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This paper deals with the problem of service rate control for a retrial queue where the controller can observe only the number of total customers in the system. Service rates are adjustable based on the partial information (the arrival or departure event) instead of the perfect system state (customer distribution among orbit and server). The goal is to find the optimal service rates that minimize the long-run average cost. This problem is formulated as an event-based optimization instead of a state-based optimization. Using the sensitivity-based optimization theory, we obtain interesting structures of the optimal service rate control policy, which show that the optimal policy is a bang-bang control and even has a threshold form under some mild conditions. The necessary and sufficient condition of optimal policies is also derived. Furthermore, by the difference formula of the system performances under any two policies, we develop a policy iteration-type algorithm to find the optimal policy for the case with general cost functions. With different initial values of service rates, our algorithm is demonstrated to efficiently find the optimal service rates. Moreover, we study the difference of system performances from the corresponding state-based optimization by numerical experiments, which indicates the managerial insights about the value of information for decision-makers. [ABSTRACT FROM AUTHOR]

Manufacturing systems (MS) have become increasingly complex due to constraints induced by a changing environment, such as flexibility, availability, competition, and key performance indicators. This change has led to a need for flexible systems capable of adapting to production changes while meeting productivity and quality criteria and reducing the risk of failures. This paper provides a methodology for designing reconfigurable and fault-tolerant control for implementation in a Programmable Logic Controller (PLC). The main contribution of this methodology is based on a safe control synthesis founded on timed properties. If a sensor fault is detected, the controller switches from normal behavior to a degraded one, where timed information replaces the information lost from the faulty sensor. Switching between normal and degraded behaviors is ensured through reconfiguration rules. The primary objective of this method is to implement the obtained control into a PLC. In order to achieve this goal, a method is proposed to translate the controllers of the two behaving modes and the reconfiguration rules into different Grafcets. This approach relies on the modular architecture of manufacturing systems to avoid the combinatorial explosion that occurs in several approaches. [ABSTRACT FROM AUTHOR]

The feedback control system with network-connected components is vulnerable to cyberattacks. We study a problem of attack detection in supervisory control of discrete-event systems. The scenario of a system subjected to actuator enablement attacks is considered in this article. We also consider that some unsafe places that should be protected from an attacker exist in the system, and some controllable events that are disabled by a supervisor might be re-enabled by an attacker. This article proposes a defense strategy to detect actuator enablement attacks and disable all controllable events after detecting an attack. We design algorithmic procedures to determine whether the system can be protected against damage caused by actuator enablement attacks, where the damage is predefined as a set of "unsafe" places. In this way, the system property is called "AE-safe controllability". The safe controllability can be verified by using a basis diagnoser or a basis verifier. Finally, we explain the approach with a cargo system example. [ABSTRACT FROM AUTHOR]

The development of intelligent techniques based on real-time monitoring for machining applications is one of the challenges of Industry 4.0, as in the Aerospace Industry. Drilling is the most used process before the assembly of airplane sheets, that nowadays are composed of different layers of materials with different optimized cutting conditions. The fault diagnosis during drilling stack materials is important to reduce cost and improve the process quality. Using a machine-tool, it is important that the fault diagnoser does not use a large amount of memory and be capable of detecting faults in a fast manner. In this paper, we propose a timed automaton model representing the drilling process of a Titanium plate on a CNC machine, which is suitable for fault diagnosis without any additional sensors. The diagnoser uses only the spindle power and Z axis displacement read directly from the system controller. The target faults in this case are: (i) excessive tool-wear or tool breakage; (ii) the tool finds an off-centered hole while producing a blind-hole; (iii) the tool finds an under layer of a different material, as it occurs in a bi-layer material; and (iv) the plate thickness is below the desired one and a though hole is produced. The results show that the model is capable of identifying all faults and it could be used to alert a problem on the sequence of machining holes in the industry [ABSTRACT FROM AUTHOR]

In this paper, incremental computation of schedules for complex discrete event systems in an uncertain environment is studied. Uncertainties are assumed to occur due to uncontrollable events. A particular class of flexible manufacturing systems (FMSs) with partial precedence constraints is proposed where some operations are performed with total precedence constraints and others with full routing flexibility (namely partial routing FMSs). Interruptions may occur due to unavailability of resources and interruption of operations. Such interruptions may deviate the trajectory from the planed schedule. For the modeling of the partial routing FMS, a systematic multi-level formalism based on the hierarchical structuration of the operations is introduced. Then, the risk of deviation is integrated and a new cost function is defined accordingly. Finally, a modified beam search algorithm referred to as generation double filtered beam search algorithm that accelerates the convergence of the method is proposed. The new algorithm is based on a new filtering mechanism that uses the cost function to selectively explore the state space of Petri net model in order to find a control sequence from an initial state to a reference one with a trade-off between performance and robustness. Examples are used to illustrate the efficiency of the proposed scheduling approach. [ABSTRACT FROM AUTHOR]

Scheduling production is an important decision issue in the manufacturing domain. With the advent of the era of Industry 4.0, the basic generation of schedules becomes no longer sufficient to face the new constraints of flexibility and agility that characterize the new architecture of production systems. In this context, schedules must take into account an increasingly disrupted environment while maintaining a good performance level. This paper contributes to the identified field of smart manufacturing scheduling by proposing a complete process for assessing the robustness of schedule solutions: i.e., its ability to resist to uncertainties. This process focuses on helping the decision maker in choosing the best scheduling strategy to be implemented. It aims at considering the impact of uncertainties on the robustness performance of predictive schedules. Moreover, it is assumed that data upcoming from connected workshops are available, such that uncertainties can be identified and modelled by stochastic variables This process is supported by stochastic timed automata for modelling these uncertainties. The proposed approach is thus based on Stochastic Discrete Event Systems models and model checking techniques defining a highly reusable and modular process. The solution process is illustrated on an academic example and its performance (generecity and scalability) are deeply evaluated using statistical analysis. The proposed application of the evaluation process is based on the technological opportunities offered by the Industry 4.0. [ABSTRACT FROM AUTHOR]

In service-based manufacturing systems, functionalities are independently developed as services and a central engine orchestrates their integration. As industrial processes tend to be very large, and performance and productivity are expected to be maximised, there is a constant interest in providing (in-advance) quality guarantees for services interactions, which contrasts with the usual non-automated workflow design. This paper provides an alternative to enhance service orchestration capabilities using supervisory control techniques. Initially, each component (atomic and composite activities) belonging to an orchestration language is modelled as a state-machine. Then, activity models are properly combined and composed, reproducing orchestrated workflows. Finally, supervisory control is used to calculate an optimal version of the orchestrator. Practical implications of handling large state-spaces are discussed and examples are provided. [ABSTRACT FROM AUTHOR]

Collision Avoidance Systems require correct and unambiguous application of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). Ship-to-ship dialogs, aimed at sharing encounter data to comply with COLREGs and reaching maneuvering agreements, would help to reduce the risk of collision. Finite State Machine (FSM) is a mathematical model for describing the sequential behavior of a control program. Sequential function chart (SFC) based on FSM, is a graphical programming language for Programmable Logic Controllers, defined by the international standard IEC 61131-3. In this work, SFC language is used to model and program the set of states and transitions involved in the ship-to-ship dialogs initiated when one of them detects a risky situation. SFC facilitates the development, verification, and maintenance of the control program. The implemented ship-to-ship communications to share data will help in eliminating differences in decision-making and achieving safer encounters. An example of a risky encounter illustrates this assertion, not contemplated in the related studies consulted. The implemented dialogs will enable sharing information on the encounter characteristics and reaching agreements on the maneuvers to be performed, or maintaining a record about disagreements.

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. [ABSTRACT FROM AUTHOR]

An approach to the Colored Petri Nets (CPN)-based control is proposed in this paper. CPN are used for modeling the dynamics of both the controller and the controlled process in the control system structure. The mathematical model of the controlled process is discretized in order to use CPN in modeling the controlled process and the control system as well. The proposed controller implements a Moore automaton. The theoretical aspects of the controller design are presented in the paper, as well as aspects of the system controllability. The level control of three-tank systems is considered to validate the proposed controller. Extensive simulations are performed using the CPN Tools tool and experimental results on laboratory equipment are included. [ABSTRACT FROM AUTHOR]

In practical systems, due to reasons such as sensor faults, sensor limitations and packet losses in networks, the observation of events becomes nondeterministic, and makes observing system behavior particularly complex. Aiming at the problem that the same event string might have multiple observations in discrete event systems and the same event observations might be different in different states, this paper proposed a method for fault diagnosis and verification under nondeterministic observations. Firstly, it formalized the diagnosability of discrete event systems under nondeterministic observations. Secondly, it constructed a verifier for the above fault diagnosis verification. Based on the verifier, it proposed a necessary and sufficient conditions and the verification algorithm for the system to be diagnosable under nondeterministic observations. Finally, an example illustrates the application of the fault diagnosis verification algorithm under nondeterministic observations. Compared with the existing research, the observation value of the fault event is not constrained in this proposed method, and it can be zero or more observations, which makes the application of this method more extensive. [ABSTRACT FROM AUTHOR]

The complexity of industrial processes imposes a lot of challenges in building accurate and representative causal models for abnormal events diagnosis, control and maintenance of equipment and process units. This paper presents an innovative data-driven causality modeling approach using interpretable machine learning and process mining techniques, in addition to human expertise, to efficiently and automatically capture the complex dynamics of industrial systems. The approach tackles a significant challenge in the causality analysis community, which is the discovery of high-level causal models from low-level continuous observations. It is based on the exploitation of event data logs by analyzing the dependency relationships between events to generate accurate multi-level models that can take the form of various state-event diagrams. Highly accurate and trustworthy patterns are extracted from the original data using interpretable machine learning integrated with a model enhancement technique to construct event data logs. Afterward, the causal model is generated from the event log using the inductive miner technique, which is one of the most powerful process mining techniques. The causal model generated is a Petri net model, which is used to infer causality between important events as well as a visualization tool for real-time tracking of the system's dynamics. The proposed causality modeling approach has been successfully tested based on a real industrial dataset acquired from complex equipment in a Kraft pulp mill located in eastern Canada. The generated causality model was validated by ensuring high model fitness scores, in addition to the process expert's validation of the results. [ABSTRACT FROM AUTHOR]

The verifications of current-state opacity (CSO) and initial-state opacity (ISO) in discrete event systems (DESs) both suffer from the curse of dimensionality, as these issues were proved to be PSPACE-complete. Hence, how to reduce the state space is crucial. In this paper, we investigate CSO and ISO in modular DESs, which consist of several individual components. Necessary and sufficient conditions of CSO and ISO for modular DESs are derived under the assumption that all synchronous events are observable by each component of modular DESs. Moreover, we prove that the initial state estimator of modular system is isomorphic to the synchronous composition of initial state estimators for individual components. These results offer us the opportunity to reduce the complexity in verifying the opacity of modular DESs. [ABSTRACT FROM AUTHOR]

This work addresses the problem of quantifying opacity for discrete event systems. We consider a passive intruder who knows the overall structure of a system but has limited observational capabilities and tries to infer the secret of this system based on the captured information flow. Researchers have developed various approaches to quantify opacity to compensate for the lack of precision of qualitative opacity in describing the degree of security of a system. Most existing works on quantifying opacity study specified probabilistic problems in the framework of probabilistic systems, where the behaviors or states of a system are classified as secret or non-secret. In this work, we quantify opacity by a state-worth function, which associates each state of a system with the worth it carries. To this end, we present a novel category of opacity, called worthy opacity, characterizing whether the worth of information exposed to the outside world during the system’s evolution is below a threshold. We first provide an online approach for verifying worthy opacity using the notion of a run matrix proposed in this research. Then, we investigate a class of systems satisfying the so-called 1-cycle returned property and present a worthy opacity verification algorithm for this class. Finally, an example in the context of smart buildings is provided.

The symmetry between customer expectations and operator goals, on one hand, and the digital transition of the railways, on the other hand, is one of the main factors affecting green transport sustainability. The European Train Control System (ETCS) was created to improve interoperability between different railway signaling systems and increase safety and security. While there are a lot of ETCS Level 2 deployments all over the world, the specifications of ETCS Level 3 are under development. ETCS Level 3 is expected to have a significant impact on automatic train operation, protection, and supervision. In this paper, we present an innovative control system architecture that allows the incorporation of artificial intelligence (AI)/machine learning (ML) applications. The architecture features control function virtualization and programmability. The concept of an intelligent railway controller (IRC) is introduced as being a piece of cloud software responsible for the control and optimization of railway operations. A microservices-based approach to designing the IRC’s functionality is presented. The approach was formally verified, and some of its performance metrics were identified.

Petri nets (PNs) can model various event-driven systems well and detect a fault via reachability analysis. Some state-equation-based approaches are designed for only subclasses of PNs with specific structures. A key issue for PNs’ reachability analysis approach based on state equation is that a non-negative integer solution (NIS) to a state equation is necessary but not sufficient for a marking’s reachability. In this paper, we propose a circuit-based subnet. For ordinary Petri nets with token-free circuit-based subnets, we present an algorithm to determine that the state equation has NISs but the marking is non-reachable. We adopt a divide-and-conquer strategy to consider each circuit-based subnet. The proposed method is tested on a PN-based flexible manufacturing system with unreliable resources. When unreliable resources are lost, the non-reachable markings can be determined effectively. Besides, simulations are done, and the results verify the efficiency of the proposed algorithm compared with the reachability-tree-based method.

Extracting key system parameters and their impact on state transition is a necessity for knowledge and data engineering. In Decision Support Systems, the quest for yet more efficient and faster methods of sensitivity analysis (SA) and feature extraction in complex and volatile systems persists. A new improved event tracking methodology, the fastTracker, for real-time SA in large scale complex systems is proposed in this paper. The main feature of fastTracker is its high-frequency analytics using meager computational cost. It is suitable for data processing and prioritization in embedded systems, Internet of Things (IoT), distributed computing (e.g. Edge computing) applications. The presented algorithm’s underpinning rationale is event driven; its objective is to correctly and succinctly quantify the sensitivity of observable changes in the system (output) with respect to the input variables. To demonstrate the performance of the proposed fastTracker methodology, fastTracker was deployed in the Supervisory control and data acquisition (SCADA) system from real cement industry. fastTracker has been verified by system experts in real industrial application. Its performance was compared with other real-time event-based SA techniques. The comparison revealed savings of 98.8% in processing time per sensitivity index and 20% in memory usage when compared with EventTracker, its closest rival. The proposed methodology is more accurate and 80.9% faster than an entropy-based method. Its application is recommended for reinforced learning and/or formulating system key performance indicators from raw data.

This paper presents the descriptive compositional approach for uncertainty modelling and performance evaluation of dynamic reconfigurable discrete event systems (ReDES) using rewriting stochastic reward nets (ReSRN) with Z-fuzzy parameters (FReSRN) that can modify in run-time their own structure by the rewriting of the rules. The expected Z-fuzzy values of the transition and rewriting rule firing rates are calculated based on credibility theory, the FReSRN model is degenerated to a conventional ReSRN model. A case study for performance modelling and analysis of particular ReDES is given in order to show the effectiveness of the proposed method.

'Topological reverse mirroring' (TRM) is a new knowledge-based methodology for more efficiently analysing reachability to derive the closed-form formulas (CFF) of the number of Control-Related states for PNs. According to variant k-th order systems, TRM is constructed by proving that both Gen-Right(k, gen) and Gen_Left(k, k−gen) are the topological reverse nets of Gen-Left(k, gen) such that there is a reversible one-to-one mapping (Mirroring) between these two systems. Thus Gen-Right(k, gen) and Gen-Left(k, k−gen) have same CFF which can be immediately derived by replacing the parameter 'gen' by 'k−gen' in validated CFF for Gen-Left(k, gen). The shown applications of TRM include the reachability analysing for systems with non-sharing sub-net; Deficient tokens; multi-non-sharing/multi-tokens resources; and multi-processes. This paper reports the core methodology to accelerate furthering emerging system-control applications for more complicated PNs based on the real-time information derived by their CFF. [ABSTRACT FROM AUTHOR]

In this work, we propose a new algorithm for the verification of codiagnosability of a discrete event system with dynamic observations, which is an extension of a recently proposed algorithm for the verification of codiagnosability of discrete event systems with static observations. The proposed verifier, called E-VERIFIER, is compared with two different verifiers presented in the literature: (i) the T-VERIFIER, which is proposed for the verification of transition-based codiagnosability; and (ii) the C-VERIFIER, which employs cluster automata as the basic element for computation instead of the original states of the automaton system model. We show that although the E-VERIFIER has the same computational complexity as the C-VERIFIER and the T-VERIFIER, it has the following advantages over the other methods: (i) since the E-VERIFIER represents all faulty and fault-free traces of the system that have the same dynamic projection, then, unlike the C-VERIFIER, the traces that lead the system to violating the codiagnosability condition can be straightforwardly obtained from the E-VERIFIER, and the delay for decentralized diagnosis can be computed using only the E-VERIFIER; and (ii) the E-VERIFIER can be computed more efficiently than the T-VERIFIER when its number of states grows. As a consequence of the proposed approach, we present a method for the computation of the delay for decentralized diagnosis of discrete event systems with dynamic observations, adapting an algorithm based on the verifier for the case of static observations. [ABSTRACT FROM AUTHOR]

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. • Fault diagnosis model-based approach for discrete events systems modeled by Petri nets is proposed. • An algebraic observer for partially observed Petri nets is designed. • Algorithms capable to detect and identify faults for partially observed Petri nets are presented. • Application to a manufacturing systems. [ABSTRACT FROM AUTHOR]

The preservation of the efficient functionality of a pressurized reactor unit in the presence of faults is the aim of the present paper. To satisfy this aim, a distributed supervisory control scheme, considering the possibility of system faults, was designed. Towards this aim, the models of the subsystems of the total pressurized reactor unit in the presence of sensor and actuator faults are developed, using finite deterministic automata. This is the first contribution of the paper. The desired performance of the unit was formulated in the form of rules guaranteeing the desired behavior of a pressurize–depressurize cycle and safety specifications. The rules were translated to six desired regular languages. The realization of these languages, in the form of supervisor automata, was accomplished. This is the second contribution of the paper. A modular supervisory design scheme, towards safety and tolerance in the presence of faults, was proposed and realized, and the properties of the proposed supervisors and the controlled automaton were proven. This is the third contribution of the paper. The complexity of each supervisor was computed. The efficiency of the supervisory design scheme was illustrated through simulations. A PLC implementation of the derived supervisors was proposed. The derived supervisors are suitable for implementation as function blocks. [ABSTRACT FROM AUTHOR]

Mobile computing systems, service-based systems, and some other systems with mobile interacting components have recently received much attention. However, because of their characteristics, such as mobility and disconnection, it is difficult to model and analyze them by using a structure-fixed model. This work proposes a new Petri net model called variable Petri net (VPN) for modeling and analyzing these systems. The definition, firing rule, and related analysis technology of VPN are introduced in detail. In a VPN, the possible interaction interfaces are abstracted as a new kind of places called virtual places, and the occurrences of (dis)connections are described by new functions, which makes it appropriate to describe the component collaboration in systems and realize the scalability and pluggability of systems. Moreover, to overcome the shortcoming that markings cannot reflect the link capability of a system, VPNs add a constraint function along with a marking to represent a complete system configuration. Several examples are used to demonstrate the newly proposed model and method. [ABSTRACT FROM AUTHOR]

This paper concerns Remaining Useful Life (RUL) estimation of discrete event systems. For that purpose, physics-based models with partially observed stochastic Petri nets are used to represent the system and its sensors. The advantage of the proposed modelling approach is to provide a realistic representation of the system, including the interaction between the normal behaviours and the failure processes. From the proposed modelling and collected measurements, timed trajectories, which are consistent with the observations, are obtained. Based on the event dates, our approach consists in evaluating the probabilities of the consistent behaviours using probabilistic models. State estimation is obtained as a consequence. The most probable future degradations, from the current state, are then considered and a method for fault prognosis is presented. Finally, the prognosis result is used to estimate the RUL as a time interval. A case study is proposed to show the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

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., (© 2024. The Author(s).)

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.

Since the mid-1960s, it has been known that regular or deterministic flow lines (DFLs) with an arbitrary arrival process possess three essential behaviors: 1) a customer exit time recursion; 2) a server reordering principle; and 3) a delay equivalence with prototype queueing systems. Even though these three fundamental behaviors were discovered decades ago, analogous results for hybrid DFLs (HDFLs)—possessing multiple servers for each stage—have not been unearthed. In this article, we prove the existence of a recursion for customer exit times in HDFLs. This result leads naturally to a server reordering principle. Furthermore, we identify specific conditions under which the total delay faced by a customer in an HDFL is equivalent to that in a $G/D/c$ queue. Exploiting these results, we numerically determine the proportion of HDFL systems with a delay equivalent, study the computational burdens required for HDFL simulation, and assess the performance of approximations for HDFL systems. We hope these newly identified behaviors will lead to improved approximation methods and optimization models for use in modern automated manufacturing systems. Note to Practitioners—Many modern automated manufacturing systems, such as semiconductor wafer fabricators, use hundreds or thousands of highly complex, costly, and precise tools. These automated tools are often configured as a collection of processing modules served by material handling robots and clustered into a single chassis. One important example of such cluster tools is the clustered photolithography tool (CPT). For certain key performance metrics, including cycle time, residency time, and throughput time, such tools can be well-modeled as a tandem queueing system with random arrivals, regular or deterministic service times, multiple servers devoted to each stage, and finite internal buffers—a so-called HDFL. Such HDFLs also serve as a fundamental model of automated assembly systems. In this work, we prove that the behavior of HDFLs can be characterized by an exit recursion that requires significantly less computation than otherwise. Furthermore, HDFLs possess the surprising property that the customer queueing time is invariant to the order of the stages and, in some cases, is equivalent to a prototype queueing system. These results can be exploited to assess customer delay and to obtain simulation models with about an order of magnitude less computational complexity. We apply our results to a CPT model from the literature to highlight the properties of such tools. Finally, the theory introduced in this article enables a deeper understanding and intuition for such systems. [ABSTRACT FROM AUTHOR]

I‐detectability is an important issue in partially observed discrete event systems (DESs). In this paper, a Boolean semi‐tensor product (BSTP) approach is proposed to investigate the I‐detectability problem of partially observed DESs. First, two concepts of I‐detectability, that is, strong I‐detectability and weak I‐detectability, are recalled for partially observed DESs. Second, in order to estimate the initial state of DESs after finite observation events, an observer is constructed and converted to an algebraic form based on BSTP. Third, based on the algebraic form of observer, two necessary and sufficient conditions are presented for strong I‐detectability and weak I‐detectability of partially observed DESs, respectively. Finally, two examples are given to illustrate the obtained results. [ABSTRACT FROM AUTHOR]

To solve the least-cost planning sequence problem of a manufacturing system which is modeled by labeled Petri nets, an algorithm based on backtracking method is proposed. Given a labeled Petri net with its structure and an initial marking, the searching stages are divided into parts according to the given labeled sequence. In each stage, the transition with the minimal cost fires at first. With the observation of all the labels following this rule, the sum of the costs of transitions in the firing sequence is the minimum total cost. The least-cost planning sequence and the corresponding total cost are stored. The proposed method traverses the solution space tree according to the depth-first strategy. By taking the current minimum total cost as the constraint condition, the markings and the sequence of transitions that do not need to be searched can be eliminated in other paths. Therefore, the searching space is reduced. An illustrative example shows the feasibility of the method. Compared with the execution of dynamic programming method, the proposed method needs a smaller amount of calculation and achieves higher work efficiency.

Fault diagnosis is an important issue of partially observed discrete event systems (DESs). In this problem, fault detection and isolation are two associated tasks, where a fault is diagnosable if it can be detected certainly with a finite delay occurrence and a system is diagnosable if any type of faults can be distinguished with the observed information. Existing researches for fault diagnosis tend to regard faults as unobservable faulty events or states. In this paper, there exists a key conversion, denoting faults by accessible changes of state transition, and then obtains a normative and a faulty construction of given systems. Specially, we establish algebraic structures of composite automata with the help of semi-tensor product and propose a definition of diagnosability based on algebraic state space. Notice that the definition is so different from existing results that provide a new opportunity to fault diagnosis when existing criteria fail. Besides, we construct matrix observers to summarize the requisite information in the evolutionary process and present corresponding theorems to the verification of diagnosability. We apply the exhaust gas recirculation system and the heating system to illustrate the matrix approach in this paper is feasible.

A new technique is presented for diagnosing faults in a Discrete Event System (DES) when the state of the system under diagnosis (SUD) is uncertain upon commencement of the diagnosis process. Specifically, a diagnoser is developed that detects, identifies, and isolates faults in a DES relative to an initial belief state : a set of states in which the SUD could be located at the diagnoser's time of activation. The diagnoser does not need to be synchronously initialized with the SUD. Rather, it can be activated anytime before or after a fault in the SUD occurs, as it does not require knowledge of the SUD's behavior pre-activation. This form of analysis can be understood as fault diagnosis under uncertain initial conditions. The construction procedure for the diagnoser is given and the new concept of semi-asynchronous diagnosability is introduced. The latter requires all failures in the SUD to be diagnosable by observing the behaviors of the SUD after the diagnoser is activated. Moreover, necessary and sufficient conditions for semi-asynchronous diagnosability of a DES are provided. The semi-asynchronous diagnosis technique presented herein is compared with classical synchronous diagnosis approaches. Illustrative examples are provided in order to explain the introduced concepts and the developed approach. [ABSTRACT FROM AUTHOR]

Solutions to decentralized discrete-event systems problems are characterized by the way local decisions are fused to yield a global decision. A fusion rule is colloquially called an architecture. Current approaches do not provide a direct way to compare existing architectures. Determining whether an architecture is more permissive than another architecture had relied on producing examples ad hoc and on individual inspiration that puts the conditions for solvability in each architecture into some form that admits comparison. In response to these research efforts, a method based on morphisms between graphs has been extracted to yield a uniform approach to compare the permissiveness of the architectures. [ABSTRACT FROM AUTHOR]

To optimize the operation of his wind farm, the farm manager needs to make precise diagnostic decisions for scheduling efficiently the maintenance actions. To assist him in this task, this paper proposes an approach aimed at designing a hybrid diagnoser model for two main goals. The first one is to detect anomalies at an early stage, and the second one is to provide specific deductions or explanations about the potential cause of the problem. In this way, the developed system can help the maintenance manager understand why a particular decision needs to be made. To do this, an original approach coupling the artificial intelligence technique with a discrete event system is proposed to diagnose, identify, and explain a probable source of a problem. Thus, the proposed method uses a hybrid approach consisting of two model blocks. The first block consists of autoencoder models to extract feature representation to diagnose the health state of the system. The second one is a discrete event-based model to create and visualize rule-based anomaly alerts and triggers to provide plausible explanations to the operator to improve his task. Then, this work introduces a methodology to jointly train these two models and learn all parameters of the hybrid diagnoser. It is shown how the information extracted from the neural network model is used to automatically construct the event-based model to explain the occurrence of an anomaly. The proposed system achieved significant results in explaining and detecting early five types of anomalies in wind turbine systems with accuracy up to 80%. The results demonstrate that the approach can deliver performance gains of up to 20% compared to standard techniques such as Long short-term memory and Random cut forest. • Anomalies detection and explanation in wind turbine systems. • Hybrid learning of an autoencoder and event-based model. • Computation of a set of rules for identifying the root causes of anomalies. • Explainable predictive maintenance for improving decision-making processes. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of optimal supervisory control for cyclic tasks in the context of discrete-event systems (DES). We consider the completion of each single task as the visit of a marked state, and overall control objective is to complete tasks cyclically in the sense that marked states are visited infinitely often. Following the standard optimal supervisory control framework, two types of costs, disable cost and occurrence cost, are considered. However, instead of considering the standard accumulated total cost or the average cost per event, we consider the measure for the control performance using the average cost per task. We show that such an optimality measure is more suitable for tasks that need to be completed cyclically. Our goal is to design a live and non-blocking supervisor such that the average cost per task in the worst-case is minimized. To solve the problem, we propose a game-theoretical approach by converting the optimal control problem as a two-player graph game. Structural properties of the converted game are discussed. In particular, we show that this game can be solved by a set of mean payoff decision problems, for which effective algorithms exist. Our problem can be considered as a special instance of the general ratio-game in the literature. However, by exploring new structural property for this problem, we achieve superior computational efficiency when compared to the conventional solution designed for more general problem formulations. Illustrative examples are provided to demonstrate the proposed algorithm. [ABSTRACT FROM AUTHOR]

The feedback control system with network-connected components is vulnerable to cyberattacks. We study a problem of attack detection in supervisory control of discrete-event systems. The scenario of a system subjected to actuator enablement attacks is considered in this article. We also consider that some unsafe places that should be protected from an attacker exist in the system, and some controllable events that are disabled by a supervisor might be re-enabled by an attacker. This article proposes a defense strategy to detect actuator enablement attacks and disable all controllable events after detecting an attack. We design algorithmic procedures to determine whether the system can be protected against damage caused by actuator enablement attacks, where the damage is predefined as a set of “unsafe” places. In this way, the system property is called “AE-safe controllability”. The safe controllability can be verified by using a basis diagnoser or a basis verifier. Finally, we explain the approach with a cargo system example.