14,265 research outputs found
A Note on Fault Diagnosis Algorithms
In this paper we review algorithms for checking diagnosability of
discrete-event systems and timed automata. We point out that the diagnosability
problems in both cases reduce to the emptiness problem for (timed) B\"uchi
automata. Moreover, it is known that, checking whether a discrete-event system
is diagnosable, can also be reduced to checking bounded diagnosability. We
establish a similar result for timed automata. We also provide a synthesis of
the complexity results for the different fault diagnosis problems.Comment: Note: This paper is an extended version of the paper published in the
proceedings of CDC'09, 48th IEEE Conference on Decision and Control and 28th
Chinese Control Conference, Shanghai, P.R. China, December 2009
The Complexity of Codiagnosability for Discrete Event and Timed Systems
In this paper we study the fault codiagnosis problem for discrete event
systems given by finite automata (FA) and timed systems given by timed automata
(TA). We provide a uniform characterization of codiagnosability for FA and TA
which extends the necessary and sufficient condition that characterizes
diagnosability. We also settle the complexity of the codiagnosability problems
both for FA and TA and show that codiagnosability is PSPACE-complete in both
cases. For FA this improves on the previously known bound (EXPTIME) and for TA
it is a new result. Finally we address the codiagnosis problem for TA under
bounded resources and show it is 2EXPTIME-complete.Comment: 24 pages
Causality and Temporal Dependencies in the Design of Fault Management Systems
Reasoning about causes and effects naturally arises in the engineering of
safety-critical systems. A classical example is Fault Tree Analysis, a
deductive technique used for system safety assessment, whereby an undesired
state is reduced to the set of its immediate causes. The design of fault
management systems also requires reasoning on causality relationships. In
particular, a fail-operational system needs to ensure timely detection and
identification of faults, i.e. recognize the occurrence of run-time faults
through their observable effects on the system. Even more complex scenarios
arise when multiple faults are involved and may interact in subtle ways.
In this work, we propose a formal approach to fault management for complex
systems. We first introduce the notions of fault tree and minimal cut sets. We
then present a formal framework for the specification and analysis of
diagnosability, and for the design of fault detection and identification (FDI)
components. Finally, we review recent advances in fault propagation analysis,
based on the Timed Failure Propagation Graphs (TFPG) formalism.Comment: In Proceedings CREST 2017, arXiv:1710.0277
The xSAP Safety Analysis Platform
This paper describes the xSAP safety analysis platform. xSAP provides several
model-based safety analysis features for finite- and infinite-state synchronous
transition systems. In particular, it supports library-based definition of
fault modes, an automatic model extension facility, generation of safety
analysis artifacts such as Dynamic Fault Trees (DFTs) and Failure Mode and
Effects Analysis (FMEA) tables. Moreover, it supports probabilistic evaluation
of Fault Trees, failure propagation analysis using Timed Failure Propagation
Graphs (TFPGs), and Common Cause Analysis (CCA). xSAP has been used in several
industrial projects as verification back-end, and is currently being evaluated
in a joint R&D Project involving FBK and The Boeing Company
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Learning multiple fault diagnosis
This paper describes two methods for integrating model-based diagnosis (MBD) and explanation-based learning. The first method (EBL) uses a generate-test-debug paradigm, generating diagnostic hypotheses using learned associational rules that summarize model-based diagnostic experiences. This strategy is a form of "learning while doing" model-based troubleshooting and could be called "online learning." The second diagnosis and learning method described here (EEL-STATIC) involves ''learning in advance." Learning begins in a training phase prior to performance or testing. Empirical results of computational experiments comparing the learning methods with MBD on two devices (the polybox and the binary full adder) are reported. For the same diagnostic performance, EBL-STATIC is several orders of magnitude faster than MBD while EBL can cause performance slow-down
A new approach for diagnosability analysis of Petri nets using Verifier Nets
In this paper, we analyze the diagnosability properties of labeled Petri nets. We consider the standard notion of diagnosability of languages, requiring that every occurrence of an unobservable fault event be eventually detected, as well as the stronger notion of diagnosability in K steps, where the detection must occur within a fixed bound of K event occurrences after the fault. We give necessary and sufficient conditions for these two notions of diagnosability for both bounded and unbounded Petri nets and then present an algorithmic technique for testing the conditions based on linear programming. Our approach is novel and based on the analysis of the reachability/coverability graph of a special Petri net, called Verifier Net, that is built from the Petri net model of the given system. In the case of systems that are diagnosable in K steps, we give a procedure to compute the bound K. To the best of our knowledge, this is the first time that necessary and sufficient conditions for diagnosability and diagnosability in K steps of labeled unbounded Petri nets are presented
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