Research on fault diagnosis method of aviation cable based on improved Adaboost

Wang, Falin; Yuan, Gang; Guo, Chaoyang; Li, Zhinong

doi:10.1177/16878132221125762

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Before generating the next base classifier, AdaBoost will amplify the weights of the misclassified samples while reducing the weights of the correctly classified samples. This approach makes the next iteration of the algorithm more focused on the misclassified samples [16]. The formula for updating the sample weights is as follows:…”

Section: Theoretical Basis Of Adaboost Algorithmmentioning

confidence: 99%

Power Transformer Fault Diagnosis Based on Feature Selection and Improved IPSO-BP-AdaBoost

Zhou,

Fu,

et al. 2024

Preprint

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This paper proposes a novel fault diagnosis methodology for oil-immersed transformers to improve the diagnostic accuracy influenced by gas components in power transformer oil. Firstly, the Random Forest (RF) algorithm is utilized to evaluate and filter the raw data features. Secondly, a multi-strategy Improved Particle Swarm Optimization (IPSO) is applied to optimize a double-hidden layer Backpropagation Neural Network (BPNN). Four enhancement strategies, including SPM chaos mapping based on opposition-based learning, adaptive weight, spiral flight search, and crisscross strategies, are introduced based on traditional Particle Swarm Optimization (PSO) to enhance the model’s optimization capabilities. Lastly, AdaBoost is integrated to fortify the resilience of the IPSO-BP network. Ablation experiments demonstrate an enhanced convergence rate and model accuracy of IPSO. Case analysis using Dissolved Gas Analysis (DGA) samples compares the proposed IPSO-BP-AdaBoost model with other swarm intelligence optimization algorithms integrated with BPNN. The experimental findings highlight the superior diagnostic accuracy and classification performance of the IPSO-BP-AdaBoost model.

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Section: Theoretical Basis Of Adaboost Algorithmmentioning

confidence: 99%

Power Transformer Fault Diagnosis Based on Feature Selection and Improved IPSO-BP-AdaBoost

Zhou,

Fu,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…As an efficient nonlinear algorithm in the Boosting learning family, BP-Adaboost modelling strategy holds the high modelling effects and had been widely used in traffic flow prediction, image classification, fingerprint recognition and other fields [34][35][36]. However, the BP-Adaboost may also cause the hyperparameter optimization issues when dealing with the highly complex problems.…”

Section: Introductionmentioning

confidence: 99%

Fatigue reliability analysis of aeroengine blade-disc systems using physics-informed ensemble learning

Li,

Song,

Choy

et al. 2023

Phil. Trans. R. Soc. A.

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For the fatigue reliability analysis of aeroengine blade-disc systems, the traditional direct integral modelling methods or separate independent modelling methods will lead to low computational efficiency or accuracy. In this work, a physics-informed ensemble learning (PIEL) method is proposed, i.e. firstly, based on the physical characteristics of blade-disc systems, the complex multi-component reliability analysis is split into a series of single-component reliability analyses; moreover, the PIEL model is established by introducing the mapping of multiple constitutive responses and the multi-material physical characteristics into the ensemble learning; finally, the PIEL-based system reliability framework is established by quantifying the failure correlation with the Copula function. The reliability analysis of a typical aeroengine high-pressure turbine blade-disc system is regarded as an example to verify the effectiveness of the proposed method. Compared with the direct Monte Carlo, support vector regression, neural network, ensemble learning and physics-informed neural network, the proposed method exhibits the highest computing accuracy and efficiency, and is validated to be an efficient method for the reliability analysis of blade-disc systems. The current work can provide a novel insight for physics-informed modelling and fatigue reliability analyses. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.

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Exploring forest fire susceptibility and management strategies in Western Himalaya: Integrating ensemble machine learning and explainable AI for accurate prediction and comprehensive analysis

Thi Hang,

Mallick,

Alqadhi

et al. 2024

Environmental Technology & Innovation

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Research on fault diagnosis method of aviation cable based on improved Adaboost

Cited by 5 publications

References 13 publications

Power Transformer Fault Diagnosis Based on Feature Selection and Improved IPSO-BP-AdaBoost

Power Transformer Fault Diagnosis Based on Feature Selection and Improved IPSO-BP-AdaBoost

Fatigue reliability analysis of aeroengine blade-disc systems using physics-informed ensemble learning

Exploring forest fire susceptibility and management strategies in Western Himalaya: Integrating ensemble machine learning and explainable AI for accurate prediction and comprehensive analysis

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