臺灣博碩士論文加值系統

機械製造引起的振動會導致產品品質發生不可預測的變化，從而增加製造成本。目前，一些研究人員正致力於建立正常的振動係數或準確高效的生產機制。因此，衍生出各種健康診斷方法和回饋機制，如動態特徵偵測、機床狀態檢測、切削顫振分析、機床內部特定部件的健康狀態反饋等。
實驗中採用程泰GA-3300車床，藉由不同夾持狀態所產生的振動訊號，建立了可用於數控車床或傳統機床的振動分析和健康診斷方法。在轉速達到2000轉之前，於主軸轉速250轉/分的每個區間和三種夾持狀態（未夾持工件、有夾持工件未偏心、有夾持工件且偏心）皆進行各五組實驗，數控車床可以幫助精確控制主軸速度。並且，利用智能預測診斷性能系統獲取振動訊號，進行頻域分析，並與均方根結果匹配，檢查得到的振動訊號數據和振動特徵值的正確性。然後，使用軟體工具根據過濾後的異常值和特徵提取的匹配結果對訊號進行過濾。我們使用主成份分析（PCA）方法評估了37個特徵，並使用投影矩陣對數據進行降維以獲得不同轉速和每種夾持狀態下的振動數據分佈圖，確實可辨別出各夾持狀態下的分佈圖的分佈範圍緊密度狀態。
本次實驗中，將收集每250 rpm間隔和三種夾持狀態的振動訊號數據，並在三種狀態之間進行了比較(每組實驗重複5 次)，確定獲得的振動訊號數據是正確且可重複的。結果證實，在數控車床中，使用PCA方法建立主成份分佈圖確定夾持狀態下的振動值變化，並基於振動變化機制建立健康狀態反饋數據集是可行的。

The vibration caused by mechanical manufacturing will lead to unpredictable changes in product quality, which will increase the manufacturing cost. Plenty of research is imposed to establish a normal vibration coefficient or to develop an accurate and efficient production process. Therefore, various health diagnosis methods and feedback mechanisms are obtained, such as dynamic feature Detection, machine tool state Detection, cutting chatter analysis, health state-feedback of specific parts in the machine tool and so on.
This study analyzes the lathe and establishes the vibration analysis and health diagnosis method used for NC lathe or traditional machine tools through the vibration signals generated by different clamping states. Before the spindle speed reaches 2000 rpm, 5-experiments at an interval of 250- rpm and three clamping states are executed. The NC lathe can help accurately control the spindle speed. Moreover, the vibration signal is obtained using the intelligent prediction and diagnosis performance system, analyzed in the frequency domain, matched with the root mean square result, and checked for the accuracy of the vibration signal data and vibration eigenvalues. Then, software tools are used to filter the signal according to the filtered outliers and the matching results of feature extraction. Principal component analysis (PCA) and the projection matrix are used to evaluate 37 features and reduce the dimension of the data, to obtain the vibration data distribution map under different rotating speeds of each clamping state. It can indeed identify the distribution range and tightness of the distribution map under each clamping state.
In the above experiment, the signal data of each 250-rpm interval and three clamping states were collected and compared. The data obtained are correct and repeatable. The results show that it is feasible to use the PCA method to determine the change of vibration value in the NC lathe and establish health state feedback data set based on the vibration change mechanism.

摘要.....i
Abstract.....ii
誌謝.....iii
目錄.....iv
表目錄.....vi
圖目錄.....vii
符號說明.....x
第一章 緒論.....1
1.1研究背景.....1
1.2研究目的.....1
1.3文獻探討.....1
1.4論文架構.....3
第二章 實驗基礎理論介紹.....5
2.1振動的基礎理論.....5
2.1.1單自由度系統.....5
2.2加速規基本原理.....7
2.3振動訊號分析重要名詞.....8
2.4濾離群值.....10
2.5卡爾曼濾波（Kalman Filter）.....10
2.6 Z-Score.....13
2.7主成份分析(Principal Components Analysis,PCA).....13
第三章 實驗設備與軟體介紹.....17
3.1 實驗設備與設備規格介紹.....17
3.2 智慧預測與診斷系統(Prediction Diagnosis Performance System).....18
3.3 加速規感測器.....19
3.4 溫度感測器.....21
3.5 電流感測器.....22
3.6 主軸產生偏心鎖固螺絲孔.....23
3.7 訊號擷取軟體.....24
3.8 訊號分析軟體.....25
第四章 實驗規劃及介紹.....26
4.1 實驗設備安裝介紹.....26
4.1.1 主軸偏心治具安裝.....27
4.2 振動訊號擷取.....28
4.3 實驗參數制定.....29
4.4 數據收集及分析.....30
4.4.1 振動數據提取.....31
4.4.2 數據濾離群值及數據切割.....32
4.4.3 特徵萃取.....34
4.4.4 主成份分析(Principal Components Analysis).....36
第五章 結果與討論.....37
5.1振動訊號頻域分析.....37
5.2同轉速、各夾持訊號RMS比較分析.....41
5.3不同夾持狀態振動訊號PCA主成份分析結果.....46
5.3.1 未夾持工件狀態振動訊號PCA分析比較.....46
5.3.2 有夾持工件未偏心狀態振動訊號PCA分析比較.....50
5.3.3 有夾持工件且偏心狀態振動訊號PCA分析比較.....54
5.4同轉速、不同夾持狀態主成份分析法PCA分佈圖結合比較.....58
5.5建立健康診斷模型及模擬診斷.....62
第六章 結論與未來展望.....65
6.1結論.....65
6.2未來展望.....66
參考文獻.....66
Extended Abstract.....69
Abstract.....69
1.Introduction.....70
2.Experiment setup.....72
3.Principal component analysis.....74
4.Comparative Analysis of Experimental Results.....75
5.Health Predictive Diagnostic Model.....75
6.Conclusions.....78
Reference.....78

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