Deep Learning-Based Fault Prediction for Electrical Equipment  

作　　者：Fuyang Miao 

机构地区：[1]Xihua University

出　　处：《计算机科学与技术汇刊(中英文版)》2024年第2期16-22,共7页Transactions on Computer Science and Technology

摘　　要：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.

关 键 词：Deep Learning Fault Prediction Electrical Equipment Convolutional Neural Networks Recurrent Neural Networks Data Preprocessing Model Evaluation 

分 类 号：P31[天文地球—固体地球物理学]