation results and analysis show that the proposed system can be effective in documenting a patient's condition and health-related behaviors. Most importantly, the system tackles the problem of unplanned hospital readmissions by supporting discharged patients at a lower cost via home/workplace monitoring without sacrificing the quality of care.

Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks.

Nowadays many studies are being conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and how to exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is proposed to provide a solution for mobility problems in cities. The interconnected traffic lights controller (TLC) network adapts traffic lights cycles, based on traffic and air pollution sensory information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease the pollution level. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach is applied to design a consensus-based control algorithm. The designed control strategy contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of TLC network components. Discrete event systems specification is applied for modelling and simulation. The proposed solution is assessed by simulation studies with very promising results to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks.

Optimal control of discrete event systems under uncertain environment based on supervisory control theory and reinforcement learning.

Discrete event systems (DESs) are powerful abstract representations for large human-made physical systems in a wide variety of industries. Safety control issues on DESs have been extensively studied based on the logical specifications of the systems in various literature. However, when facing the DESs under uncertain environment which brings into the implicit specifications, the classical supervisory control approach may not be capable of achieving the performance. So in this research, we propose a new approach for optimal control of DESs under uncertain environment based on supervisory control theory (SCT) and reinforcement learning (RL). Firstly, we use SCT to gather deliberative planning algorithms with the aim to safe control. Then we convert the supervised system to Markov Decision Process simulation environments that is suitable for optimal algorithm training. Furthermore, a SCT-based RL algorithm is designed to maximize performance of the system based on the probabilistic attributes of the state transitions. Finally, a case study on the autonomous navigation task of a delivery robot is provided to corroborate the proposed method by multiple simulation experiments. The result shows the proposed approach owning 8.27 % performance improvement compared with the non-intelligent methods. This research will contribute to further studying the optimal control of human-made physical systems in a wide variety of industries.

Identification of unobservable behavior in stochastic discrete event systems with a low number of sensors.

Dynamic discrete event systems (DDES) are systems that evolve from the asynchronous occurrence of discrete events. Their versatility has become a critical modeling tool in different applications. Finding models that define the behavior of DES is a topic that has been addressed from different approaches, depending on the type of system to be modeled and the model's objective. This article focuses on the identification of timed models for stochastic discrete event systems. The identified model includes both observable and unobservable behavior. The objective of the method is achieved through the following steps:•Identifying the sequences of events observed at different time instances during the closed-loop operation of the system (observed language),•Inferring the stochastic behavior of time between events and modeling the observable behavior as a stochastic timed Interpreted Petri Net (st-IPN),•and finally, inferring the non-observable behavior using the language projection operation between the observed language and the language generated by the st-IPN.This method has novel aspects because it uses timed events, can be applied to systems with a low number of sensors and can infer unobservable behavior for any sequence of events.

Fault diagnosis for discrete events systems described by partially observed Petri nets.

Fault diagnosis problem is discussed for discrete events systems described by partially observed Petri nets. Our goal is to detect and identify faults that may have occurred in both transitions and places assuming that we can measure some of them. The proposed approach is based on two mains steps (i) designing an algebraic observer for estimating the markings and the transitions of a partially observed Petri nets and (ii) presenting algorithms to detect and identify faults based on comparing the estimation of both the transitions and markings of the faulty system provided by an algebraic observer with those of the normal system. The effectiveness of the proposed approach is illustrated through a simple and a manufacturing example.

Reliable predictability of patterns in decentralized discrete-event systems.

Predictability is an important property which is used to predict the failures which is not observable for the sensors straightly before they occur. In an automation system, in addition to the failure caused by a single event, there also exist pattern failures caused by event strings composed of multiple events. In order to prevent some local sites malfunction, the issue of reliable predictability of patterns is considered in this paper, where the prediction information may be distributed at physically separated sites. Our contributions are listed mainly as follows: Firstly, the k-reliable pattern copredictability in decentralized DESs is defined with formal languages. Generally speaking, for a decentralized system where there are r local sites, it is said to be k-reliably pattern copredictable (1≤k≤r) if there are at least r-k+1 local agents which can predict every occurrences of the pattern failure for every pattern failure, it indicates that the prognostication capability will be maintained while r-k local sites in malfunction state. Then two nondeterministic automata respectively named codiagnoser and coverifier from the given system are constructed in this paper, and two algorithms of verifying the reliable copredictability of pattern are presented by constructing the codiagnoser and coverifier respectively for the purpose of attain the capability of prognostication. Especially, two necessary and sufficient conditions under the codiagnoser and coverifier are proposed. Moreover, for the decentralized DESs, the verification algorithm related to the k-reliable pattern copredictability is proposed after presenting the necessary and sufficient conditions for reliable pattern copredictability. It is worth noting that a polynomial complexity algorithm is used in constructing the coverifier and verifying the k-reliable pattern copredictability.

Model abstraction for discrete-event systems by binary linear programming with applications to manufacturing systems.

Model abstraction for finite state automata is helpful for decreasing computational complexity and improving comprehensibility for the verification and control synthesis of discrete-event systems (DES). Supremal quasi-congruence equivalence is an effective method for reducing the state space of DES and its effective algorithms based on graph theory have been developed. In this paper, a new method is proposed to convert the supremal quasi-congruence computation into a binary linear programming problem which can be solved by many powerful integer linear programming and satisfiability (SAT) solvers. Partitioning states to cosets is considered as allocating states to an unknown number of cosets and the requirement of finding the coarsest quasi-congruence is equivalent to using the least number of cosets. The novelty of this paper is to solve the optimal partitioning problem as an optimal state-to-coset allocation problem. The task of finding the coarsest quasi-congruence is equivalent to the objective of finding the least number of cosets. Then the problem can be solved by optimization methods, which are respectively implemented by mixed integer linear programming (MILP) in MATLAB and binary linear programming (BLP) in CPLEX. To reduce the computation time, the translation process is first optimized by introducing fewer decision variables and simplifying constraints in the programming problem. Second, the translation process formulates a few techniques of converting logic constraints on finite automata into binary linear constraints. These techniques will be helpful for other researchers exploiting integer linear programming and SAT solvers for solving partitioning or grouping problems. Third, the computational efficiency and correctness of the proposed method are verified by two different solvers. The proposed model abstraction approach is applied to simplify the large-scale supervisor model of a manufacturing system with five automated guided vehicles. The proposed method is not only a new solution for the coarsest quasi-congruence computation, but also provides us a more intuitive understanding of the quasi-congruence relation in the supervisory control theory. A future research direction is to apply more computationally efficient solvers to compute the optimal state-to-coset allocation problem.

Temporal-Fault Diagnosis for Critical-Decision Making in Discrete-Event Systems.

Since its appearance in AI, model-based diagnosis is intrinsically set-oriented. Given a sequence of observations, the diagnosis task generates a set of diagnoses, or candidates, each candidate complying with the observations. What all the approaches in the literature have in common is that a candidate is invariably a set of faulty elements (components, events, or otherwise). In this paper, we consider a posteriori diagnosis of discrete-event systems (DESs), which are described by networks of components that are modeled as communicating automata. The diagnosis problem consists in generating the candidates involved in the trajectories of the DES that conform with a given temporal observation. Oddly, in the literature on diagnosis of DESs, a candidate is still a set of faulty events, despite the temporal dimension of trajectories. In our view, when dealing with critical domains, such as power networks or nuclear plants, set-oriented diagnosis may be less than optimal in explaining the supposedly abnormal behavior of the DES, owing to the lack of any temporal information relevant to faults, along with the inability to discriminate between single and multiple occurrences of the same fault. Embedding temporal information in candidates may be essential for critical-decision making. This is why a temporal-oriented approach is proposed for diagnosis of DESs, where candidates are sequences of faults. This novel perspective comes with the burden of unbounded candidates and infinite collections of candidates, though. To cope with, a notation based on regular expressions on faults is adopted. The diagnosis task is supported by a temporal diagnoser, a flexible data structure that can grow over time based on new observations and domain-dependent scenarios.

Petri Net controller synthesis based on decomposed manufacturing models.

Utilizing of supervisory control theory on the real systems in many modeling tools such as Petri Net (PN) becomes challenging in recent years due to the significant states in the automata models or uncontrollable events. The uncontrollable events initiate the forbidden states which might be removed by employing some linear constraints. Although there are many methods which have been proposed to reduce these constraints, enforcing them to a large-scale system is very difficult and complicated. This paper proposes a new method for controller synthesis based on PN modeling. In this approach, the original PN model is broken down into some smaller models in which the computational cost reduces significantly. Using this method, it is easy to reduce and enforce the constraints to a Petri net model. The appropriate results of our proposed method on the PN models denote worthy controller synthesis for the large scale systems.