Fault Diagnosis of Gas Turbine Engines by Using Dynamic Neural Networks

Mohammadi, R.; Naderi, E.; Khorasani, K.; Hashtrudi-Zad, S.

doi:10.1115/gt2010-23586

Cited by 18 publications

(7 citation statements)

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“…It was pointed out that those fault diagnostic algorithms were having better early detection ability with smaller false alarms, higher fault classification rate, and more efficient fault identification than the other AI techniques. Recently, Tayarani-Bathaie et al [135], Mohammadi et al [136], Kiakojoori and Khorasani [137], and Vanini et al [62] proposed a dynamic neural network (DNN) fault diagnostic techniques for aircraft engine applications More recently, an ensemble GT fault diagnosis system was devised by Amozegar and Khorasani [138] using different types of MLP networks. Nested MLP networks were also used to a fault detection and isolation application by Tahan et al [139].…”

Section: Multilayer Perceptronmentioning

confidence: 99%

A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities

et al. 2019

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Gas-path diagnostics is an essential part of gas turbine (GT) condition-based maintenance (CBM). There exists extensive literature on GT gas-path diagnostics and a variety of methods have been introduced. The fundamental limitations of the conventional methods such as the inability to deal with the nonlinear engine behavior, measurement uncertainty, simultaneous faults, and the limited number of sensors available remain the driving force for exploring more advanced techniques. This review aims to provide a critical survey of the existing literature produced in the area over the past few decades. In the first section, the issue of GT degradation is addressed, aiming to identify the type of physical faults that degrade a gas turbine performance, which gas-path faults contribute more significantly to the overall performance loss, and which specific components often encounter these faults. A brief overview is then given about the inconsistencies in the literature on gas-path diagnostics followed by a discussion of the various challenges against successful gas-path diagnostics and the major desirable characteristics that an advanced fault diagnostic technique should ideally possess. At this point, the available fault diagnostic methods are thoroughly reviewed, and their strengths and weaknesses summarized. Artificial intelligence (AI) based and hybrid diagnostic methods have received a great deal of attention due to their promising potentials to address the above-mentioned limitations along with providing accurate diagnostic results. Moreover, the available validation techniques that system developers used in the past to evaluate the performance of their proposed diagnostic algorithms are discussed. Finally, concluding remarks and recommendations for further investigations are provided.

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Section: Multilayer Perceptronmentioning

confidence: 99%

A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities

et al. 2019

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“…Finally, our proposed prognosis strategy is applied to a gas turbine engine application to predict the system health parameters variations when it is subjected to soft degradation damages. The mathematical model of the considered gas turbine engine has been already verified and validated in our earlier works [31]- [33] by utilizing the commercially available software simulation toolbox GSP10 [34]. Based on the predicted system health parameters, the remaining useful life of the engine is determined.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

An improved particle filtering-based approach for health prediction and prognosis of nonlinear systems

Daroogheh

Meskin

Khorasani

2018

Journal of the Franklin Institute

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Health monitoring of nonlinear systems is broadly concerned with the system health tracking and its prediction to future time horizons. Estimation and prediction schemes constitute as principle components of any health monitoring technique. Particle filter (PF) represents a powerful tool for performing state and parameter estimation as well as prediction of nonlinear dynamical systems. Estimation of the system parameters along with the states can yield an up-to-date and reliable model that can be used for long-term prediction problems through utilization of particle filters. This feature enables one to deal with uncertainty issues in the resulting prediction step as the time horizon is extended. Towards this end, this paper presents an improved method to achieve uncertainty management for long-term prediction of nonlinear systems by using particle filters. In our proposed approach, an observation forecasting scheme is developed to extend the system observation profiles (as time-series) to future time horizon. Particles are then propagated to future time instants according to a resampling algorithm instead of considering constant weights for the particles propagation in the prediction step. The uncertainty in the long-term prediction of the system states and parameters are managed by utilizing dynamic linear models for development of an observation forecasting scheme. This task is addressed through an outer adjustment loop for adaptively changing the sliding observation injection window based on the Mahalanobis distance criterion. Our proposed approach is then applied to predicting the health condition as well as the remaining useful life (RUL) of a gas turbine engine that is affected by degradations in the system health parameters. Extensive simulation and case studies are conducted to demonstrate and illustrate the capabilities and performance characteristics of our proposed and developed schemes.

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“…Timely detection of such anomalies in machinery has many applications, which include reduced downtime, reduced maintenance cost and less safety hazards. Increasing focus on reliability and maintenance of complex systems like turbofan engines demands intelligent and autonomous ways to manage the health of these safety critical systems [1]. One such way is to deploy autonomous anomaly detection to monitor the health of turbofan engines.…”

Section: Introductionmentioning

confidence: 99%

Autoencoder based Semi-Supervised Anomaly Detection in Turbofan Engines

Bataineh¹,

Mairaj²,

Kaur³

2020

IJACSA

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This paper proposes a semi-supervised autoencoder based approach for the detection of anomalies in turbofan engines. Data used in this research is generated through simulation of turbofan engines created using a tool known as Commercial Modular Aero-Propulsion System Simulation (CMAPSS). C-MAPSS allows users to simulate various operational settings, environmental conditions, and control settings by varying various input parameters. Optimal architecture of autoencoder is discovered using Bayesian hyperparameter tuning approach. Autoencoder model with optimal architecture is trained on data representing normal behavior of turbofan engines included in training set. Performance of trained model is then tested on data of engines included in test set. To study the effect of redundant features removal on performance, two approaches are implemented and tested: with and without redundant features removal. Performance of proposed models is evaluated using various performance evaluation metrics like F1-score, Precision and Recall. Results have shown that best performance is achieved when autoencoder model is used without redundant feature removal.

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Fault Diagnosis of Gas Turbine Engines by Using Dynamic Neural Networks

Cited by 18 publications

References 0 publications

A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities

A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities

An improved particle filtering-based approach for health prediction and prognosis of nonlinear systems

Autoencoder based Semi-Supervised Anomaly Detection in Turbofan Engines

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