本論文應用智慧型電子裝置(Intelligent electrical device, IED)及失效模式與影響分析模糊規則(FMEA fuzzy rule)，設計一套具人工智慧之電力設備狀態維護管理系統架構。硬體方面，建議應用IED擷取各式類比訊號，作為狀態監測之部分參數，藉由網際網路將資料傳送至雲端伺服器。軟體方面，建議以風險序數分析(Risk priority number, RPN)對既有失效模式及影響分析結果作一風險分析，根據不同風險給予適當維護方式。最後，整合電力公司內部有效之資訊及IED彙集之即時數據，以模糊推論對電力設備失效機率作一預測，據設備失效機率安排維護工作，使電力設備維護更為合理化。
本文利用Matlab Fuzzy Tool Box，針對變壓器之鐵心、繞組及有載切換器之失效模式建立模糊規則。最後以八個具代表性變壓器事故範例說明推論過程且利用輸入不同之變壓器狀況以計算各部品之失效指標值。

This thesis presents an architecture of conditioning based maintenance management systems by integrating IEDs (Intelligent Electrical Device) and FMEA (Failure Mode and Effect Analysis) Fuzzy rules. IEDs acquire analog signals and send messages which are parameters for estimating conditions of electrical equipments to remote servers by Ethernet. This thesis proposes risk priority number to analyse and optimize the current maintenance systems which inspect conditions of electrical equipments based on time period. Furthermore, the experts can establish fine fuzzy rules with the analysis results. The conditioning based maintenance management systems integrate data distributed in different departments and information acquired by IEDs to infer the possible failure rate of components of electrical equipments with fuzzy rules. Therefore, operators can look for the best chance to maintain the facilities.
This thesis establishes fuzzy rules to forecast failure rates of iron cores, copper windings, and tap changers of power transformers. Eight examples demonstrate the inference process of failure rates with different index inputs related to the conditions of power transformers. The final results show the fuzzy rule can judge the condition of the power transformer.

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