Fault Diagnosis of PT Fuel System Based on Particle Swarm Optimization-Support Vector Machine

Wang, Dong; Wang, Xin Qing; Wang, Xiaolong; Liang, Sheng; Zhao, Yang

doi:10.4028/www.scientific.net/amm.121-126.2809

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Apart from the two samples mentioned in Section 4.2.1 [89,90], Ref. [97] applied an SVM with optimised parameters in a diesel engine's fuel system for the fault diagnosis. They found that compared with neural networks, SVMs can achieve better training results with a limited number of samples, and are less likely to fall into local optimum and overfitting problems.…”

Section: Support Vector Machinementioning

confidence: 99%

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

King

Jennions

2023

Machines

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The fuel system, which aims to provide sufficient fuel to the engine to maintain thrust and power, is one of the most critical systems in the aircraft. However, possible degradation modes, such as leakage and blockage, can lead to component failure, affect performance, and even cause serious accidents. As an advanced maintenance strategy, Condition Based Maintenance (CBM) can provide effective coverage, by combining state-of-the-art sensors with data acquisition and analysis techniques to guide maintenance before the asset’s degradation becomes serious. Artificial Intelligence (AI), particularly machine learning (ML), has proved effective in supporting CBM, for analyzing data and generating predictions regarding the asset’s health condition, thus influencing maintenance plans. However, from an engineering perspective, the output of ML algorithms, usually in the form of data-driven neural networks, has come into question in practice, as it can be non-intuitive and lacks the ability to provide unambiguous engineering signals to maintainers, making it difficult to trust. Engineers are interested in a deterministic decision-making process and how it is being revealed; algorithms should be able to certify and convince engineers to approve recommended actions. Explainable AI (XAI) has emerged as a potential solution, providing some of the logic on how the output is derived from the input given, which may help users understand the diagnostic result of the algorithm. In order to inspire and advise data scientists and engineers who are about to develop and use AI approaches in fuel systems, this paper explores the literature of experiment, simulation, and AI-based diagnostics for the fuel system to make an informed statement as to the progress that has been made in intelligent fault diagnostics for fuel systems, emphasizing the necessity of giving unambiguous engineering signals to maintainers, as well as highlighting potential areas for future research.

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Section: Support Vector Machinementioning

confidence: 99%

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

King

Jennions

2023

Machines

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“…We will track the latest progress of SVM optimised by PSO as follows. In Wang and Wang (2012), in order to overcome the difficulty in selecting parameters of SVM when modelling the PT fuel system fault diagnosis, SVM optimised by PSO algorithm was proposed. The result of experiment confirmed the validity of this method through comparison of the BP-NN, SVM.…”

Section: Research Progress On Parameters Optimisation Of Svm With Psomentioning

confidence: 99%

Research on parameters optimisation of SVM based on swarm intelligence

Ding¹,

Huang²

2014

IJCI

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Support vector machine (SVM) is a new machine learning method based on statistical learning theory, which has become a hot research topic in the field of machine learning because of its excellent performance. However, the performance of SVM is very sensitive to its parameters. At present, swarm intelligence is the most common method to optimise the parameters of SVM. In this paper, the research on parameters optimisation of SVM based on swarm intelligence algorithms is reviewed. Firstly, we briefly introduce the theoretical basis of SVM. Secondly, we describe the latest progress of parameters optimisation of SVM based on swarm intelligence in recent years. Finally, we point out the research and development prospects of this kind of method.

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Fault Diagnosis of PT Fuel System Based on Particle Swarm Optimization-Support Vector Machine

Cited by 2 publications

References 6 publications

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

Research on parameters optimisation of SVM based on swarm intelligence

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