Measurement Selections for Multi-Component Gas Path Diagnostics Using Analytical Approach and Measurement Subset Concept

Jasmani, Mohd Shahrizal; Li, Yi-Guang; Ariffin, Zaharudin

doi:10.1115/gt2010-22402

Cited by 4 publications

(4 citation statements)

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“…The secondary air taken from the gas generator compressors serve as cooling and sealing air. The gas turbine has six main gaspath components, including an intermediate-pressure compressor (IPC), a high-pressure compressor (HPC), a combustion chamber, a high-pressure turbine (HPT), an intermediatepressure turbine (IPT), and a power turbine (PT) [25]. Three-shaft industrial gas turbine configurations are shown in Figure 1.…”

Section: Case Enginementioning

confidence: 99%

Simultaneous Fault Diagnostics for Three-Shaft Industrial Gas Turbine

et al. 2023

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The study focused on the development of -gas turbine full- and part-load operation diagnostics. The gas turbine performance model was developed using commercial software and validated using the engine manufacturer data. Upon the validation, fouling, erosion, and variable inlet guide vane drift were simulated to generate faulty data for the diagnostics development. Because the data from the model was noise-free, sensor noise was added to each of the diagnostic set parameters to reflect the actual scenario of the field operation. The data was normalized. In total, 13 single, and 61 double, classes, including 1 clean class, were prepared and used as input. The number of observations for single faults diagnostics were 1092, which was 84 for each class, and 20,496 for double faults diagnostics, which was 336 for each class. Twenty-eight machine learning techniques were investigated to select the one which outperformed the others, and further investigations were conducted with it. The diagnostics results show that the neural network group exhibited better diagnostic accuracy at both full- and part-load operations. The test results and its comparison with literature results demonstrated that the proposed method has a satisfactory and reliable accuracy in diagnosing the considered fault scenarios. The results are discussed, following the plots.

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Section: Case Enginementioning

confidence: 99%

Simultaneous Fault Diagnostics for Three-Shaft Industrial Gas Turbine

et al. 2023

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“…The initial population is 20, the maximum generation G is 200, the terminal number G end for iteration is 30, the initial crossover probability P c0 is 0.9, and the initial mutation probability P m0 is 0.04. The value range of the regularization parameter is (2 À10 -2 10 ) and the initial value is 1. The value range of the kernel parameter is (2 À5 -2 5 ) and the initial value is 0.1.…”

Section: Monitoring Modulementioning

confidence: 99%

“…Currently, several approaches related to searching for optimal sensor suites have been proposed by different scholars. [10][11][12] Regardless of the method chosen for measurement, the main aim is to provide optimal sensors for gas turbine engine health monitoring and fault diagnosis. However, as critical components, sensors are also prone to fail by themselves, especially those along the gas path bearing high temperature, high pressure, and radiation in extreme conditions.…”

Section: Introductionmentioning

confidence: 99%

Sensor fault diagnosis of gas turbine engines using an integrated scheme based on improved least squares support vector regression

Zhu

Sun

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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As the flight envelope is widening continuously and operational capability is improving sequentially, gas turbine engines are faced with new challenges of increased operation and maintenance requirements for efficiency, reliability, and safety. The measures for security and safety and the need for reducing the life cycle cost make it necessary to develop more accurate and efficient monitoring and diagnostic schemes for the health management of gas turbine components. Sensors along the gas path are one of the components in gas turbines that play a crucial role in turbofan engines owing to their safety criticality. Failures in sensor measurements often result in serious problems affecting flight safety and performance. Therefore, this study aims to develop an online diagnosis system for gas path sensor faults in a turbofan engine. The fault diagnosis system is designed and implemented using a genetic algorithm optimized recursive reduced least squares support vector regression algorithm. This method uses a reduction technique and recursion strategy to obtain a better generalization performance and sparseness, and exploits an improved genetic algorithm to choose the optimal model parameters for improving the training precision. The effectiveness of the sensor fault diagnosis system is then validated through typical fault modes of single and dual sensors.

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“…The sensitivity norm which describes the overall sensitivity of each sensor can then be expressed using Eq. ( 4) below (Jasmani et al, 2010).…”

Section: Sensor Selectionmentioning

confidence: 99%

An Adaptive Model-Based Framework for Prognostics of Gas Path Faults in Aircraft Gas Turbine Engines

Alozie

et al. 2019

IJPHM

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This paper presents an adaptive framework for prognostics in civil aero gas turbine engines, which incorporates both performance and degradation models, to predict the remaining useful life of the engine components that fail predominantly by gradual deterioration over time. Sparse information about the engine configuration is used to adapt a performance model, which serves as a baseline for implementing optimum sensor selection, operating data correction, fault isolation, noise reduction and component health diagnostics using nonlinear Gas Path Analysis (GPA). Degradation models, which describe the progression of faults until failure, are then applied to the diagnosed component health indices from previous run-to-failure cases. These models constitute a training library from which fitness evaluation to the current test case is done. The final remaining useful life (RUL) prediction is obtained as a weighted sum of individually evaluated RULs for each training case. This approach is validated using dataset generated by the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) software, which comprises both training and testing instances of run-to-failure sensor data for a turbofan engine, some of which are obtained at different operating conditions and for multiple fault modes. The results demonstrate the capability of improved prognostics of faults in aircraft engine turbomachinery using models of system behavior, with continuous health monitoring data.

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Measurement Selections for Multi-Component Gas Path Diagnostics Using Analytical Approach and Measurement Subset Concept

Cited by 4 publications

References 0 publications

Simultaneous Fault Diagnostics for Three-Shaft Industrial Gas Turbine

Simultaneous Fault Diagnostics for Three-Shaft Industrial Gas Turbine

Sensor fault diagnosis of gas turbine engines using an integrated scheme based on improved least squares support vector regression

An Adaptive Model-Based Framework for Prognostics of Gas Path Faults in Aircraft Gas Turbine Engines

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