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研究生(外文):LIN, HO-MIN
論文名稱(外文):Research of Marine Engine Failure Prediction Based on Random Forest Algorithm
指導教授(外文):GUO, JUN-LIANG
外文關鍵詞:predictive maintenancefailure predictionrandom forest classification algorithm
  • 被引用被引用:5
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隨著大數據的發展,在裝備失效預測上,使用隨機森林分類演算法進行預測及分類,透過這種模式可以檢測裝備失效狀況,從而可以節省因裝備失效、故障而導致的停機時間,本研究證明了隨機森林失效檢測方法的有效性和可靠性,該方法獲得 99% 的正確率,由此可得知,使用裝備的監控狀況運用於隨機森林演算法可來預測其裝備的失效狀況並在故障發生之前進行維修保養。
Due to the development of the shipping industry and the increased competition in the international shipping market, ship maintenance and repair is a topic that recently has been paid more attention, especially, the maintenance and repair of marine engines. With the development of technology, it is possible to create an optimized maintenance strategy, which is the predictive maintenance. This maintenance strategy is based on the condition of monitoring an equipment operation, to determine whether maintenance is required or not. This forecast can play a key role in improving the quality of maintenance and reduction of costs.
With the development of big data, the random forest classification algorithm is used for prediction and classification in equipment failure prediction. Through this mode, equipment failure status can be detected, which can save downtime caused by equipment failure and failure. The validity and reliability of the random forest failure detection method are proved. The method obtains 99% correct rate. It can be known that the monitoring condition of the equipment used in the random forest algorithm can predict the failure condition of the equipment and Perform maintenance before the fault occurs.

致謝 i
摘要 ii
Abstract iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1研究背景 1
1.2研究動機 5
1.3研究目的 7
1.4研究限制 7
1.5研究流程 7
第二章 文獻探討 10
2.1預測性維修 10
2.2失效預測 12
2.3不平衡資料集處理 13
2.4機器學習之分類演算法 14
2.5 隨機森林 18
2.6文獻小結 19
第三章 研究方法 20
3.1研究架構 20
3.2資料對象及前處理 22
3.3特徵選取及不平衡資料處理 24
3.4分類演算法 25
3.4.1決策樹 25
3.4.2支援向量機 26
3.4.3類神經網路 27
3.4.4隨機森林 27
3.5評估指標 32
第四章 模型評估與實驗設計 36
4.1實驗設計 36
4.2實驗一:探索性資料分析及建模結果 36
4.3實驗二:特徵選取分析及建模結果 41
4.4實驗三:不平衡處理分析及建模結果 43
4.5實驗四:綜合模型分析結果及評估 46
4.6整體分析結果及評估 48
第五章 結論與建議 50
5.1研究結論 50
5.2研究貢獻 51
5.3未來研究建議方向 52
參考文獻 55

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