ARIMA-AdaBoost hybrid approach for product quality prediction in advanced transformer manufacturing

Chien, Chun-Hua; Trappey, Amy J.C.; Wang, Chien-Chih

doi:10.1016/j.aei.2023.102055

Cited by 13 publications

(3 citation statements)

References 56 publications

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“…Differences in materials, manufacturing processes, production batc transportation methods and other factors among different manufacturers c ferences in transformer life-cycle costs [24,25]. In order to further analyze…”

Section: Transformer Cost Comparison By Manufacturermentioning

confidence: 99%

“…Differences in materials, manufacturing processes, production batches, lead times, transportation methods and other factors among different manufacturers can result in differences in transformer life-cycle costs [24,25]. In order to further analyze the influencing factors of the cost differences between different manufacturers, a more detailed study was carried out.…”

Section: Transformer Cost Comparison By Manufacturermentioning

confidence: 99%

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Enhancing Economic Efficiency: Analyzing Transformer Life-Cycle Costs in Power Grids

Gui,

Chen,

Zhao

et al. 2024

Energies

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The transformer is a fundamental piece of equipment for power grids. The analysis and optimization of their life-cycle costs are of great importance to reinforce the economic efficiency of electrical networks. This paper constructs a comprehensive transformer life-cycle cost (LCC) model by fusing life-cycle cost theory with relevant transformer expenditure. It proceeds to examine the life-cycle cost aspects of the transformer, delving into its cost dynamics under various influencing factors, establishing interconnections between these factors and analyzing the cost relationship. By employing MATLAB software (Matlab 2021a) along with the whale optimization algorithm (WOA) this paper optimizes the objective function. Through this, it establishes the LCC model for 20 power transformers, obtaining the optimal objective function curve and the maximum value for LCC optimization of the transformer. Unlike previous research, this study adds a detailed analysis of several factors that influence LCC. At the same time, it develops a more complete, scientific and rational LCC optimization model. An illustrative example validates the model and the superiority of the whale optimization algorithm. The algorithm not only shows its scientific basis and superiority, but also serves as a guiding mechanism for LCC management in transformer engineering practices. Ultimately, it emerges as a fundamental tool to improve the efficiency of power grid asset management.

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Section: Transformer Cost Comparison By Manufacturermentioning

confidence: 99%

Section: Transformer Cost Comparison By Manufacturermentioning

confidence: 99%

Enhancing Economic Efficiency: Analyzing Transformer Life-Cycle Costs in Power Grids

Gui,

Chen,

Zhao

et al. 2024

Energies

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“…Adaptive Boosting (AdaBoost) (Shrestha & Solomatine, 2006; Chien et al ., 2023), stands as a popular machine learning technique renowned for its ability to enhance predictive model performance, particularly well‐suited for dealing with complex and high‐dimensional NIR data. AdaBoost effectively elevates the overall model performance, excelling notably in quantitative analysis tasks.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis of reducing sugars in solid‐state fermentation based on NIR combined with GA‐AdaBoost

Zhao,

Sun,

et al. 2024

Int J of Food Sci Tech

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SummaryThis study focuses primarily on the real‐time monitoring of reducing sugar levels throughout solid‐state ethanol fermentation. A predictive model for accurate quantification of reducing sugar levels will be formulated through a comprehensive analysis of near‐infrared spectra across different fermentation stages. Building a predictive model using Adaptive Boosting (AdaBoost), The spectral data was pre‐processed using the Standard Normal Variate (SNV) method, followed by feature band selection using the Genetic Algorithm (GA), resulting in the identification of 181 characteristic wavelengths. The resulting GA‐AdaBoost model has excellent performance metrics: of 0.989, RMSEC of 0.393, of 0.978, RMSEP of 0.640, RPD of 6.651 and RER of 0.142. Comparative evaluations against Competitive Adaptive Reweighted Sampling (CARS), Successive Projection Algorithm (SPA), Uninformative Variable Elimination (UVE) and Principal Component Analysis (PCA) consistently underscore the superior efficacy of the GA‐AdaBoost model. These results robustly confirm its reliability in accurately quantifying reducing sugars in solid‐state fermentation processes.

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Multi-modal Quality Prediction Algorithm Based on Anomalous Energy Tracking Attention

Li,

Zhang,

et al. 2024

Lecture Notes in Computer Science

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ARIMA-AdaBoost hybrid approach for product quality prediction in advanced transformer manufacturing

Cited by 13 publications

References 56 publications

Enhancing Economic Efficiency: Analyzing Transformer Life-Cycle Costs in Power Grids

Enhancing Economic Efficiency: Analyzing Transformer Life-Cycle Costs in Power Grids

Quantitative analysis of reducing sugars in solid‐state fermentation based on NIR combined with GA‐AdaBoost

Multi-modal Quality Prediction Algorithm Based on Anomalous Energy Tracking Attention

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