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系統識別號 U0026-2407202016290600
論文名稱(中文) 運用非監督式深度學習方法分析滾珠螺桿傳動系統性能之智慧化診斷
論文名稱(英文) Intellectual Diagnosis for the Performances of Ball Screw System by Using the Unsupervised Deep Learning Method
校院名稱 成功大學
系所名稱(中) 機械工程學系
系所名稱(英) Department of Mechanical Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 陳冠宇
研究生(英文) Guan-Yu Chen
學號 N16074302
學位類別 碩士
語文別 中文
論文頁數 144頁
口試委員 指導教授-林仁輝
口試委員-黃逸羣
口試委員-魏進忠
口試委員-李榮宗
口試委員-韓長富
中文關鍵字 滾珠螺桿磨潤性能分析  深度學習  長短期記憶編碼器 
英文關鍵字 Analysis of Ball Screw Lubrication Performance  Deep Learning  Long Short-Term Memory Encoder Decoder. 
學科別分類
中文摘要 工作機台在運作時其工作效率會隨時間而慢慢衰退,所以針對機台工作效率的診斷方法非常重要,現今的診斷方法大多只能區分出正常與損壞的運作狀態。本研究運用智慧化之非監督式深度學習方法進行機台工作狀態診斷,主要目的有二:發現滾珠螺桿隨著時間變化之性能衰退,在滾珠螺桿出現性能衰退至門檻值之前給予及時補油修正與補償。由於機器性能衰退難以用人工方式清楚判別,本研究使用了長短期記憶網路編碼器(LSTM-ED),將滾珠螺桿的振動訊號代入非監督式深度學習網路中,藉由神經網路的資料辨別能力,得到滾珠螺桿在四次長時間重負載運轉下健康指數(Health index)隨運行距離的變化。
本研究分析滾珠螺桿的訊號,是加速規量測上螺帽部位的振動訊號。潤滑條件為試驗開始前注脂12c.c.,且連續運行到150公里前都沒有再注脂。為了要得到滾珠螺桿的健康指數,我們依下列步驟處理:首先是振動訊號前處理(Preprocess),包含等速段訊號擷取與正規化(Normalization)、零平均處理(Zero-mean process)等。接下來代入深度學習模型做健康診斷,藉由模型的重構誤差得到滾珠螺桿性能衰退曲線,也就是健康指數的變化。除了討論健康指數的下降趨勢外,還有三個方法進行結果分析,找出主導健康指數變化的因素:首先,在試
驗前後都有量測滾珠螺桿的預壓扭矩,利用預壓扭矩與健康指數變化作圖,訂定健康指數起始與結束的標準。第二,利用快速傅立葉轉換,觀察球通頻率及各元件的缺陷頻率判定哪些接觸配對為影響整體系統健康指數的主導因子。最後利用經驗模態分解法將原始訊號由高頻拆分至低頻,將主導磨耗及振動行為之高頻訊號單獨代入模型運算,分析高頻訊號隨運行距離之健康指數變化,用來對照整體健康指數的變化,瞭解高頻振動訊號對整體健康指數的影響。
健康指數能顯示滾珠螺桿在尚未發生機件損壞前的性能衰退現象,尤其能對滾珠螺桿的磨潤狀況有不錯的分析評估能力。而高頻磨耗對整體健康指數的影響也會隨運行距離有不同的變化。本研究也將健康指數量化分析滾珠螺桿的磨潤狀態,發現隨著運行距離的增加,滾珠螺桿的效能衰退速度有變快的趨勢。由於使用的是非監督式的深度學習模型,在處理複雜的振動訊號時,模型能自行學習找出判斷性能的標準,因此能應用於各種振動訊號的分析。使用本研究成果,在機台性能(健康指數)下降至門檻值之前便給予及時的補償與補充潤油,達到智慧化診斷及省時省力的目的。
英文摘要 The efficiency of the machine will degrade with the increase in working time. It is important to diagnose the working state of the machine. This study focuses on intellectual diagnosis for the performance of ball screw system by using an unsupervised deep learning method. There are two purposes of this study:first, figure out the performance change of ball screw with working time, then giving instant lubrication. Because the degradation of ball screw system is difficult to figure out by manual methods. This study can show the degradation by using a unsupervised deep learning method named:Long Short-Term Memory Encoder Decoder (LSTM-ED). With the data analysis ability of the neural network, we can obtain Health Index (HI) of ball screw which shown the degradation while ball screw was working.
This study quantified the health index with an exponential function. We found that the performance declined faster as the accumulated running distance increase. Also, we decomposed raw data into several Intrinsic Mode Functions (IMFs) by using Empirical Mode Decomposition (EMD) and then trained model with each IMF. Finally, we can obtain HI of each frequency region. The health index has a great ability to display the lubrication status of ball screw. We can combine preload torque with health index to explain the overall situation. With the result of this study, we can compensate for machine lubrication immediately before the damage happened.
論文目次 目錄
摘要 I
Extended Abstract III
致謝 VI
目錄 VII
表目錄 XII
圖目錄 XIII
第一章 緒論 1
1-1 前言 1
1-2 文獻回顧 2
1-3 研究目的 5
1-4 論文架構 6
第二章 基本理論與程式流程 8
2-1 數位訊號處理 8
2-1-1 快速傅立葉轉換(Fast Fourier Transform, FFT) 8
2-1-1-1 傅立葉變換及離散傅立葉變換 8
2-1-1-2 快速傅立葉轉換理論 10
2-1-1-3 快速傅立葉轉換處理流程與限制條件 11
2-1-2 經驗模態分解(Empirical Mode Decomposition) 13
2-1-3 特徵頻率(Characteristic Frequency) 15
2-1-3-1 轉軸頻率(Shaft Rotating Frequency) 15
2-1-3-2 滾珠螺桿各元件特徵頻率分析 16
2-1-3-3 球通頻率(Ball Passing Frequency) 18
2-1-3-4 倍頻與頻率調變關係 20
2-2 機器學習基礎理論 21
2-2-1 機器學習理論 21
2-2-2 機器學習流程 23
2-2-3 人工神經網路(Artificial Neural Network, ANN) 25
2-2-3-1 梯度下降法(Gradient Descent Method) 27
2-2-3-2 反向傳播法(Back Propagation) 29
2-3 深度學習(Deep Learning) 31
2-3-1 長短期記憶網路(Long Short-Term Memory Network) 32
2-3-2 自編碼器(Autoencoder) 37
2-3-3 長短期記憶編碼器(Long Short-Term Memory Encoder-Decoder, LSTM-ED) 39
2-4 程式設計流程 41
2-4-1 長短期記憶神經網路訓練流程設計 41
2-4-2 健康指數衰退程度量化分析方法 43
2-4-3 健康指數變化分析與程式設計方式 44
2-4-3-1 接觸元件頻率分析程式設計方式 44
2-4-3-2 高頻磨耗健康指數變化之程式設計方式 46
第三章 試驗方法及資料擷取 67
3-1 試驗設備 67
3-1-1 試驗機台簡介 67
3-1-2 量測儀器簡介 69
3-1-2-1 加速規(Accelerometers) 69
3-1-2-2 扭力計(Torque meter) 70
3-1-2-3 推拉力計(Force gauge) 70
3-2 試驗步驟 71
3-2-1 試驗條件與參數 71
3-2-2 加速規及扭矩訊號之擷取 72
第四章 結果與討論 84
4-1 訊號前處理結果 84
4-1-1 訊號前處理結果 84
4-2 運用長短期記憶編碼器對滾珠螺桿性能衰退分析 85
4-2-1 長短期記憶編碼器超參數設置 85
4-2-2 長短期記憶編碼器分析結果 87
4-2-3 健康指數衰退量化分析(Quantitative analyses for the early degradation of health index) 89
4-3 滾珠螺桿部件特徵頻率計算分析結果 90
4-3-1 球通頻率計算與分析 91
4-3-2 缺陷頻率計算與分析 94
4-3-2-1 缺陷頻率計算 94
4-3-2-2 缺陷頻率出現次數分析 96
4-3-2-3 缺陷頻率振動峰值變化分析 97
4-4 高頻磨耗對滾珠螺桿性能衰退分析 100
4-4-1 高頻磨耗之模型訓練與健康指數分析 100
4-4-2 高頻健康指數與整體健康指數映射分析 102
4-5 健康指數與預壓扭矩分析 104
第五章 結果討論與未來發展 135
5-1 結論 135
5-2 未來展望 136
參考資料 138

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