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

Hu, Yuxiao; Zhu, Jizhong; Sun, Zhigang; Gao, Lijia

doi:10.1177/0954410019873795

Cited by 12 publications

(8 citation statements)

References 31 publications

(36 reference statements)

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“…The results showed good accuracy, up to 90%, with a shorter diagnosis time. The work presented in [86] aimed to develop an online diagnosis system for gas path sensor faults in GTs. A genetic algorithm (GA) was designed and optimised by the recursive reduced least squares support vector regression (RRLSSVR) algorithm.…”

Section: Sensor Faultsmentioning

confidence: 99%

“…Sepehr Maleki and Chris Bingham [87] 2019 Hierarchical clustering One-class sensor fault detection Linhai Zhu et al [84] 2020 SRCKF + DBSCAN Sensor faults' identification in harsh conditions Rongzhuo Sun et al [85] 2020 RNN Coupling diagnosis method for sensor fault identification Yu Hu et al [86] 2020 GA-RRLSSVR Online diagnosis for gas path sensor faults Ying Liu et al [89] 2020 ANN Sensor fault detection and verification in a digital simulation platform…”

Section: Year ML Model Applicationmentioning

confidence: 99%

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Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

2021

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Condition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machine-learning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment.

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Section: Sensor Faultsmentioning

confidence: 99%

Section: Year ML Model Applicationmentioning

confidence: 99%

Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

2021

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“…The data‐based method has garnered significant attention due to its inherent advantage of not requiring an explicit mathematical model of the turbine. In this regard, methods such as fuzzy expert systems, 8 genetic algorithms, 9 and a variety of neural network and deep neural network‐based methods have been used for performance monitoring and availability improvement in gas turbines 10,11 . In this regard, Yazdani and Montazeri proposed the use of type‐2 fuzzy logic to detect, isolate, and identify gas turbine faults 12 .…”

Section: Introductionmentioning

confidence: 99%

“…In this regard, methods such as fuzzy expert systems, 8 genetic algorithms, 9 and a variety of neural network and deep neural network-based methods have been used for performance monitoring and availability improvement in gas turbines. 10,11 In this regard, Yazdani and Montazeri proposed the use of type-2 fuzzy logic to detect, isolate, and identify gas turbine faults. 12 Given the propensity for type-2 fuzzy logic to exhibit superior performance in tackling intricate nonlinear problems characterized by significant data pattern overlaps, Montazeri and Yazdani have adopted this approach for the identification of gas path faults in industrial gas turbines.…”

mentioning

confidence: 99%

Noise‐robust gas path fault detection and isolation for a power generation gas turbine based on deep residual compensation extreme learning machine

Nekoonam,

Montazeri‐Gh

2023

Energy Science & Engineering

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One of the major challenges facing fault diagnosis tools is their exposure to noise. The presence of noise may cause false alarms or the inability to detect a progressive fault in the early stages of its occurrence. Continuing previous efforts to address such a problem, in this paper, a noise‐robust diagnosis system for an industrial gas turbine is presented. The proposed structure employs a set of deep residual compensation extreme learning machines (DRCELMs). In this model, an optimal number of compensating blocks are trained to recover some of the lost useful information in the face of noise. Training and testing data required to develop the fault diagnosis model are generated by a performance model of the studied gas turbine. The t‐distributed stochastic neighbor embedding algorithm is employed for visualizing the gas path faults. Furthermore, the performance of the DRCELM is evaluated by comparing it with six other diagnosis models. The results indicate higher robustness of the DRCELM compared to other fault diagnosis systems. The proposed model presents a classification accuracy of >97% in noisy data and an accuracy of >98% in noise‐free data and combined data, while the average of fault positive rate and fault negative rate in noisy data is less than 2.5%.

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“…Therefore, this study proposes a regression analysis method of performance degradation based on the support vector regression (SVR) model, which uses two relaxation variables to control the sample isolation band and takes the band width and total loss as optimization target. 16…”

Section: Introductionmentioning

confidence: 99%

Performance degradation prediction of aeroengine based on attention model and support vector regression

Che

Wang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part G:

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Accurate performance degradation prediction of aeroengines can ensure the safety and reliability of the aircraft. Based on the mass long time series data of multiple state parameters, a novel performance degradation prediction method based on attention model (AM) and support vector regression (SVR) is proposed in this article. The AM uses the attention mechanism between encoder and decoder to realize weight distribution of different source samples, so as to realize time series prediction of state parameters. The SVR model is used to mine the mapping relationship between multiple state parameters and performance degradation. The performance degradation prediction results can be achieved by putting the time series prediction results of multiple state parameters into the SVR model. The turbofan engine degradation simulation dataset carried out using commercial modular aero-propulsion system simulation (C-MAPSS) is used to verify the effectiveness of the proposed method. The results demonstrate that it can get accurate time series prediction and performance degradation analysis results. Compared with other methods, the proposed attention model and support vector regression (AM-SVR) model has lower prediction error and higher stability when dealing with noised samples.

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

Cited by 12 publications

References 31 publications

Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

Noise‐robust gas path fault detection and isolation for a power generation gas turbine based on deep residual compensation extreme learning machine

Performance degradation prediction of aeroengine based on attention model and support vector regression

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