Uncertainties in gas-path diagnosis of gas turbines: Representation and impact analysis

Liao, Zengbu; Wang, Jian; Liu, Jinxin; Geng, Jianghui; Li, Ming; Chen, Xuefeng; Zhi-ping, Song

doi:10.1016/j.ast.2021.106724

Cited by 25 publications

(4 citation statements)

References 16 publications

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“…In this study, PLM HP and PLM are constructed based on equations (4) and (5), respectively, and they are used for HP estimation and sensor fault diagnosis to provide the prerequisite of information sharing design, respectively. In order to construct the PLM, it is necessary to construct the state space model in different working states, then interpolate the coefficient matrix to form a coefficient matrix library that can be applied to a large range of engine working states.…”

Section: State Space Modelmentioning

confidence: 99%

“…Aero-engines are core machinery systems with a complex structure, high level of integration, and poor working conditions, and their reliable and efficient operation is crucial to the flight safety of aircraft [1][2][3][4]. According to the statistics [5], the maintenance, repair and overhaul market of civil aviation wa valued at $81.9 billion in 2019, and will average * Authors to whom any correspondence should be addressed. 3.5% growth annually in the next decade.…”

Section: Introductionmentioning

confidence: 99%

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A modified fusion model-based/data-driven model for sensor fault diagnosis and performance degradation estimation of aero-engine

Huang¹,

Sun²,

Tao³

et al. 2022

Meas. Sci. Technol.

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Sensor fault diagnosis and performance degradation estimation (SFDPDE) play a critical role in the operation and maintenance of aero-engine. In this study, a modified fusion model driven by sensor measurements is proposed to overcome the drawbacks of single data-driven and single model-based methods. Two types of on-board models are established based on augmented state space equations, and a data-driven model based on extreme learning machine (ELM) is constructed for residual correction of the on-board model. A bidirectional information transmission algorithm is designed in the SFDPDE framework in order to include the function coordination. Kalman filter (KF) is employed as the optimal algorithm in the SFDPDE framework containing the standardized sensor parameter selection process. The experimental results indicate that, the proposed fusion model improves the accuracy of sensor fault diagnosis, reduces the mean square error (MSE) of health parameter (HP) estimations, while the information sharing module expands the application scope of SFDPDE and improves its accuracy as well as stability.

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Section: State Space Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A modified fusion model-based/data-driven model for sensor fault diagnosis and performance degradation estimation of aero-engine

Huang¹,

Sun²,

Tao³

et al. 2022

Meas. Sci. Technol.

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“…This relates both to the level of noise and uncertainty of the data, as well as the specific measurements available for diagnostics [37]. The importance of handling uncertainties in measurements is highlighted in [38]. A fundamental principle is that, regardless of method, a coupling between the available measurements and the fault or degradation to be estimated must be present [39].…”

Section: Introductionmentioning

confidence: 99%

Estimation and Mitigation of Unknown Airplane Installation Effects on GPA Diagnostics

Stenfelt

Kyprianidis

2022

Machines

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In gas turbines used for airplane propulsion, the number of sensors are kept at a minimum for accurate control and safe operation. Additionally, when data are communicated between the airplane main computer and the various subsystems, different systems may have different constraints and requirements regarding what data transmit. Early in the design process, these parameters are relatively easy to change, compared to a mature product. If the gas turbine diagnostic system is not considered early in the design process, it may lead to diagnostic functions having to operate with reduced amount of data. In this paper, a scenario where the diagnostic function cannot obtain airplane installation effects is considered. The installation effects in question is air intake pressure loss (pressure recovery), bleed flow and shaft power extraction. A framework is presented where the unknown installation effects are estimated based on available data through surrogate models, which is incorporated into the diagnostic framework. The method has been evaluated for a low-bypass turbofan with two different sensor suites. It has also been evaluated for two different diagnostic schemes, both determined and underdetermined. Results show that, compared to assuming a best-guess constant-bleed and shaft power, the proposed method reduce the RMS in health parameter estimation from 26% up to 80% for the selected health parameters. At the same time, the proposed method show the same degradation pattern as if the installation effects were known.

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“…In addition, other work has focused on the evaluation of the fatigue reliability of this type of rotating machine, such as the work of Herman Shen M.H. in [15] and the work of Rongzhuo Sun et al in [32] focused on the coupling of faults in diagnosis with the control of gas turbines, thus the work Sandy Rahme et al in [34] focused on the development of adaptive observers in sliding mode for the fault diagnosis of a gas turbine and the work of Yu Zhi Chen et al in [46] and Zengbu Liao et al in [47] focused on performance improvement and impact analysis on gas turbines with modern diagnostic approaches.…”

Section: Introductionmentioning

confidence: 99%

Gas turbine reliability estimation to reduce the risk of failure occurrence with a comparative study between the two-parameter Weibull distribution and a new modified Weibull distribution

Zohair¹,

Hafaifa²,

Iratni³

et al. 2022

Diagnostyka

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Responding to the needs of quality and robustness of analysis and management of degradation of equipment, to increase their life cycle and to expand these facilities to become more and more sophisticated and agronomic. This work proposes a contribution to increase the survival of a gas turbine, installed in a gascompression plant, with a comparative study between the two-parameter Weibull distribution. A new modified Weibull distribution was proposed also to reduce the risk of occurrence of failure in this rotating machine. A Statistical analysis and validation on the synthesis of turbine's reliability data and failures were considered, with a particular focus on the use of this data to increase the availability of this type of machine. So, developing a maintenance plan based on their reliability indices for scheduled inspections

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Uncertainties in gas-path diagnosis of gas turbines: Representation and impact analysis

Cited by 25 publications

References 16 publications

A modified fusion model-based/data-driven model for sensor fault diagnosis and performance degradation estimation of aero-engine

A modified fusion model-based/data-driven model for sensor fault diagnosis and performance degradation estimation of aero-engine

Estimation and Mitigation of Unknown Airplane Installation Effects on GPA Diagnostics

Gas turbine reliability estimation to reduce the risk of failure occurrence with a comparative study between the two-parameter Weibull distribution and a new modified Weibull distribution

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