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计算机科学与技术汇刊

《计算机科学与技术汇刊》是IVY出版社旗下的一本关注计算机理论与技术应用发展的国际期刊,是计算机理论与现代工业技术相结合的综合性学术刊物。主要刊登有关计算机理论,及其在自然科学、工程技术、经济和社会等各领域内的最新研究进展的学术性论文和评论性文章。旨在为该领域内的专家、学者、科研人员提供一个良好的传播、分享和探讨计算机理论与技术进展的交流平台,反映学术前沿水平,促进学术交流,推进计算机理论和应用技术的发展。本刊可接收中、英文稿件。其中,中…… 【更多】 《计算机科学与技术汇刊》是IVY出版社旗下的一本关注计算机理论与技术应用发展的国际期刊,是计算机理论与现代工业技术相结合的综合性学术刊物。主要刊登有关计算机理论,及其在自然科学、工程技术、经济和社会等各领域内的最新研究进展的学术性论文和评论性文章。旨在为该领域内的专家、学者、科研人员提供一个良好的传播、分享和探讨计算机理论与技术进展的交流平台,反映学术前沿水平,促进学术交流,推进计算机理论和应用技术的发展。

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ISSN Online:2327-0918

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Paper Infomation

Deep Learning-Based Fault Prediction for Electrical Equipment

Full Text(PDF, 58KB)

Author: Fuyang Miao

Abstract: With the rapid advancement of deep learning and the increasing availability of large-scale data, fault prediction for electrical equipment has become a vital area of research. This paper explores the application of deep learning techniques in predicting faults within electrical systems, focusing on the challenges and methodologies that can enhance prediction accuracy and system reliability. Traditional fault prediction methods, such as threshold-based models and statistical approaches, often fall short in handling complex, nonlinear data and large-scale systems. In contrast, deep learning models, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have shown significant promise in learning from large and diverse datasets to detect subtle patterns that indicate potential failures. This paper also discusses the importance of data collection and preprocessing, model training, evaluation metrics, and cross-validation techniques, all of which contribute to improving the robustness and accuracy of fault prediction models. Despite the advancements, challenges remain, such as data quality, model interpretability, and computational efficiency. The paper concludes by outlining future research directions and the potential impact of emerging technologies like the Internet of Things (IoT) and edge computing in the field of fault prediction.

Keywords: Deep Learning, Fault Prediction, Electrical Equipment, Convolutional Neural Networks, Recurrent Neural Networks, Data Preprocessing, Model Evaluation

References:

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