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研究生:謝男凱
研究生(外文):Nan-Kai Hsieh
論文名稱:工具機主軸品質診斷系統開發
論文名稱(外文):Development of Fault Diagnosis System For High Speed Spindle
指導教授:楊宏智楊宏智引用關係
口試委員:林清安陳湘鳳李貫銘許巍耀林威延
口試日期:2015-07-15
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:機械工程學研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:108
中文關鍵詞:高速主軸振動訊號傅立葉轉換多尺度熵損壞辨識
外文關鍵詞:High Speed SpindleVibration SignalFourier TransformMulti-scale entropyDefect Diagnosis System
相關次數:
  • 被引用被引用:1
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  • 收藏至我的研究室書目清單書目收藏:1
隨著自動化加工技術的蓬勃發展,加工設備亦延長運作時間。當設備長時間運作時,所產生之振動與噪音,勢必對設備造成磨損與故障等狀況發生,進而影響生產線的稼動率。目前受限於檢測方法與設備能力,往往無法預知故障之發生。假若能有效偵測故障即將發生,便能將設備故障所帶來之損失降之最低。因此,近年來許多智能監控與無人化工廠廣泛運用於製造業之產線上。本研究中,乃是根據被喻為工具機心臟的高速主軸,進行智能化高速主軸系統的開發。首先,透過製程管系統(Manufacturing Execution System, MES)收集了高速主軸之生產與維修資訊,且整理出常見之故障原因。再利用定壓預壓主軸,模擬出每種損壞狀況下之振動情形,並收集三軸向之振動訊號。常見之高速主軸檢測方法,乃是將振動訊號經由傅立葉轉換,再透過頻譜圖觀察不同頻率下之振幅值;本文並加入多尺度熵做為損壞辨識方法,藉由計算訊號在各尺度下的亂度值,做為判斷方式。
本研究最後透過重複性實驗擷取損壞模型之振動訊號,並搭配演算法找出各損壞模型之閥值。將各損壞模型之閥值彙整後,匯入所開發之損壞辨識系統(Defect Diagnosis System),此套系統能針對不同之損壞狀況,以即時訊息之方式告知操作人員,並將系統實際應用於高速主軸進行跑合前之故障檢測。其主要目的為,高速主軸可透過跑合前的篩檢,預先檢測出原先振動計所無法辨識出之潛在故障因子,有效防止主軸在跑合過程中發生軸承損毀之狀況。目前損壞辨識系統已能實際應用於生產線上,做為產品品管之工具,且辨識準確率已能達9成以上。


During the long machining cycles, the vibration and accompanied noise produced by the machine tool may cause unduly mechanical wear and, consequently malfunction. The inadequate facility limits an effective malfunction pre-warning diagnosis and this often leads to the sudden failure of the spindle. Therefore, it is necessary to rely on the real-time monitor system to enhance the reliability of the machine tool. This research collects the information in the process of production and maintenance, from which the common faults are summarized, and then a constant-preload spindle is used to collect vibration signals and simulate each failure mode. This research also proposes the multi-scale entropy (MSE), in addition to the most common detection of vibration signals, the Fourier analysis. It is used to observe the amplitude of each frequency at each instant. The MSE can be used to calculate the MSE curve which can then be used to correctly identify some defect modes. In order to prevent the failure without warning, this research is designed to extract the threshold of each fault condition by repetitive experiments to develop the defect diagnosis system (DDS).
The main objective of the DDS is applied to detect the faults before the run-in period of spindle so that any potential errors can be identified and recognized. These potential damaging factors are detected from the spindle to avoid breaking the bearing during the run-in period. At present, the DDS is applied successfully in the production line for quality control. and the recognition rate of DDS is high up to 90 percent
.

第一章 緒論 1
1.1 研究動機 1
1.2 文獻回顧 2
1.3 研究背景 5
1.4 研究架構 6
第二章 高速主軸損壞資料統計 7
2.1 退修原因統計 7
2.2 損壞資料統計 8
2.2.1 軸承損壞位置統計 10
2.2.2 維修過程 11
2.3 製程管制系統 12
2.3.1 使用壽命統計 12
2.3.2 各年度之主軸退修數目 15
2.3.3 各年度在保固期範圍內之主軸 16
2.3.4 各年度出廠之主軸壽命評估 18
2.4 小結 19
第三章 常見之振動問題與損壞特徵 20
3.1 常見之故障原因 20
3.2 常見之損壞模型 22
3.2.1 動平衡不良 22
3.2.2 偏心 24
3.2.3 潤滑油之影響 25
3.2.4 軸承預壓值的大/小 26
3.2.5 軸承損壞 27
3.3 小結 29
第四章 人造損壞模型之定義 30
4.1 動平衡不良之損壞模型 30
4.2 偏心之損壞模型 33
4.3 油脂潤滑失當之損壞模型 34
4.4 預壓失當之損壞模型 35
4.4.1. 定壓預壓調整機構 35
4.5 間格環不平行之損壞模型 38
4.6 高速主軸檢測流程 39
4.7 振動訊號分析過程 44
4.7.1. 振動訊號擷取方式之探討 44
4.7.2. 不同轉速下之探討 44
4.8 小結 47
第五章 溫昇與振動訊號擷取設備 48
5.1 振動資料擷取模組 48
5.1.1 感測器特性簡介 49
5.1.2 資料擷取卡簡介 53
5.1.3 LabView開發 54
5.2 溫昇資料擷取模組 57
5.2.1 溫昇資料擷取硬體裝置 57
5.2.2 溫昇資料擷取軟體裝置 59
5.3 實驗設備 62
5.3.1 損壞模型樣本數 63
5.3.2 實驗環境測試 63
5.4 小結 65
第六章 以傅立葉轉換為基礎之特徵擷取及比對演算法 66
6.1 傅立葉轉換於旋轉機械之相關研究 66
6.2 傅立葉轉換 71
6.3 各損壞模型之傅立葉分析 71
6.3.1 合格主軸之傅立葉分析 71
6.3.2 動不平衡主軸之傅立葉分析 73
6.3.3 偏心主軸之傅立葉分析 76
6.4 小結 78
第七章 以多尺度熵為基礎之特徵擷取及比對演算法與損壞辨識系統 79
7.1 多尺度熵簡介 79
7.2 多尺度熵演算法 80
7.3 多尺度熵實驗分析結果 81
7.3.1 預壓大小之分析 81
7.3.2 油脂多寡之分析 85
7.3.3 組裝瑕疵之分析 87
7.4 損壞辨識系統開發 89
7.4.1 損壞特徵建立 89
7.4.2 損壞系統辨識流程 89
7.4.3 損壞辨識系統操作流程實測 94
7.4.4 生產線實測 94
7.5 小結 96
第八章 結論與未來展望 98
8.1 結論 98
8.2 未來展望 99
8.2.1 加強損壞辨識之能力 99
8.2.2 系統的整合與資料庫之完整性 100
參考文獻 102



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