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Title: 類神經演算法應用於具後燃器渦輪風扇發動機之監控診斷
Neural Network Condition Monitoring and Fault Diagnosis of A Turbofan Engine with AfterBurner
Authors: 郭慶輝
Ching-Hui Kuo
Contributors: Jeu-Jiun Hu
資訊管理系碩士班
Keywords: 類神經網路;氣路分析法;監控診斷系統;最陡坡降法;動量項演算法;Levenberg-Marquardt演算法
Artificial Neural Network;gradient decent algorithm;momentum term algorithm;Levenberg-Marquardt algorithm
Date: 2010
Issue Date: 2011-05-26 11:18:06 (UTC+8)
Publisher: 高雄市:[樹德科技大學資訊管理系碩士班]
Abstract: 本研究係利用類神經演算法中之倒傳遞類神經網路,發展出一套具有故障診斷能力之監診系統應用於具後燃器渦輪風扇發動機。實驗數據來源係以具後燃器之渦輪風扇發動機半經驗試車數據,並經數據正規化後作為本研究之訓練及測試樣本。故障案例運用最徒坡降法、動量項法和LM演算法,分別對有限型及擴展型之監控系統進行發動機冷段及熱段建構故障診斷模型,並進行模型訓練及診斷測試,以建置完整之具後燃器渦輪風扇發動機冷、熱段之類神經監控診斷系統。
研究顯示,在有限型監控系統部份,網路架構以21個神經元之隱藏層並配合LM演算法,能取得最佳之誤差均方根,其程式之學習時間最短,且整體診斷率達90%以上;接著應用至擴展型監控系統部份,研究結果令人滿意。故倒傳遞類神經網路配合LM演算法所發展之監控診斷系統,用於診斷具後燃器渦輪風扇發動機之故障,能得到不錯之效果。
The purpose of this thesis is to develop a Neural Network Condition Monitoring and Fault Diagnosis system of a turbofan engine with afterburner. The semi-artificial sensing engine data are normalized and then feeding into the neural network. There are two model of our purposed system: 1. limited-model in which contends 4-node input and 5-node output parameters; 2.extented-model in which contends 6-node input and 7-node output parameters. By the using of gradient method, momentum term method and Levenberg Marquardt (LM) method, the results show excellent effectiveness and accuracy. This shows that the construction of purposed system can be used as a reference of the faultier diagnosis.
  As a result, in the case of limited-model, it shows smaller root mean square error in the network architetecture of a 21-node hidden layer neurons using LM algorithm and achieves 90% admeasure rate. In the case of extended-model, the network architetecture of the 25-node hidden layer neurons using LM algorithm can achieve 100% admeasure rate. Finally, the system is then applied for diagnosis of the turbofan engine with hot-section. The effectiveness of the proposed system is verified.
Appears in Collections:[資訊管理系(所)] 博碩士論文

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