拜科技快速發展所賜，人工智慧被大量應用於各產業之際，第四次工業革命已悄然到臨，然素有工業之母美名的旋轉電機在電網與工廠中扮演著舉足輕重的角色，而預知保養策略係當今備受矚目的重要運維議題，遂基於模糊理論建構具故障診斷與狀態監測之旋轉電機專家系統，俾利於提升機組穩定運轉的可靠度。

　　本研究針對十組旋轉電機實驗模型進行電氣、振動與局部放電訊號分析，利用資料探勘技術挖掘潛藏於訊號中的異常徵兆，並根據不同成本與安全考量等因素，提出五種故障診斷與三種狀態監測系統解決方案。經實驗模型測試結果，電氣法以52 %的最高機率準確地推論轉子條斷裂故障，振動法以100 %的最高機率準確地推論軸承外環損傷與偏心故障，局部放電法以100 %的最高機率準確地推論定子絕緣故障。然而混合電氣與振動法可有效改善轉子條斷裂推論機率至83 %，再將局部放電法進行整合，可以更準確地辨識定子絕緣異常的類型，且有效地避免誤判的情形發生；而電氣、振動與局部放電之模糊狀態監測系統考慮了氣隙間距與故障因素等監測項目，強化現行以國際標準為主的狀態監測準則，但尚需在未來進行旋轉電機線上長期監測工作，並根據分析數據來調整推理法則與權重值，以期實質提升監測性能的可靠度。

　　總結本研究所提出的旋轉電機運維策略經實驗證實具可行性與有效性，期使能在國內產業發展進程中產生正面助益，以避免因旋轉電機異常而引發人員傷亡與經濟損失的嚴重負面影響。

　　Thanks to the rapid development of science and technology, artificial intelligence has been widely used in various industries, the fourth industrial revolution has quietly come, the rotating electrical machine is the mother of industry, plays a pivotal role in the power grid and plants, and predict maintenance strategy is now of concern important operation and maintenance issues, therefore, put forward a fuzzy theory-based fault diagnosis and condition monitoring systems for rotating electrical machine, which will help to enhance the reliability of the unit stable operation.

　　In this study, a total of ten rotating electrical machine for an experimental model to make electrical, vibration and partial discharge signal analysis, and the use of data mining technology in the mining potential of the signal fault symptoms, according to different considerations of cost and safety factors are proposed five kinds of fault diagnosis and three states monitoring system solutions. The experimental model test results, 52 % of the electrical method with the highest probability of accurately infer broken rotor bar fault, vibration method to 100 % of the highest probability of accurately infer the bearing outer ring damage and eccentric failure, partial discharge method with the highest probability of 100 % accurately infer the stator insulation failure. However, hybrid electric and vibration method can effectively improve the broken rotor bars inference probability to 83 %, and then integrate the partial discharge method, can be more accurately identification of stator insulation type of exception, and effectively avoid the possibility of misjudged, electrical, vibration and partial discharge of condition monitoring systems, taking into account relevant monitoring programs, such as the gap spacing and failure factors, and strengthening existing international standards-based condition monitoring guidelines, but in the future remains to be done to monitor the long-term operation of the rotating electrical machine, according to data analysis adjustment inference rule and the weight values to expect substantive enhance monitoring performance and operation reliability.

　　Concluding our proposed rotating electrical machine operation and maintenance strategy confirmed by experiments with the feasibility and effectiveness. We look forward to a positive helpful in the development process of the domestic industry, and further to avoid rotating motor abnormalities lead to serious negative impact on casualties and economic losses.

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