%0 Journal Article %T 基于深度学习的电气自动化设备故障诊断与预测技术研究
Research on Fault Diagnosis and Prediction Technology of Electrical Automation Equipment Based on Deep Learning %A 胡惠媛 %A 赵祖玲 %J Journal of Sensor Technology and Application %P 447-458 %@ 2331-0243 %D 2025 %I Hans Publishing %R 10.12677/jsta.2025.133045 %X 随着电气自动化设备在工业中的广泛应用，故障诊断与预测技术成为确保设备可靠性和安全性的重要研究方向。本研究关注于深度学习技术在电气自动化设备故障诊断与预测中的应用，通过系统梳理深度学习的基础知识和其在相关领域的应用现状，对比分析不同深度学习模型的优势与局限，明确本文的研究定位。在此基础上，综合考虑电气自动化设备故障的类型和特征，采取有效的数据采集与预处理策略，并运用特征提取方法提炼故障信息。本研究进一步探讨深度学习模型在故障诊断实际中的应用，包括模型构建、训练和参数优化等关键技术，通过实验验证该模型的有效性。同时，针对故障预测，构建符合故障动态特性的预测模型，通过案例分析展示其在实际工业环境中的应用潜力。本研究的成果不仅提高了电气自动化设备的监测和维护效率，还有助于推动智能制造领域的发展。
With the widespread application of electrical automation equipment in industry, fault diagnosis and prediction technology has become an important research direction to ensure the reliability and safety of equipment. This study focuses on the application of deep learning technology in the fault diagnosis and prediction of electrical automation equipment. It systematically organizes the foundational knowledge of deep learning and its current applications in related fields, conducting a comparative analysis of the advantages and limitations of different deep learning models, thus clarifying the research positioning of this paper. On this basis, taking into account the types and characteristics of electrical automation equipment faults, effective data collection and preprocessing strategies are adopted, and feature extraction methods are employed to distill fault information. This research further explores the practical application of deep learning models in fault diagnosis, including key techniques such as model construction, training, and parameter optimization, and validates the effectiveness of the model through experimentation. Additionally, for fault prediction, a predictive model that aligns with the dynamic characteristics of faults is constructed, and case analyses demonstrate its application potential in actual industrial environments. The results of this study not only enhance the monitoring and maintenance efficiency of electrical automation equipment but also contribute to the advancement of the intelligent manufacturing field. %K 深度学习， %K 电气自动化， %K 故障诊断， %K 故障预测， %K 特征提取， %K 模型优化
Deep Learning %K Electrical Automation %K Fault Diagnosis %K Fault Prediction %K Feature Extraction %K Model Optimization %U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=115234