Dynamie neural networks for jet engine degradation prediction and prognosis

Kiakojoori, S.; Khorasani, K.

doi:10.1109/ijcnn.2014.6889694

Cited by 3 publications

(2 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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“…rough a series of calculation, analysis, and learning processes, the object recognition in the scene, the description of the relationship between the objects in the scene, and the recognition of the scene are obtained [1]. In short, image recognition.…”

Section: Introductionmentioning

confidence: 99%

Image Recognition Algorithm Based on Artificial Intelligence and Machine Learning

Liu

2022

Mobile Information Systems

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At present, most of the existing image recognition methods are not only cumbersome in process but also require manual design of functions, resulting in poor recognition results and time-consuming training. This study explores image recognition algorithms based on artificial intelligence and machine learning, which can simulate the hierarchical structure of the human brain and nervous system, realize automatic extraction of complex features, and have powerful data representation capabilities. In this study, the structure of artificial neuron is introduced first, and the training process of the neural network is introduced in detail. At the same time, this study improves the traditional training algorithm and proposes two new machine learning models for different application scenarios, which effectively improves the performance of the optimal model. In relevant tests, the effect is significant. On the basis of these improved algorithms, an online recognition system is designed and implemented. The recognition accuracy rate of the system for the hidden layer reaches more than 90%, which verifies the effectiveness of the technology. At the same time, after repeated experiments, the results show that the multiview algorithm effectively solves the problem that the recognition results of the traditional multiview algorithm are affected by the size of the target contour.

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