Prospects for aero gas-turbine diagnostics: a review

Marinai, Luca; Probert, Douglas; Singh, Riti

doi:10.1016/j.apenergy.2003.10.005

Cited by 93 publications

(94 citation statements)

References 20 publications

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“…Since then several algorithms with some improvements have been contributed by others [21,22]. Marinai and Singh [5], have made a detailed review of NLGPA.…”

Section: Model Based Diagnostics Methodsmentioning

confidence: 99%

“…An expert system is a computer program that used to capture human expert knowledge in the form of facts and rules to solve a problem or giving an advice [5,8]. The architecture of expert systems consist of four basic elements, user interface (used to acquire information and display results), inference engine (deals with all the reasoning operations of the system based on known facts and rules), knowledge base (contains facts and rules about the problem to offer the appropriate decision) and developer (stores information about current education) [25].…”

Section: Expert Systems (Es)mentioning

confidence: 99%

“…# long computation time model complexity ES ) simple to develop and easy to understand ) preferable when the diagnostic problem is well understood, stable, and human experts are available to develop the knowledge base (rules) ) more suitable technique for stable and predictable engine operating conditions, if potential faults can be defined easily # model accuracy highly depends on the up to date knowledge of the human expert # significant number of comprehensive set of rules are required # precise inputs required # give approximate results are not effective for highly variable operating condition cases GPA ) more suitable for on-board application ) the algorithm have direct relationship with the gas turbine gas path physics ) fairly high computational speed ) capable to handle abrupt changes in measurements due to operational damage ) capable to multiple components fault isolation [7] # has a limitation on clear component fault isolation [5] # for better prediction large number of sensors are required # face a problem when N>M # can diagnose engine faults if and only if noise and bias doesn't exist # modeling difficulties due to the existence of limited information in the open domain # For large variations in component faults the programme has convergence problem [34] …”

Section: Flmentioning

confidence: 99%

“…Specifically, gas turbine diagnostics can be for the sensor system, control system, fuel and oil system, vibration system, and gas path system [4]. Among them gas path diagnostics is the most critical tool and it works on the basis of engine performance deterioration [5].…”

Section: Introductionmentioning

confidence: 99%

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Gas turbine gas path diagnostics: A review

2016

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Abstract. In this competitive business world one way to increase profitability of a power production unit is to reduce the operation and maintenance expenses. This is possible if the gas turbine availability and reliability is improved using the appropriate maintenance action at the right time. In that case, fault diagnostics is very critical and effective and advanced methods are essential. Gas turbine diagnostics has been studied for the past six decades and several methods are introduced. This paper aims to review and summarise the published literature on gas path diagnostics, giving more emphasis to the recent developments, and identify advantages and limitations of the methods so that beginners in diagnostics can easily be introduced. Towards this end, this paper, identifies various diagnostic methods and point out their pros and cons. Finally, the paper concludes the review along with some recommended future works.

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“…Since then several algorithms with some improvements have been contributed by others [21,22]. Marinai and Singh [5], have made a detailed review of NLGPA.…”

Section: Model Based Diagnostics Methodsmentioning

confidence: 99%

Section: Expert Systems (Es)mentioning

confidence: 99%

Section: Flmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gas turbine gas path diagnostics: A review

2016

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“…The advanced engine health condition monitoring method is classified into the model based method (such as observers, parity equations, parameter estimation and Gas Path Analysis (GPA)) and the soft computing method (such as expert system, fuzzy logic, Neural Networks (NNs) and Genetic Algorithms (GA)) [6][7].…”

Section: Introductionmentioning

confidence: 99%

Review on Advanced Health Monitoring Methods for Aero Gas Turbines using Model Based Methods and Artificial Intelligent Methods

Kong¹

2014

International Journal of Aeronautical and Space Sciences

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The aviation gas turbine is composed of many expensive and highly precise parts and operated in high pressure and temperature gas. When breakdown or performance deterioration occurs due to the hostile environment and component degradation, it severely influences the aircraft operation. Recently to minimize this problem the third generation of predictive maintenance known as condition based maintenance has been developed. This method not only monitors the engine condition and diagnoses the engine faults but also gives proper maintenance advice. Therefore it can maximize the availability and minimize the maintenance cost. The advanced gas turbine health monitoring method is classified into model based diagnosis (such as observers, parity equations, parameter estimation and Gas Path Analysis (GPA)) and soft computing diagnosis (such as expert system, fuzzy logic, Neural Networks (NNs) and Genetic Algorithms (GA)). The overview shows an introduction, advantages, and disadvantages of each advanced engine health monitoring method. In addition, some practical gas turbine health monitoring application examples using the GPA methods and the artificial intelligent methods including fuzzy logic, NNs and GA developed by the author are presented.

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A Fuzzy Logic Approach to Gas Path Diagnostics in Aero-engines

Marinai

Singh

Advanced Information and Knowledge Processing

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Engine-related costs contribute a large fraction of the direct operating costs (DOCs) of an aircraft, because the propulsion system requires a significant part of the overall maintenance effort. Thus, to ensure competitive advantage in the aeroengine market, health monitoring systems with gas path diagnostics capability are highly desirable.In this chapter, an application of fuzzy logic technology to gas path diagnostics for aero-engines performance analysis is presented and the setup procedure for a modern civil turbofan is described, as an example. The objective is to estimate the changes in engine component performance due to the engine degradation over time from the knowledge of only a few measurable parameters, inevitably affected by noise. This is a novel process that achieves effective diagnosis by means of a rule-based pattern-recognition methodology founded on fuzzy algebra, developed to provide an alternative technology versus conventional estimation algorithms.The inherent capability of fuzzy logic to deal with gas path diagnostics difficulties, thanks to the use of fuzzy set theory and its rule-based nature, is highlighted. First, the problem of noisy measurements is treated at a fuzzy-set level. Second, at the system level the definition of fuzzy rules is used to map input sets of measurements into output faulty classes of performance parameters in a constrained search space; this enables a problem reduction aimed at overcoming the fact that the analytical formulation is undetermined.The process quantifies the performance parameters' deteriorations through a nonlinear approach, even in the presence of noisy measurements that typically complicate the diagnostic assessment. The diagnostics model's setup as well as its outcome can be attained in a relatively short time, making this technique suitable for on-board use. The accuracy of the technique relative to simulated turbofan data is tested and its advantages and limitations are discussed. IntroductionThe performance of an aero-engine deteriorates over time as a consequence of its components' degradation. The identification of the exact component(s) responsible for the performance loss facilitates the choice of the recovery action to be undertaken. An engine gas-path diagnostic process calculates changes in the magnitude of the component performance parameters (e.g., efficiency and flow capacity) given a set of measurements (e.g., temperatures, pressures, shaft speed and fuel flow) through the engine. However, accurate assessment is complicated by

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Prospects for aero gas-turbine diagnostics: a review

Cited by 93 publications

References 20 publications

Gas turbine gas path diagnostics: A review

Gas turbine gas path diagnostics: A review

Review on Advanced Health Monitoring Methods for Aero Gas Turbines using Model Based Methods and Artificial Intelligent Methods

A Fuzzy Logic Approach to Gas Path Diagnostics in Aero-engines

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