本研究之主要目的在於改善且提昇台塑企業「變壓器油中氣體分析系統」之診斷能力，其中包括對變壓器油中氣體分析與異常診斷Laborelec數碼診斷法之修正、改採粗集合理論作爲知識擷取之方法及加入回饋機制三項作爲，以期強化異常診斷能力及改進對專家經驗規則知識的模糊擷取能力，進而成爲智慧型油浸式變壓器油中氣體分析與異常診斷系統。就成果而論，使實務存在約有45.1%無法明確歸屬故障類別之狀況，改善爲100%可被歸類；在提升專家經驗規則的擷取能力方面，則達到最佳規則正確率90.7%（信賴度CF=1，依賴度γ(下標 p)=90.8%）；最後，因回饋機制之採用，有利於動態學習與預測，不斷累積專家經驗規則而精進各種狀況研判之可靠性。

This study was aimed to improve the faults diagnosis capability of the dissolved gas analysis (DGA) system installed for the oil-filled transformers used in Formosa Plastics Corporation. Three attempts were made: the modification of Laborelec ratio codes criterion for achieving effective and accurate DGA and faults diagnosis, the adoption of rough set theory (RST) for improving knowledge mining capacity and the addition of feedback mechanism for enhancing the overall capacity of the entire system. Main achievements of this study included: (1) improving the crisp classification accuracy from 45.1% to 100%; (2) improving the capability of extracting and discovering experts' experience up to 90.7% (with CF=1 and γ (subscript p) =90.8%).