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基於XGBoost機器學習演算法建立船舶主機PHM模型

為了解決740li妥善率的問題，作者温慧菱 這樣論述：

工業4.0的時代，以物聯網和智慧系統為基礎核心，預測技術與智慧演算法交織，對裝備之主動維護產生積極的影響。柴油發動機的可靠性優化對船舶可用性、安全性和生命週期成本具有巨大之影響，現有的監視與警報系統僅針對基本運轉狀態偵測及警示故障，而預兆式健康管理模型的最終目標是可靠地偵測異常及預測故障時間，以便系統自主地進行有效的維護計畫。由於科技日新月益，武器裝備的集成度、複雜度及智慧化急劇增加，對資訊精準度與時效性的要求亦大幅升級，推動人們對於演算法的創新發展與運用，本研究採用透過系統優化和演算法增強的XGBoost機器學習演算法與隨機森林和支援向量機演算法進行預測模型的比較，實驗證明XGBoost可

顯著改進傳統維護方案，更加快速且準確地偵測船舶主機系統異常並預警，及時供維保人員與決策者於主機裝備維護之決策參考，藉由預兆式健康管理模型透過優化的預測方法，以提升補保系統適時適切地進行後勤作業，可使裝備整體生命週期的維運成本大幅降低，並提高裝備妥善率，未來可有助國軍進一步全面優化4M管理。

半導體晶片製造中電漿製程之失效偵測與診斷分類

為了解決740li妥善率的問題，作者楊仁淵 這樣論述：

The Transformer coupled plasma (TCP) reactors, which usually cost several millions of US dollars, have high capability of producing extremely tiny features and are often used in the semiconductor fabrication etching process. However, because of lacking real-time etching control, it often results in

some unacceptable process shifting and thus leads to lower yield of wafer. Besides, if the reactor is halted due to process faults, its productivity will be reduced. In order to maximize the product/wafer yield and tool productivity, a timely and effective fault process detection and classification

is required in a plasma reactor.Optical emission spectroscopy (OES) is one of the most frequency used metrology in in-situ process monitoring. An OES is a non-invasive system and can measure the variation of the optical emission intensity of plasma, which can be used to monitor the etching rate, un

iformity, selectivity, critical dimensions, and even the profile of etching features on a wafer. However, an OES may provide a huge amount of information such that the data cannot be analyzed timely. As a result, a real-time fault detection and classification with rapid algorithm is needed.This stud

y proposes two novel methods for fault process detection and one new method for fault classification. The first fault process detection method adapts the technique of digital image process skill and applies the time series of OES full spectrum intensity, which can be transferred into a binary image.

By comparing the image patterns of the process conditions between the incoming test and the normal process, the fault process condition in each recipe step can be found by calculating the difference by each pixel. The second fault process detection method uses statistical skill to build up a health

y process sigma model, utilized to compare the uncertainly process OES data and to generate a match rate indicator, a maker to show whether the process is normal or not. The experiments were conducted and the results showed that this proposal methods can detect the fault process in real-time with hi

gh successful rate. Finally, a fault process classification method is proposed using the match rate concept to identify the fault process type. The match rate generated by OES data is transferred into twelve different match rates, by spectrum bands, which are used to build up the models of fault cau

ses. Comparing the test data to the constructed models, the probability indexes (PI) for the fault causes are generated, from which the highest value of PI is regarded as the fault cause. A real-time classification of plasma faults is thus achieved. Experiments were conducted to validate the novel f

ault classification. From the experimental results, it concludes that the proposed method is feasible providing that the overall accuracy rate of the classification for fault event shifts is 27 out of 28 or about 96.4% in success.