Fault diagnosis in internet of things systems, where multiple distributed components interact asynchronously through communication buffers, poses significant challenges due to system scalability and communication uncertainties. To address this, this paper studies the problem of diagnosability consistency in a discrete event system modeled using a labeled Petri net composed of several interconnected subnets via buffer places. Due to the state explosion problem, the diagnosability analysis by a centralized approach for large-scale systems is computationally demanding and sometimes even impossible. In this work, we assume that Petri net modules are connected through buffer places according to predefined rules and do not share transitions or resources, offering a complementary and computationally efficient alternative to existing modular approaches for large-scale systems. The diagnosability of subnets is analyzed with a particular automaton, called an unfolded verifier, by determining whether there exists a fundamental path that leads to the violation of the diagnosability. The proposed approach investigates the diagnosability of large systems with modular structures (namely global diagnosability), without constructing a global unfolded verifier, by analyzing the diagnosability of each module only (namely local diagnosability). More precisely, the consistency between the local diagnosability and the global diagnosability is addressed by determining whether all the fundamental paths of subnets survive in the global net due to the composition of subnets. Finally, an algorithm is given to deduce the diagnosability of a monolithic system. Compared with the existing centralized approaches, the complexity is practically mitigated by using the proposed one.

This study tackles the challenge of robust fault diagnosis in discrete event systems (DESs) that experience permanent observation losses using labeled Petri nets (LPNs). We consider the scenario that the initially observable transitions may become unobservable before their firings. Especially, the case that some, instead of all, of the transitions with a shared label may become unobservable is also taken into account. In such a scenario, the diagnosers in the existing methods may not report correct diagnostic results. This article presents a novel notion to ensure robust diagnosability for LPNs, aimed at overcoming the issue of permanent observation loss. To avert enumerating all the reachable markings, a structure called a tagged basis reachability graph (t-BRG) is developed, based on which all subsets of observable transitions, called diagnosis transition sets (DTSs), that ensure the diagnosability of the plant independently are calculated. Then, a special class of verifiers to assess the robust diagnosability of a system experiencing permanent observation loss is developed. Finally, an online diagnosis method performed by a set of diagnosers is presented and demonstrated by examples.

This paper addresses the problems of diagnosability verification and enforcement of discrete event systems modeled with unbounded Petri nets. Diagnosability in such systems is critical for ensuring reliability and maintaining operational integrity, yet current methods often struggle with the complexity introduced by unboundedness and potential deadlocks. Given an unbounded labeled Petri net that may reach deadlocks, a quiescent basis coverability graph is established to verify the diagnosability of the considered system. This procedure employs a deterministic finite state automaton, called an extended verifier, derived from the proposed quiescent basis coverability graph. It is shown that an unbounded Petri net is diagnosable if and only if the verifier does not contain a class of cycles, called repetitive F-cycles. This result also provides necessary and sufficient conditions for diagnosability enforcement by developing an online supervisor. Further, the designed supervisor is maximally permissive and also circumvents a plant entering deadlocks by firing non-fault sequences. Examples are presented to demonstrate the proposed method. Note to Practitioners—Fault diagnosis and diagnosability enforcement are critical for the development and operation of highly automated systems covering computer-integrated production processes, intelligent traffic, computer and communication networks, smart gird, etc. This work touches upon this problem from the perspective of discrete event systems that are modeled with unbounded labeled Petri nets. The feasibility and applicability of the reported method stem from the usage of a structurally compact representation of a considered plant such that the computational cost of a real-world system is acceptable. The graphical representation of Petri nets as well as the proposed quiescent basis coverability graph make the method easy to use and manipulate. Moreover the sufficient and necessary conditions of diagnosability enforcement can be readily verified by the supervisory theory, facilitating its adoption by practitioners.

The fault diagnosis problem in discrete event systems consists in detecting the occurrences of faults in a plant, which is essential for ensuring the reliability of the plant. In the literature, this problem has been widely studied by assuming that the communications between a plant and a diagnostic agent, i.e., a diagnoser, are reliable and instantaneous. However, for a networked system, the information generated by a plant is transmitted through a shared communication network such that communication delays and losses are inevitable. This article formulates and studies the fault diagnosis problem in networked discrete event systems (NDESs) modeled by labeled Petri nets, where communication delays and losses are considered. Such a problem is also calledrobust fault diagnosisin this article. An important notion closely related to robust diagnosis, namednetworked diagnosability, is introduced, indicating that any occurred fault in a NDES is definitely determined after a limited number of observations. For a NDES, a tool called anetworked basis diagnoseris excogitated to solve the robust diagnosis problem. A necessary and sufficient condition for verifying the networked diagnosability of a plant is derived by using the developed diagnoser. Finally, a manufacturing system is presented to illustrate the developed approach.

Monocular 3D lane detection has recently received increasing research attention in autonomous driving due to its application effectiveness and simplicity. However, depending solely on the limited semantic information from a single image makes current monocular detection methods unable to deal with complex scenarios, such as occluded, blurred, and unaligned scenes. In this study, we introduce an end-to-end framework named Bi2Lane which models temporal dependency in a continuous sequence. It recurrently utilizes detected lanes within historical frames as prior information to achieve robust lane detection. Additionally, Bi2Lane employs temporal reverse refinement together with temporal forward refinement to achieve bi-directional temporal refinement (BDTR) while maintaining a robust temporal dependency. For the refined features of different frames, we design a bi-level feature aggregation module (BLFA) to fuse them in both point-level and line-level manners, enabling a comprehensive feature representation to deal with complicated road scenes. Extensive experiments conducted on the OpenLane dataset demonstrate the superiority of Bi2Lane, achieving a notable F1 score of 63.8% using a simple ResNet50 backbone, surpassing the performance of existing state-of-the-art methods.

In this paper, we study the nonblocking supervisor synthesis problem in partially observed discrete event systems modeled by finite-state automata. We consider a particular type of supervisors that can observe not only the execution of observable events in a plant but also the quiescence of it. We first define a q-observer to characterize the behavior of a plant with observable quiescence. Comparing with the classical observer structure, the q-observer contains the quiescence information of a plant, which can be used to improve state estimation. Then we propose a method to detect the blocking states in a$q$-observer. Finally, we develop an iterative method to synthesize a nonblockingness enforcement supervisor from the$q$-observer. Since quiescence provides additional information on state estimation, the supervisor synthesized by the proposed method is in general more permissive than those synthesized by the existing approaches that do not monitor the quiescence. A manufacturing system example is also given to elucidate the effectiveness of the developed approachNote to Practitioners—A discrete event system is a discrete-state and event-driven system, covering a deluge of contemporary computer-integrated man-made constructs, such as automated manufacturing systems, smart urban transportation systems and computer communication networks. Such a system is in general partially observed due to the limited sensor deployment, which complicates its controller design. This research studies the typical supervisory control problem for a partially observed discrete event system. A supervisor is designed to restrict the dynamics of the system in order to guarantee a safe operation through a different control scheme by using the quiescence information that can be usually provided by a real-world system. The practitioners in control and automation community are capable of practicing the formulated control scheme for engineering applications.

This research focuses on the forbidden state problem in the framework of labeled Petri nets (LPNs), i.e., to design a supervisor for a plant modeled by an LPN such that the closed-loop system cannot reach a set of predefined forbidden markings and does not contain any deadlock. Different from the traditional control scheme, the supervisor derived by this work can not only observe the observable transitions, but also the quiescence information. First, a new structure named an extended basis reachability graph (EBRG) is introduced to describe the reachability space of an LPN without computing all reachable markings. Based on an EBRG, a basis observer is then excogitated to represent the behavior of an LPN. Some states in the basis observer are defined as bad states and control-induced deadlocks, which relates to the undesirable behavior of the plant. Finally, an algorithm is introduced to compute a supervisor based on the basis observer. The consideration of system quiescence provides extra information on the marking estimation of the closed-loop system such that certain disabled transitions are re-enabled. Consequently, the developed supervisor in this article is generally more permissive than those do not observe the quiescence.