Setting Up of a Probabilistic Neural Network for Sensor Fault Detection Including Operation With Component Faults

Romesis, C.; Mathioudakis, K.

doi:10.1115/1.1582493

Cited by 41 publications

(18 citation statements)

References 16 publications

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“…The works of Loboda and Yepifanov, (2010);Donald, et al, (2008);Loboda, (2008);Fast, et al, (2009);; Romesis and Mathioudakis, (2003); Roemer and Kacprzynski, (2000); Volponi, et al, (2003); Kamboukous and Mathioudakis, (2005) on gas path analysis in condition monitoring, were also critically considered to actualize this task. More so, trend monitoring as utilized in Bently, et al, (2002) and Uhumnwangho, et al, (2003) was also seen as a viable tool for this package.…”

Section: Brief Condition Monitoring Methodsmentioning

confidence: 99%

Adaptive Gas Path Modeling in Gas Turbine Health Monitoring

Ogbonnaya¹,

Johnson²,

Ugwu³

et al. 2012

Thermal Power Plants

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Section: Brief Condition Monitoring Methodsmentioning

confidence: 99%

Adaptive Gas Path Modeling in Gas Turbine Health Monitoring

Ogbonnaya¹,

Johnson²,

Ugwu³

et al. 2012

Thermal Power Plants

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“…Volponi [8] compares the Kalman filter and ANN (Artificial Neural Network) approaches for fault isolations in a gas turbine. Romesis and Mathioudakis [1] used probabilistic neural networks (PNN) to detect sensor faults on a gas turbine engine. Martucci [9] has suggested fuzzy fuel flow selection logic for gas turbine control.…”

Section: Introductionmentioning

confidence: 99%

Soft Computation of Turbine Inlet Temperature of Gas Turbine Power Plant Using Type-2 Fuzzy Logic Systems

Gupta

Pareek

Kar

2007

2007 IEEE International Fuzzy Systems Conference

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This paper aims to demonstrate application of Type-2 Fuzzy Logic Systems (FLS) to predict a critical parameter of Gas Turbine in a power plant viz., the Turbine Inlet Temperature (TIT). Maintaining higher TIT than allowed severely affects the life of the components whereas operating at lower TIT may cause low efficiency and low load. Nonavailability of TIT, which cannot be measured directly, puts great limitations on efficient gas turbine operation. Accurate estimation of this parameter requires significant computing power and the time required places limitations on the conventional modeling methods for use in real time applications. It is also demonstrated here by way of comparison, that a type-2 FLS is more robust in the presence of noise uncertainties than a type-1 conventional FLS for this application. Results are verified through the practical plant data obtained from an 88 MW gas turbine power plant.

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“…10 for instance, is the data contains significant transients due to changes in load and power demand. Importantly, the HC 'fingerprint' is shown to be robust to such effects without creating undue false-alarms which is an unfortunate characteristic of other methods that often misclassify unless presented with steady-state data [22,23] . Conversely, even when masked by the effects of transient demand/load changes (Figs.…”

Section: Case 3: Detection Of Bearing Faultmentioning

confidence: 99%

“…This often occurs as a consequence of algorithms being 'tuned' during steady operational conditions, for instance [22,23] , and are typically addressed by subsequently desensitizing the monitoring algorithms, which then leads to the non-detection of genuine fault conditions.…”

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

confidence: 99%

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Applied fault detection and diagnosis for industrial gas turbine systems

Zhang

Bingham

Garlick

et al. 2016

Int. J. Autom. Comput.

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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...

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Setting Up of a Probabilistic Neural Network for Sensor Fault Detection Including Operation With Component Faults

Cited by 41 publications

References 16 publications

Adaptive Gas Path Modeling in Gas Turbine Health Monitoring

Adaptive Gas Path Modeling in Gas Turbine Health Monitoring

Soft Computation of Turbine Inlet Temperature of Gas Turbine Power Plant Using Type-2 Fuzzy Logic Systems

Applied fault detection and diagnosis for industrial gas turbine systems

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