臺灣博碩士論文加值系統

為使電力系統正常運轉，汽力發電廠內設備初期故障的預防與偵測工作，在其中佔有相當重要的地位；特別是影響範圍頗為廣大的大型汽渦輪發電機組。在汽渦輪發電機組初期故障時，如未能及早偵測，將導致後期因嚴重故障衍生的不良結果。隨著汽渦輪發電機組容量之持續增加與構造日趨複雜，機組設備各部份零組件間之聯繫關係更為緊密，此現象雖然導致機組振動原因更加多樣化與複雜化，但振動狀態仍是判斷機組是否正常運轉之一重要指標。再者，汽渦輪發電機組可能同時出現多重故障症兆與多重故障種類，以目前設備診斷技術而言，多重故障診斷問題仍有待進一步解決。因此，汽渦輪發電機組振動監測與故障診斷方法不僅對電力系統安全穩定運轉與經濟效益具有重大意涵，同時亦是電機工程領域重要之研究課題。此外，汽渦輪發電機組振動故障診斷方法為一非常複雜之系統診斷問題其涵輔釵h領域且與現場經驗密切相關；所以，此一問題亦引起工程界相當多的重視。
汽渦輪發電機組振動故障具有多樣化特徵與可能發生多重故障，傳統的監督式類神經網路方法雖可針對單一故障進行有效診斷，但對於多重故障之診斷，則需對各種多重故障樣本進行廣泛學習。如此將使得類神經網路之訓練樣本變大，並增加網路之訓練時間。此外，多重故障樣本之學習可能會降低類神經網路之診斷準確性，使得故障診斷工作變得更加困難。因此，為解決機組振動故障診斷問題，本文提出應用粗糙集理論之方法於汽渦輪發電機組振動故障診斷問題，所提方法將從振動數據資料庫中進行診斷規則之萃取。粗糙集理論藉由所萃取之診斷規則直接描述振動數據資料庫所蘊藏之診斷知識。測試結果顯示，所提方法可從數據資料中萃取既簡單又有效之診斷規則，且本文證實所提方法應用於實際系統故障診斷之可行性。

From the view of preventive measures, the work on earlier detection of the incipient fault of the fundamental equipment in the steam power plants, especially the steam turbine-generator units, has attracted quite much attention. As a vital device in the power system, the fault of the steam turbine-generator unit will lead itself to a very wide range of outage of the system. Due to the increasing capacity and structure complexity of steam turbine-generator units, the relations among components of the unit become closer than before. These causes resulting in the machine vibration become more various and complicated, but the vibration signal is still a very important information to evaluate the operating conditions of the machine. Furthermore, the vibration fault diagnosis of steam turbine-generator is a complicated system diagnosis problem; thus, this problem draws much attention from engineering field. Moreover, multiple faults may simultaneously occur in the steam turbine-generator units, and the multiple fault diagnosis has not been solved appropriately. Thus, the research on vibration fault diagnosis not only has great importance and benefit for the machine to operate safely and stably, but also is a frontier issue for electrical engineering.
The vibration faults of the steam turbine generators have the various characteristics and multiple faults may occur simultaneously. Although the traditional supervised neural network can diagnose the single fault effectively, in multiple faults diagnosis the neural network must be trained by all sample of multiple faults, which will greatly increase the computer efforts of the learn process. Thus the fault diagnosis can not be easily performed. To solve the vibration fault diagnosis of units, this paper presents rough set theory based approach to extract diagnosis rules from data base for vibration fault diagnosis of steam turbine-generator units. The proposed rough set approach directly describes the discovered knowledge by the extracted rules. The tests results show that the proposed approach can extract simple and effective diagnosis rules from data base, and the feasibility of applying the method to practical system has been demonstrated.

中文摘要 IV
英文摘要 V
致 謝 VI
目 錄 VII
表目錄 VIII
圖目錄 VIII
第一章 緒論 1
1-1 研究動機 1
1-2 研究背景 2
1-3 研究目的 11
1-4 論文架構 12
第二章 問題描述 13
第三章 粗糙集理論 18
3-1 粗糙集理論 18
3-2 粗糙集應用實例 26
3-3 約簡演算法 32
3-3-1 Johnson演算法(Johnson Algorithm, JA) 32
3-3-2遺傳演算法(Genetic Algorithm, GA) 34
第四章 測試結果與討論 38
4-1 單一故障案例測試 38
4-2 多重故障案例測試 44
第五章 結論及未來研究方向 46
參考文獻 48

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