39,438 research outputs found
Robust Fault Diagnosis by Optimal Input Design for Self-sensing Systems
This paper presents a methodology for model based robust fault diagnosis and
a methodology for input design to obtain optimal diagnosis of faults. The
proposed algorithm is suitable for real time implementation. Issues of
robustness are addressed for the input design and fault diagnosis
methodologies. The proposed technique allows robust fault diagnosis under
suitable conditions on the system uncertainty. The designed input and fault
diagnosis techniques are illustrated by numerical simulation.Comment: Accepted in IFAC World Congress 201
Domain Adaptive Transfer Learning for Fault Diagnosis
Thanks to digitization of industrial assets in fleets, the ambitious goal of
transferring fault diagnosis models fromone machine to the other has raised
great interest. Solving these domain adaptive transfer learning tasks has the
potential to save large efforts on manually labeling data and modifying models
for new machines in the same fleet. Although data-driven methods have shown
great potential in fault diagnosis applications, their ability to generalize on
new machines and new working conditions are limited because of their tendency
to overfit to the training set in reality. One promising solution to this
problem is to use domain adaptation techniques. It aims to improve model
performance on the target new machine. Inspired by its successful
implementation in computer vision, we introduced Domain-Adversarial Neural
Networks (DANN) to our context, along with two other popular methods existing
in previous fault diagnosis research. We then carefully justify the
applicability of these methods in realistic fault diagnosis settings, and offer
a unified experimental protocol for a fair comparison between domain adaptation
methods for fault diagnosis problems.Comment: Presented at 2019 Prognostics and System Health Management Conference
(PHM 2019) in Paris, Franc
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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
Expert systems for real-time monitoring and fault diagnosis
Methods for building real-time onboard expert systems were investigated, and the use of expert systems technology was demonstrated in improving the performance of current real-time onboard monitoring and fault diagnosis applications. The potential applications of the proposed research include an expert system environment allowing the integration of expert systems into conventional time-critical application solutions, a grammar for describing the discrete event behavior of monitoring and fault diagnosis systems, and their applications to new real-time hardware fault diagnosis and monitoring systems for aircraft
Similarity Matching Techniques For Fault Diagnosis In Automotive Infotainment Electronics
Fault diagnosis has become a very important area of research during the last decade due to the advancement of mechanical and electrical systems in industries. The automobile is a crucial field where fault diagnosis is given a special attention. Due to the increasing complexity and newly added features in vehicles, a comprehensive study has to be performed in order to achieve an appropriate diagnosis model. A diagnosis system is capable of identifying the faults of a system by investigating the observable effects (or symptoms). The system categorizes the fault into a diagnosis class and identifies a probable cause based on the supplied fault symptoms. Fault categorization and identification are done using similarity matching techniques. The development of diagnosis classes is done by making use of previous experience, knowledge or information within an application area. The necessary information used may come from several sources of knowledge, such as from system analysis. In this paper similarity matching techniques for fault diagnosis in automotive infotainment applications are discussed
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
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