基於多感測器訊號與機器學習技術之刀具磨耗分類與預測

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研究生：	葉幸哲 Xing-Zhe Ye
論文名稱：	基於多感測器訊號與機器學習技術之刀具磨耗分類與預測 Tool Wear Classification and Prediction based on Multiple Sensors and Machine Learning Techniques
指導教授：	劉孟昆 Meng-Kun Liu
口試委員:	藍振洋 Chen-Yang Lan 劉益宏 Yi-Hung Liu
學位類別：	碩士 Master
系所名稱：	工程學院 - 機械工程系 Department of Mechanical Engineering
論文出版年：	2022
畢業學年度：	110
語文別：	中文
論文頁數：	107
中文關鍵詞：	刀具磨耗、特徵指標、特徵提取、特徵選擇、特徵降維、迴歸模型、分類模型、共線性診斷、支持向量機、隨機森林迴歸
外文關鍵詞：	Tool wear, features indexes, feature extraction, feature selection, feature dimensionality reduction, regression model, classification model, collinearity diagnosis, support vector machine, random forest regression
相關次數：	點閱：678 下載：0
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摘要    I
Abstract    II
誌謝    IV
目錄    V
表目錄    VIII
圖目錄    X
第一章、緒論    1
1 研究背景與動機    1
2 論文架構    3
第二章、文獻回顧    4
1 刀具磨耗    4
2 特徵處理    5
2.1 特徵前處理    5
2.2 特徵提取    5
2.3 特徵選擇    6
3 機器學習    7
3.1 機器學習之分類    8
3.2 迴歸分析與預測    10
第三章、研究方法    11
1 實驗規劃    11
1.1 實驗設備與設置    11
1.2 刀具磨耗實驗    15
1.3 磨耗結果    17
1.4 實驗流程    18
2 特徵提取    20
2.1 時域特徵    20
2.2 頻域特徵    23
3 特徵選擇    24
3.1 變異數分析(analysis of variance, ANOVA)    24
3.2 皮爾森相關係數(Pearson correlation coefficient, PCC)    25
4 特徵降維    26
4.1 線性判別分析(Linear discriminant analysis, LDA)    26
4.2 主成分分析(principal component analysis, PCA)    27
5 共線性診斷    28
5.1 方差膨脹因子(variance inflation factor, VIF)    28
5.2 迴歸模型正規化    29
6 支持向量機(support vector machine, SVM)    31
7 隨機森林迴歸    35
第四章、分類結果分析    37
1 分類分析流程    37
1.1 無獨立特徵集之分析流程    38
1.2 有獨立特徵集之分析流程    40
2 ANOVA特徵選擇    42
2.1 ANOVA (無獨立特徵集)    42
2.2 ANOVA (有獨立特徵集)    45
3 特徵降維    48
3.1 PCA降維分析(無獨立特徵集)    48
3.2 PCA降維分析(有獨立特徵集)    49
3.3 LDA降維分析    50
4 分類結果    51
第五章、迴歸分析    55
1 迴歸分析流程    55
2 迴歸模型    57
3 線性迴歸模型分析    58
3.1 皮爾森相關係數(PCC)    58
3.2 VIF迴歸分析    60
3.3 LASSO迴歸分析    62
3.4 PCR分析    63
2.5 線性迴歸分析總結    65
4 非線性迴歸模型分析    66
4.1 VIF迴歸分析    66
4.2 LASSO迴歸分析    67
4.3 PCR分析    67
4.4 非線性迴歸分析總結    68
5 迴歸模型與機器學習模型比較    71
第六章、結果討論與未來展望    65
1 分類分析結果討論    65
2 迴歸分析結果討論    66
3 未來展望    68
第七章、參考文獻    69
附錄A、刀具磨耗圖    73
附錄B、各實驗皮爾森相關係數    74
附錄C、各實驗主成分累積貢獻比    77
附錄D、各實驗共線性診斷後平均刀具磨耗預測曲線    80
附錄E、混合特徵集特徵處理後結果    83




                                

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全文公開日期 2027/08/09 (校內網路)
全文公開日期 2027/08/09 (校外網路)
全文公開日期 2027/08/09 (國家圖書館：臺灣博碩士論文系統)

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