电力设备故障红外诊断系统的研究与实现

Research and Implementation of Power Equipment Fault Infrared Diagnosis System

随着电力企业状态检修工作的深入开展,红外测温因其不用停电并能检测设备是否存在故障的优势,已成为电力设备带电检测的重要手段,但目前红外测温图谱的分析工作主要依靠专业技术人员凭借经验完成,因人员专业技术水平的差异、人员细心程度和工作疲劳程度等原因可能导致设备诊断出现偏差给设备安全运行埋下隐患。基于上述原因,文章介绍一种以人工智能领域规则和案例推理为基础的电力设备故障红外诊断系统,实现电力设备红外测温现场的测温数据录入和设备故障诊断。

Along with the deepening of power enterprise condition maintenance working, infrared temperature measurement has been an important online fault diagnosis method due to its advantage of detection running equipments. However, the infrared spectrum analysis mainly relies on professional technical personnel with experience, which may result in equipments operation risk from diagnosis deviation because of their technical level, focus and fatigue. To solve these issues, this paper introduces a power equipment fault infrared diagnosis system based on artificial intelligence domain rules and case based reasoning, which can meet the needs of field temperature data entry and equipment fault diagnosis.