臺灣博碩士論文加值系統

本篇論文包含下列的兩大主題，分別是：(a)主題一：主成分分析法(Principal Component Analysis, PCA)的研究，及(b)主題二：將主成分分析法應用於分析直流馬達的電流訊號。其中主題一的主成分分析法(PCA)，它的主要功能是將資料簡化(data reduction)或維度化簡(dimension reduction)。PCA的分析過程簡述如下：(1)分析變數之間的相關性，目的是要找出這些變數之間的相關性；(2)依據分析的結果來減少變數個數或是產生另一組數量較原變數個數少的新變數。簡言之，PCA首先是從原始變數間找到它們的相依關係，而後是保留影響程度較大的變數，而去除影響程度較小的變數。如此，PCA不但達到減少變數個數的目的，也同時保留大部分的訊息。其中的主題二，是將PCA法應用於分析直流馬達(DC motor)的電流訊號。本主題二分成如下的四個部份，分別是：(a)定義馬達電流訊號之測試點；(b)尋找與儲存馬達電流訊號的測試點；(c)以PCA法選取各種馬達品質類別的主要成分；(d)各種故障馬達品質類別的辨識。本文能辨識五種的馬達品質類別，包含好的馬達品質類別(Type-good)及四種故障馬達品質類別(Type error-1~Type error-4)。經多次實驗，正確辨識率(TCA)Type error-1為91.66%，Type error-2為86.66%，Type error-3為84.61%，Type error-4為81.81%，Type-good為94.73%，平均正確辨識率為88.57%。

This study consists of two main topics: (a) Topic 1: The research on Principal Component Analysis (PCA), and (b) Topic 2: Using PCA for analyzing DC motors current signal. The principal component analysis is utilized for data reduction or dimension reduction. The process of PCA is summarized as follows: (1) To find the correlation between these variables by analyzing the correlation between variables and ; (2) According to the analyzed results, one can reduce the number of variables or generate another group which the number of new variables is smaller than the number of original variables. In other words, the PCA firstly finds dependencies of these original variables, and then retains the variables that have a greater impact and removes the less-affected variables. Therefore, PCA not only achieves the purpose of reducing the number of variables, but also retains most of the information. The second topic is to apply the PCA method to analyze the current signals of DC motors. This topic is divided into two parts: (a) Defining test points for motor current signals; (b) Finding and storing test points for motor current signals; (c) Selecting principal components of various motor quality classes using the PCA method; (d) Determing motor quality types. This study identifies five types of motor quality, including Type-good and Type error-1 to Type error-4. After lots of experiments, the results show that the total classification accuracies (TCAs) are 91.66%，86.66%，84.61%，81.81%，and 94.73% for Type error-1, Type error-2, Type error-3, Type error-4, and Type-good, respectively. The average TCA is 88.57%.

目　　錄
摘要-----------------------------------------------------------------ii
Abstract-------------------------------------------------------------iii
致謝-----------------------------------------------------------------iv
目錄------------------------------------------------------------------v
表目錄---------------------------------------------------------------vi
圖目錄--------------------------------------------------------------vii
第一章 前言-----------------------------------------------------------1
第二章 主成分分析法的回顧---------------------------------------------3
2.1 簡介--------------------------------------------------------------3
2.2基本理論----------------------------------------------------------4
2.3主成分分析法的計算步驟--------------------------------------------5
第三章 主成分分析法的應用--------------------------------------------10
3.1 訊號分析器的系統架構---------------------------------------------10
3.2主要成分的選取 (Principal Component Selection) -------------------11
3.2.1 馬達電流訊號之測試點的定義-------------------------------------11
3.2.2擷取與尋找馬達電流訊號的測試點----------------------------------13
3.2.3各種馬達品質類別之主成分的選取---------------------------------18第四章 馬達品質類別的辨識--------------------------------------------19
4.1故障馬達品質類別Type error-1的辨識-------------------------------19
4.2故障馬達品質類別Type error-2的辨識-------------------------------22
4.3故障馬達品質類別Type error-3的辨識-------------------------------26
4.4故障馬達品質類別Type error-4的辨識-------------------------------29
第五章 實驗與性能評估------------------------------------------------32
5.1實驗一：評估辨識單一週期訊號的能力--------------------------------32
5.1.1故障馬達品質類別Type error-1------------------------------------33
5.1.2故障馬達品質類別Type error-2------------------------------------34
5.1.3故障馬達品質類別Type error-3------------------------------------35
5.1.4故障馬達品質類別Type error-4------------------------------------36
5.1.5良好馬達品質類別Type-good--------------------------------------37
5.2實驗二：評估整體的正確辨識率--------------------------------------39第六章 結論----------------------------------------------------------41參考文獻-------------------------------------------------------------42
簡歷-----------------------------------------------------------------43
表目錄
表3-1：馬達電流訊號中10個測試點的定義---------------------------------13
表3.1：陣列Table-all中的存放格式------------------------------------17
表3.2：演算法Procedure-PCA所決定的主要成分--------------------------18
表4.1：Type error-1的門檻值------------------------------------------21
表4.2：Type error-2的門檻值------------------------------------------24
表4.3：Type error-3的門檻值------------------------------------------27
表4.4：Type error-4的門檻值------------------------------------------30
表5.1：待測試的馬達個數 ---------------------------------------------32
表5.1：Type error-1的門檻值與本實驗的量測值--------------------------34
表5.2：Type error-2的門檻值與本實驗的量測值--------------------------35
表5.3：Type error-3的門檻值與本實驗的量測值--------------------------36
表5.4：Type error-4的門檻值與本實驗的量測值--------------------------37
表5.2：實驗二的辨識結果----------------------------------------------40




圖目錄
圖2.1：演算法Procedure-PCA的流程圖[13] -----------------------------9
圖3.1：本篇論文所提之馬達電流訊號分析器的系統架構圖------------------11
圖3.2：馬達電流訊號中十個測試點point 1至point 10的定義[13,14]------12
圖3.3：Procedure-Fetch演算法的流程圖---------------------------------14
圖3.4：演算法Procedure-Pole的流程圖----------------------------------15
圖3.5：演算法Procedure-up-down的流程圖------------------------------16
圖3.6：演算法Procedure-All的流程圖----------------------------------17
圖4.1：品質類別Type error-1的馬達電流波形圖-------------------------21
圖4.2：演算法Procedure-Type error-1的流程圖-------------------------22
圖4.3：品質類別Type error-2的馬達電流波形圖-------------------------25
圖4.4：演算法Procedure-Type error-2的流程圖-------------------------25
圖4.5：品質類別Type error-3的馬達電流波形圖-------------------------27
圖4.6：演算法Procedure-Type error-3的流程圖-------------------------28
圖4.7：Type error-4的馬達電流波形圖----------------------------------31
圖4.8：演算法Procedure-Type error-4的流程---------------------------31
圖5.1：品質類別Type error-1的馬達電流波形圖-------------------------33
圖5.2：品質類別Type error-2的馬達電流波形圖-------------------------34
圖5.3：品質類別Type error-3的馬達電流波形圖-------------------------36
圖5.4：Type error-4的馬達電流波形圖----------------------------------37
圖5.5：品質類別Type-good的馬達電流波形圖----------------------------38
圖5.6：品質類別Type-good的辨識過程流程圖----------------------------39

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