Fault Detection for a Class of Impulsive Switched Systems

Su, Qingyu; Li, Yan-Cheng; Dai, Xingze; Li, Jian

doi:10.1007/s11633-014-0784-7

Cited by 6 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Whilst typically, fault diagnosis algorithms are applied after the initial detection of an emerging fault, thereby forming a two-stage FDD procedure [1,2] , here, the proposed FDD approaches both detect and identify emerging faults in a single stage.…”

Section: International Journal Of Automation and Computing 00(0) Monmentioning

confidence: 99%

“…The adoption of 'early warning' systems to identify and localize emerging faults has therefore attracted considerable attention due to the widely-recognized benefits of facilitating reduced down-time and assurance of safety, through the use of fault detection and diagnosis (FDD) [2,3] algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Considering signal processing-based FDD Yu Zhang 1 Chris Bingham* 1 Mike Garlick 2 Michael Gallimore 1 1 School of Engineering, University of Lincoln, Lincoln, LN6 7TS, U.K. 2 Siemens Industrial Turbomachinery, Lincoln, LN6 3AD, U.K.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Applied fault detection and diagnosis for industrial gas turbine systems

Zhang

Bingham

Garlick

et al. 2016

Int. J. Autom. Comput.

View full text Add to dashboard Cite

Abstract:The paper presents readily implementable approaches for fault detection and diagnosis (FDD) based on measurements from multiple sensor groups, for industrial systems. Specifically, the use of hierarchical clustering (HC) and self-organizing map neural networks (SOMNNs) are shown to provide robust and user-friendly tools for application to industrial gas turbine (IGT) systems. HC fingerprints are found for normal operation, and FDD is achieved by monitoring cluster changes occurring in the resulting dendrograms. Similarly, fingerprints of operational behaviour are also obtained using SOMNN based classification maps (CMs) that are initially determined during normal operation, and FDD is performed by detecting changes in their CMs. The proposed methods are shown to be capable of FDD from a large group of sensors that measure a variety of physical quantities. A key feature of the paper is the development of techniques to accommodate transient system operation, which can often lead to false-alarms being triggered when using traditional techniques if the monitoring algorithms are not first desensitized. Case studies showing the efficacy of the techniques for detecting sensor faults, bearing tilt pad wear and early stage pre-chamber burnout, are included. The presented techniques are now being applied operationally and monitoring IGTs in various regions of the world.Keywords: Fault detection and diagnosis, hierarchical clustering, self-organizing map neural network. Introduction 1The purpose of fault detection is to automatically generate an 'alarm' or 'flag' to inform operators of impending or developing failure, whilst fault diagnosis aims to identify the location and predict the consequences of the failure [1] . The adoption of 'early warning' systems to identify and localize emerging faults has therefore attracted considerable attention due to the widely-recognized benefits of facilitating reduced down-time and assurance of safety, through the use of fault detection and diagnosis (FDD) [2,3] algorithms.Of the methods previously explored to date, FDD techniques can be broadly divided into three categories viz. knowledge-, model-and signal processing-based approaches [3,4,5] . Knowledge-based approaches often rely on monitoring residuals Manuscript received date; revised date * Corresponding author:Tel: +44 1522 837912; Email:cbingham@lincoln.ac.uk between multiple sensor measurements [6] , however, due to the high number of sensors used on modern industrial gas turbines (IGTs) and other complex industrial systems, the adoption of additional redundant sensors is prohibitively expensive. When using model-based approaches, a virtual sensor (a 'model' by some description) is employed to provide an estimate of expected measurements, from which residuals are then used as an indicator of potential failure modes being present [3] . However, for large IGT systems, which are often custom-designed to meet individual orders, the use of application specific materials and components (for example, to satisfy off-s...

show abstract

Section: International Journal Of Automation and Computing 00(0) Monmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Applied fault detection and diagnosis for industrial gas turbine systems

Zhang

Bingham

Garlick

et al. 2016

Int. J. Autom. Comput.

View full text Add to dashboard Cite

show abstract

“…The observer-based approach is one of the most attractive of the residual generation strategies. Su et al (2014), Valibeygi et al (2016) and Zhou and Zeng (2015) give some approaches to solve the fault detection and isolation problem for both linear and nonlinear systems according to uncertainty in parameters. The proposed fault diagnosis schemes use a parametric observer and a fault isolation and identification filter.…”

Section: Introductionmentioning

confidence: 99%

A new robust observer design for nonlinear systems with application to fault diagnosis

Zemzemi

Kamel

Toumi

et al. 2017

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

This paper presents a robust fault diagnosis scheme for a class of uncertain nonlinear systems whose nonlinear function satisfies the Lipschitz condition with unmatched time-varying uncertainties, external disturbances and perturbed output. The design procedure combines the high robustness of the nonlinear unknown input observer with sliding-mode techniques in order to enhance the estimation qualities. The proposed design is derived and expressed as a linear matrix inequality optimization problem. Additionally, we have provided an approach to reduce conservatism in the derivation of the stability conditions. The effectiveness of this observer and the fault diagnosis scheme are shown by applying them to a single-link manipulator. Simulation results are presented to validate the proposed approach and show the robustness for the system nonlinearity and unmatched time-varying uncertainties.

show abstract

Study of model-free adaptive data-driven SMC algorithm

Tang

2016

Int. J. Autom. Comput.

View full text Add to dashboard Cite

Fault Detection for a Class of Impulsive Switched Systems

Cited by 6 publications

References 27 publications

Applied fault detection and diagnosis for industrial gas turbine systems

Applied fault detection and diagnosis for industrial gas turbine systems

A new robust observer design for nonlinear systems with application to fault diagnosis

Study of model-free adaptive data-driven SMC algorithm

Contact Info

Product

Resources

About