Distinguishing experiments for timed nondeterministic finite state machine

The problem of constructing distinguishing experiments is a fundamental problem in the area of finite state machines (FSMs), especially for FSM-based testing. In this paper, the problem is studied for timed nondeterministic FSMs (TFSMs) with output delays. Given two TFSMs, we derive the TFSM intersection of these machines and show that the machines can be distinguished using an appropriate (untimed) FSM abstraction of the TFSM intersection. The FSM abstraction is derived by constructing appropriate partitions for the input and output time domains of the TFSM intersection. Using the obtained abstraction, a traditional FSM-based preset algorithm can be used for deriving a separating sequence for the given TFSMs if these machines are separable. Moreover, as sometimes two non-separable TFSMs can still be distinguished by an adaptive experiment, based on the FSM abstraction we present an algorithm for deriving an r-distinguishing TFSM that represents a corresponding adaptive experiment

Multi-resolution fault diagnosis in discrete-event systems

In this thesis, a framework for multi-resolution fault diagnosis in discrete-event systems (DES) is introduced. Here a sequence of plant models, with increasing resolution, are used in fault diagnosis and the range of possible diagnosis is narrowed down step by step, until the failure node is isolated. In this way, the original problem of fault diagnosis is replaced by a sequence of smaller problems. The plant models used at each step of diagnosis are abstractions of the original plant model. We propose to use model reduction through the solutions of the Relational Coarsest Partition problem to obtain these abstractions. For each diagnosis step, minimal sensor sets are chosen to have a coarser output map, and hence, to improve the efficiency of model reduction. In this thesis, a polynomial algorithm is proposed that verifies failure diagnosability by examining the distinguishability of two plant (normal/faulty) conditions at a time. A procedure is presented that finds minimal sensor sets, referred to as minimal distinguishes for distinguishability of one condition from another. A polynomial procedure is introduced that combines minimal distinguishers to obtain a minimal sensor set for fault diagnosis. The proposed method reduces the computational complexity of sensor selection. A benefit of using minimal distinguishers is that their computation maybe speeded up using expert knowledge. The proposed method for sensor selection is particularly suitable for multi-resolution diagnosis since it permits some of the results of computations, performed for sensor selection at the lowest (finest) level of multi-resolution diagnosis to be reduced at higher levels. This feature is particularly useful in reducing the computations necessary for online reconfiguration of the multi-resolution diagnosis system. An important procedure used in sensor selection is testing diagnosability. In this thesis, a new procedure for testing diagnosability in timed DES is introduced based on the relatively timing of plant output sequence. It is shown through example that the proposed test maybe executed with significantly fewer computations compared to tests developed for untimed models and adapted for timed systems. Furthermore, two new sets of sufficient conditions are provided under which diagnoser design and diagnosability tests based on relative timing of output sequence can be performed efficientl

Polynomial Time Decidability of Weighted Synchronization under Partial Observability

We consider weighted automata with both positive and negative integer weights on edges and study the problem of synchronization using adaptive strategies that may only observe whether the current weight-level is negative or nonnegative. We show that the synchronization problem is decidable in polynomial time for deterministic weighted automata

Linear Distances between Markov Chains

We introduce a general class of distances (metrics) between Markov chains, which are based on linear behaviour. This class encompasses distances given topologically (such as the total variation distance or trace distance) as well as by temporal logics or automata. We investigate which of the distances can be approximated by observing the systems, i.e. by black-box testing or simulation, and we provide both negative and positive results

Property Enforcement for Partially-Observed Discrete-Event Systems

Engineering systems that involve physical elements, such as automobiles, aircraft, or electric power pants, that are controlled by a computational infrastructure that consists of several computers that communicate through a communication network, are called Cyber-Physical Systems. Ever-increasing demands for safety, security, performance, and certi cation of these critical systems put stringent constraints on their design and necessitate the use of formal model-based approaches to synthesize provably-correct feedback controllers. This dissertation aims to tackle these challenges by developing a novel methodology for synthesis of control and sensing strategies for Discrete Event Systems (DES), an important class of cyber-physical systems. First, we develop a uniform approach for synthesizing property enforcing supervisors for a wide class of properties called information-state-based (IS-based) properties. We then consider the enforcement of non-blockingness in addition to IS-based properties. We develop a nite structure called the All Enforcement Structure (AES) that embeds all valid supervisors. Furthermore, we propose novel and general approaches to solve the sensor activation problem for partially-observed DES. We extend our results for the sensor activation problem from the centralized case to the decentralized case. The methodology in the dissertation has the following novel features: (i) it explicitly considers and handles imperfect state information, due to sensor noise, and limited controllability, due to unexpected environmental disturbances; (ii) it is a uniform information-state-based approach that can be applied to a variety of user-speci ed requirements; (iii) it is a formal model-based approach, which results in provably correct solutions; and (iv) the methodology and associated theoretical foundations developed are generic and applicable to many types of networked cyber-physical systems with safety-critical requirements, in particular networked systems such as aircraft electric power systems and intelligent transportation systems.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137097/1/xiangyin_1.pd

Computer Aided Verification

This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book