目前水庫設施故障診斷多以檢核表進行評估，其成果與品質受限於檢測人員的經驗與經歷，無法達到客觀性判斷及預知維修之目標。有鑑於此，本研究首先以韋伯分配（Weibull Distribution）推求系統可靠度及整體壽命分配，並藉由專家學者及歷史實際故障維修統計資料建立多狀態失誤樹（Multi-State Fault Tree），除先利用失誤樹分析（Fault Tree Analysis, FTA）進行頂事件及各中間事件故障診斷外，同時將該多狀態失誤樹轉換成貝氏網路（Bayesian Network, BN），並加入專家經驗及意見導入節點間橫向連結及權重，接著利用AgenaRisk軟體計算建立條件機率表（Condition Probability Table, CPT）及所建立的貝氏網路作為模型進行故障診斷，同時以現有營運中之石門水庫三部水力發電系統作為分析及驗證對象，經分析比對貝氏網路計算結果與韋伯分配、失誤樹分析、歷史統計資料及系統故障大修後實際運轉統計之平均故障間隔時間（Mean Time Between Failure, MTBF）相符，顯示本研究由多狀態失誤樹轉換成貝氏網路架構，配合導入專家經驗之橫向連結及權重所建立之貝氏網路模型具有合理性與可用性，可作為日後水力發電系統故障診斷脆弱環節及研訂有效維修策略之用。

Current fault diagnosis of reservoir facilities relies mostly on check-list evaluation. The results and qualities of evaluation are limited by experiences and abilities of the evaluators, which may not achieve the goal of systematic assessment in a consistent manner. To overcome the limitation of the traditional approach, We first use the Weibull distribution to calculate the system failure process and the failure distribution, and then this research develops a fault diagnosis and evaluation system for reservoir facility by utilizing multi-state Fault-Tree Analysis (FTA) technique, in conjuction with Bayesian Networks (BN) which incorporate expert experiences through lateral linkages among BN nodes and weighting factors. The system has been used to analyze and verify against three hydro-power systems at the Shihmen reservoir currently in operation. It was found that through BN analysis the fault trend is consistent to that from the Weibull distribution and FTA. This indicates that the transformation of a multi-state Fault-Tree (FT) into BN is reasonable and practical. Based upon the analysis of BN by inputting prior information of the hydro-power systems, the probabilities of fault occurrences and the sensitive factors are effectively computed. Proper preventive maintenance strategies can then be established based upon the BN outputs.

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