SUPERVISORY CONTROL AND FAILURE DIAGNOSIS OF DISCRETE EVENT SYSTEMS: A TEMPORAL LOGIC APPROACH

Discrete event systems (DESs) are systems which involve quantities that take a discrete set of values, called states, and which evolve according to the occurrence of certain discrete qualitative changes, called events. Examples of DESs include many man-made systems such as computer and communication networks, robotics and manufacturing systems, computer programs, and automated trac systems. Supervisory control and failure diagnosis are two important problems in the study of DESs. This dissertation presents a temporal logic approach to the control and failure diagnosis of DESs. For the control of DESs, full branching time temporal logic-CTL* is used to express control specifications. Control problem of DES in the temporal logic setting is formulated; and the controllability of DES is defined. By encoding the system with a CTL formula, the control problem of CTL* is reduced to the decision problem of CTL*. It is further shown that the control problem of CTL* (resp., CTL{computation tree logic) is complete for deterministic double (resp., single) exponential time. A sound and complete supervisor synthesis algorithm for the control of CTL* is provided. Special cases of the control of computation tree logic (CTL) and linear-time temporal logic (LTL) are also studied; and for which algorithms of better complexity are provided. For the failure diagnosis of DESs, LTL is used to express fault specifications. Failure diagnosis problem of DES in the temporal logic setting is formulated; and the diagnosability of DES is defined. The problem of testing the diagnosability is reduced to that of model checking. An algorithm for the test of diagnosability and the synthesis of a diagnoser is obtained. The algorithm has a polynomial complexity in the number of system states and the number of fault specifications. For the diagnosis of repeated failures in DESs, different notions of repeated failure diagnosability, K-diagnosability, [1,K]-diagnosability, and [1,1]-diagnosability, are introduced. Polynomial algorithms for checking these various notions of repeated failure diagnosability are given, and a procedure of polynomial complexity for the on-line diagnosis of repeated failures is also presented

RULES BASED MODELING OF DISCRETE EVENT SYSTEMS WITH FAULTS AND THEIR DIAGNOSIS

Failure diagnosis in large and complex systems is a critical task. In the realm of discrete event systems, Sampath et al. proposed a language based failure diagnosis approach. They introduced the diagnosability for discrete event systems and gave a method for testing the diagnosability by first constructing a diagnoser for the system. The complexity of this method of testing diagnosability is exponential in the number of states of the system and doubly exponential in the number of failure types. In this thesis, we give an algorithm for testing diagnosability that does not construct a diagnoser for the system, and its complexity is of 4th order in the number of states of the system and linear in the number of the failure types. In this dissertation we also study diagnosis of discrete event systems (DESs) modeled in the rule-based modeling formalism introduced in [12] to model failure-prone systems. The results have been represented in [43]. An attractive feature of rule-based model is it\u27s compactness (size is polynomial in number of signals). A motivation for the work presented is to develop failure diagnosis techniques that are able to exploit this compactness. In this regard, we develop symbolic techniques for testing diagnosability and computing a diagnoser. Diagnosability test is shown to be an instance of 1st order temporal logic model-checking. An on-line algorithm for diagnosersynthesis is obtained by using predicates and predicate transformers. We demonstrate our approach by applying it to modeling and diagnosis of a part of the assembly-line. When the system is found to be not diagnosable, we use sensor refinement and sensor augmentation to make the system diagnosable. In this dissertation, a controller is also extracted from the maximally permissive supervisor for the purpose of implementing the control by selecting, when possible, only one controllable event from among the ones allowed by the supervisor for the assembly line in automaton models

Autonomous Recovery Of Reconfigurable Logic Devices Using Priority Escalation Of Slack

Field Programmable Gate Array (FPGA) devices offer a suitable platform for survivable hardware architectures in mission-critical systems. In this dissertation, active dynamic redundancy-based fault-handling techniques are proposed which exploit the dynamic partial reconfiguration capability of SRAM-based FPGAs. Self-adaptation is realized by employing reconfiguration in detection, diagnosis, and recovery phases. To extend these concepts to semiconductor aging and process variation in the deep submicron era, resilient adaptable processing systems are sought to maintain quality and throughput requirements despite the vulnerabilities of the underlying computational devices. A new approach to autonomous fault-handling which addresses these goals is developed using only a uniplex hardware arrangement. It operates by observing a health metric to achieve Fault Demotion using Recon- figurable Slack (FaDReS). Here an autonomous fault isolation scheme is employed which neither requires test vectors nor suspends the computational throughput, but instead observes the value of a health metric based on runtime input. The deterministic flow of the fault isolation scheme guarantees success in a bounded number of reconfigurations of the FPGA fabric. FaDReS is then extended to the Priority Using Resource Escalation (PURE) online redundancy scheme which considers fault-isolation latency and throughput trade-offs under a dynamic spare arrangement. While deep-submicron designs introduce new challenges, use of adaptive techniques are seen to provide several promising avenues for improving resilience. The scheme developed is demonstrated by hardware design of various signal processing circuits and their implementation on a Xilinx Virtex-4 FPGA device. These include a Discrete Cosine Transform (DCT) core, Motion Estimation (ME) engine, Finite Impulse Response (FIR) Filter, Support Vector Machine (SVM), and Advanced Encryption Standard (AES) blocks in addition to MCNC benchmark circuits. A iii significant reduction in power consumption is achieved ranging from 83% for low motion-activity scenes to 12.5% for high motion activity video scenes in a novel ME engine configuration. For a typical benchmark video sequence, PURE is shown to maintain a PSNR baseline near 32dB. The diagnosability, reconfiguration latency, and resource overhead of each approach is analyzed. Compared to previous alternatives, PURE maintains a PSNR within a difference of 4.02dB to 6.67dB from the fault-free baseline by escalating healthy resources to higher-priority signal processing functions. The results indicate the benefits of priority-aware resiliency over conventional redundancy approaches in terms of fault-recovery, power consumption, and resource-area requirements. Together, these provide a broad range of strategies to achieve autonomous recovery of reconfigurable logic devices under a variety of constraints, operating conditions, and optimization criteria

Theory and design of reliable spacecraft data systems

Theory and techniques applicable to design, analysis, and fault diagnosis of reliable spacecraft data system

Time Decomposition for Diagnosis of Discrete Event Systems

Artificial intelligence diagnosis is a research topic of knowledge representation and reasoning. This work addresses the problem of on-line model-based diagnosis of Discrete Event Systems (DES). A DES model represents state dynamics in a discrete manner. This work concentrates on the models whose scales are finite, and thus uses finite state machines as the DES representation. Given a flow of observable events generated by a DES model, diagnosis aims at deciding whether a system is running normally or is experiencing faulty behaviours. The main challenge is to deal with the complexity of a diagnosis problem, which has to monitor an observation flow on the fly, and generate a succession of the states that the system is possibly in, called belief state. Previous work in the literature has proposed exact diagnosis, which means that a diagnostic algorithm attempts to compute a belief state at any time that is consistent with the observation flow from the time when the system starts operating to the current time. The main drawback of such a conservative strategy is the inability to follow the observation flow for a large system because the size of each belief state has been proved to be exponential in the number of system states. Furthermore, the temporal complexity to handle the exact belief states remains a problem. Because diagnosis of DES is a hard problem, the use of faster diagnostic algorithms that do not perform an exact diagnosis is often inevitable. However, those algorithms may not be as precise as an exact model-based diagnostic algorithm to diagnose a diagnosable system. This Thesis has four contributions. First, Chapter 3 proposes the concept of simulation to verify the precision of an imprecise diagnostic algorithm w.r.t. a diagnosable DES model. A simulation is a finite state machine that represents how a diagnostic algorithm works for a particular DES model. Second, Chapter 4 proposes diagnosis using time decomposition, and studies window-based diagnostic algorithms, called Independent-Window Algorithms (IWAs). IWAs only diagnose on the very last events of the observation flow, and forget about the past. The precision of this approach is assessed by constructing a simulation. Third, Chapter 5 proposes a compromise between the two extreme strategies of exact diagnosis and IWAs. This work looks for the minimum piece of information to remember from the past so that a window-based algorithm ensures the same precision as using the exact diagnosis. Chapter 5 proposes Time-Window Algorithms (TWAs), which are extensions to IWAs. TWAs carry over some information about the current state of the system from one time window to the next. The precision is verified by constructing a simulation. Fourth, Chapter 6 evaluates IWAs and TWAs through experiments, and compares their performance with the exact diagnosis encoded by Binary Decision Diagrams (BDD). Chapter 6 also examines the impact of the time window selections on the performance of IWAs and TWAs