A distributed fault-detection and diagnosis system using on-line parameter estimation
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A distributed fault-detection and diagnosis system using on-line parameter estimation

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Published by National Aeronautics and Space Administration, National Technical Information Service, distributor in [Washington, D.C, Springfield, Va .
Written in English


  • Computerized simulation.,
  • Control systems design.,
  • Distributed parameter systems.,
  • Failure analysis.,
  • Fault detection.,
  • Mathematical models.,
  • Parameter identification.,
  • System failures.

Book details:

Edition Notes

Other titlesDistributed fault detection and diagnosis system using on-line parameter estimation.
StatementT.H. Guo and W. Merrill and A. Duyar.
SeriesNASA technical memorandum -- 104433.
ContributionsMerrill, Walter C., Duyar, Ahmet., United States. National Aeronautics and Space Administration.
The Physical Object
ID Numbers
Open LibraryOL15407300M

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The strategy for detecting the fault and estimating the fault parameters using a distributed on-line parameter identification scheme is presented. A two-step approach is proposed. The first step is composed of a group of hypothesis testing modules, (HTM) in parallel processing to test each class of : W. Merrill, A. Duyar and T.-H. Guo. could be applied to fault detection and diagnosis in an on-line estimation of a dynamic model for distributed parameter systems, with exemplification in the case of the city road traffic. Keywords: Fault detection and diagnosis, distributed parameter systems, system identification, wireless sensor networks, Bayesian networks, city road traffic. By contrast, this paper introduces a novel fault detection and estimation scheme by using a novel observer, which is designed directly based on PDE representation of DPS. Initially, a Luenberger-type observer was designed using healthy DPS dynamics to estimate system state and by: Distributed fault detection and estimation in cyber–physical systems subject to actuator faults Zhang buted fault diagnosis in a class of interconnected nonlinear uncertain Sarangapani -based fault detection, estimation, and prediction for a class of linear distributed parameter systems. Automatica, 66 (), pp. Author: Dezhi Xu, Fanglai Zhu, Zepeng Zhou, Xinggang Yan.

that faults affect the physical parameters in additive form. The faulty model given by equation (1) is used. to transform the problem of nonlinear fault diagnosis in an on-line nonlinear parameter estimation. problem, for which unknown fault parameters are estimated using system inputs and measurements. Parameter fault detection and estimation of a class of nonlinear systems using observers Article (PDF Available) in Journal of the Franklin Institute (7) . The main idea is to take into account the essentially distributed nature of the problem. This is done by the use of Petri nets and their causality semantics, that are well known as a powerful model for concurrent systems. We baseourapproachonanexplicitdescriptionoffaultpropagations,usingcapacity-onePetri nets. and fault detection [3]. Many methods of on-line parameter estimation have been proposed [4]. However, most of them are only applicable to the plants with constant parameters. When the plant is time-varying, most parameter estimation algorithms, e.g. .

Fault Diagnosis of Distributed Parameter Systems Modeled by Linear Parabolic Partial Differential Equations With State Faults Hasan Ferdowsi. Hasan Ferdowsi. Electrical Engineering Department, Model-Based Fault Detection, Estimation, and Prediction for a Class of Linear Distributed Parameter Systems Cited by: 3. The results of the hypothesis modules are processed by the fault-detection and estimation module. Using the results of the on-line diagnosis, the intelligent control system will be . This paper presents a new fault detection and diagnosis approach for nonlinear dynamic plant systems with a neuro-fuzzy based approach to prevent developing of fault . Supervision, Fault-Detection and Fault-Diagnosis Methods By these kinds of fuzzy sets and corresponding mem- bership functions, all the analytic and heuristic symp- toms can be represented in a unified way within the range 0 Cited by: