摘要
本文全面深入地探讨了多源信息融合技术在电力设备故障诊断中的应用。首先，对信息融合的基本概念、发展现状与应用领域进行了概述，并详细分析了其关键技术。在此基础上，文章着重阐述了多源信息融合技术在电力设备故障诊断中的四大优势：提高故障诊断的准确性、提升故障诊断的实时性、增强故障诊断的鲁棒性、降低故障诊断成本。然而，文章也客观地指出了当前多源信息融合技术在电力设备故障诊断应用中存在的一些问题，包括数据冗余与不一致性、数据同步性问题、数据不确定性与不完整性，以及技术复杂性与实施难度等。针对这些问题，文章提出了一系列具有针对性的优化建议，包括强化数据预处理与清洗，具体涉及数据质量评估与筛选、噪声去除与异常值检测、数据标准化与归一化处理；实现数据同步与对齐，通过建立时间戳同步机制、异步数据的融合策略、采用中间件实现跨系统同步；研发高效的数据融合算法，如优化信息权重分配策略、应用深度学习提升融合效果、开发自适应融合模型；以及加强技术培训与人才培养，通过设立专门的培训课程体系、实施在职员工技能提升计划、制定定期技术交流和研讨会制度等。
关键词：多源信息融合技术；电力设备；故障诊断；数据冗余；数据同步
Abstract
This paper discusses the application of multi-source information fusion technology in power equipment fault diagnosis. Firstly, the basic concept, development status and application fields of information fusion are summarized, and its key technologies are analyzed in detail. On this basis, the paper focuses on four advantages of multi-source information fusion technology in power equipment fault diagnosis: improve the accuracy of fault diagnosis, improve the real-time fault diagnosis, enhance the robustness of fault diagnosis, and reduce the cost of fault diagnosis. However, this paper also ob jectively points out some problems existing in the application of multi-source information fusion technology in power equipment fault diagnosis, including data redundancy and inconsistency, data synchronization, data uncertainty and incomplete, and technical complexity and implementation difficulty. To solve these problems, this paper puts forward a series of targeted optimization suggestions, including strengthening data preprocessing and cleaning, including data quality assessment and screening, noise removal and outlier detection, data standardization and normalization. To realize data synchronization and alignment, through establishing timestamp synchronization mechanism, asynchronous data fusion strategy, using middleware to realize cross-system synchronization; Develop efficient data fusion algorithms, such as optimizing information weight allocation strategies, applying deep learning to improve fusion effects, and developing adaptive fusion models; And strengthen technical training and personnel training, through the establishment of a special training course system, the implementation of on-the-job staff skills improvement plan, the development of regular technical exchange and seminar system.
Keywords： Multi-source information fusion technology; Electric power equipment; Fault diagnosis; Data redundancy; Data synchronization
目 录
摘要 I
Abstract II
一、绪论 1
（一）研究背景 1
（二）研究目的及意义 1
二、多源信息融合技术概述 2
（一）信息融合的基本概念 2
（二）发展现状与应用领域 2
（三）关键技术分析 2
三、多源信息融合技术在电力设备故障诊断中的应用 4
（一）提高故障诊断的准确性 4
（二）提升故障诊断的实时性 4
（三）增强故障诊断的鲁棒性 4
（四）降低故障诊断成本 4
四、多源信息融合技术在电力设备故障诊断中存在的问题 6
（一）数据冗余与不一致性 6
（二）数据同步性问题 6
（三）数据不确定性与不完整性 6
（四）技术复杂性与实施难度 6
五、多源信息融合技术在电力设备故障诊断中的优化建议 8
（一）强化数据预处理与清洗 8
（二）实现数据同步与对齐 9
（三）研发高效的数据融合算法 10
（四）加强技术培训与人才培养 10
结 论 12
参考文献 13
