Gas Turbine Engine and Sensor Fault Diagnosis Using Optimization Techniques

Zedda, Marco; Singh, Riti

doi:10.2514/2.6050

Cited by 75 publications

(51 citation statements)

References 7 publications

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“…From the linear GPA method developed by Urban in the late 1960's [13], a series of GPA methods were proposed, such as an adaptive nonlinear GPA method [7], artificial neural networks [3], rule-based expert system and rule-based fuzzy expert system [11], and genetic algorithm [4,12,17]. The merit of artificial intelligence methods, such as neural network, rough set [14], Bayesian network [9,10] and rule based expert system, is that they do not need a gas turbine performance model, as only the relation information between fault symptom and degradation is needed.…”

Section: Gpamentioning

confidence: 99%

Gas Turbine Gas Path Fault Diagnosis based on Adaptive Nonlinear Steady-State Thermodynamic Model

Li¹

2018

IJPE

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Gas turbine engines always run during poor working conditions that have high temperatures, high pressures, and high mechanical and thermal stress. Thus, the performance of the gas path components gradually degrades, leading to serious faults. So, the health status of the engine gas path components provides essential information for users and operators. Here, a new gas path analysis approach has been developed to predict gas turbine engine health status by using gas path measurements. The developed approach has been tested in seven test cases where the degradation of a model gas turbine engine similar to a three-shaft marine engine has been analyzed. The case studies have shown that the approach can accurately and quickly detect, isolate, and quantify the degradation of major engine gas path components with the existence of measurement noise. The test cases have also shown that the time cost by the approach is short enough for its potential application of online health monitoring.

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Section: Gpamentioning

confidence: 99%

Gas Turbine Gas Path Fault Diagnosis based on Adaptive Nonlinear Steady-State Thermodynamic Model

Li¹

2018

IJPE

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“…Moreover, unlike the diagnosis method based on ICM inversion, both [12,[14][15][16] have claimed that the results of these methods may be affected by the smearing effects [17,18] (attributing a fault to a clean component). The ability to deal with the measurement uncertainties and high computational speed are the main advantages of the ANNbased FDI systems which attracted a lot of attention in the last decades.…”

Section: Introductionmentioning

confidence: 99%

“…Among the presented performance-based gas turbine diagnosis methods, the techniques based on Influence Coefficient Matrix (ICM) inversion [2], Weighted Least Square (WLS) [3][4][5], Kalman-Filter (KF) [6], Bayesian-belief network [7], the Artificial Neural Network (ANN) [8][9][10] and also the methods based on global optimization [11][12][13] can be mentioned as the most common performance-based FDI techniques.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy-based gas turbine fault detection and identification system for full and part-load performance deterioration

Mohammadi

Montazeri-Gh

2015

Aerospace Science and Technology

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“…Here the pressure measuring parameter is related to the non-dimensional flow parameter, and the combination of pressure and temperature measuring parameters is related to the efficiency. The greater number of measuring parameters and the more precise diagnostic results are expected, but the measuring sensor error increase and the measuring cost increase [61][62][63][64]. Figure 15 (a) shows the diagnostic analysis results of single fault cases without sensor noise and biases.…”

Section: Application Example Using Gamentioning

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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Gas Turbine Engine and Sensor Fault Diagnosis Using Optimization Techniques

Cited by 75 publications

References 7 publications

Gas Turbine Gas Path Fault Diagnosis based on Adaptive Nonlinear Steady-State Thermodynamic Model

Gas Turbine Gas Path Fault Diagnosis based on Adaptive Nonlinear Steady-State Thermodynamic Model

A fuzzy-based gas turbine fault detection and identification system for full and part-load performance deterioration

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

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