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作者(中文):黃柏文
作者(外文):Huang, Po-Wen
論文名稱(中文):應用微機電三軸加速計振動感測模組開發滾動軸承健康診斷預測系統
論文名稱(外文):Development of Rolling Bearing Health Diagnosis and Prediction System Using MEMS Three-Axis Accelerometer Vibration Sensing Module
指導教授(中文):李昇憲
指導教授(外文):Li, Sheng-Shian
口試委員(中文):宋振國
丁川康
白明憲
口試委員(外文):Sung, Cheng-Kuo
Ting, Chuan-Kang
Bai, Ming-Sian
學位類別:碩士
校院名稱:國立清華大學
系所名稱:智慧生產與智能馬達電控產業碩士專班
學號:108134501
出版年(民國):110
畢業學年度:109
語文別:中文
論文頁數:89
中文關鍵詞:壓電式微機電加速度計馬達滾動軸承健康診斷總體型經驗模態分解包絡譜分析類神經網路
外文關鍵詞:Piezoelectric MEMS AccelerometerMotor Rolling Bearing Health DiagnosisEnsemble Empirical Mode DecompositionEnvelope AnalysisArtificial Neural Network
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本研究工作為應用本團隊開發之高頻寬、高性價比之模組化壓電式微機電三軸加速計於工具機馬達滾動軸承健康診斷預測系統之開發。在工業4.0年代,工具機的智慧控制與維護逐漸受到重視,其中轉動軸承為眾多工具機械不可或缺的一部分,軸承損壞造成的故障在機械故障原因統計中占比高達80%,故本論文將研究目標聚焦在馬達轉動軸承損害的分析診斷上。
本研究主要希望開發一套結合神經網路的預處理流程來達成最佳軸承健康辨識效果,我們將三種不同健康狀態的軸承依500、1,000、1,500 rpm轉速的設計收集資料,應用總體型經驗模態分解法對數據進行本質模態函數的分離,最終選擇IMF3作為包絡譜分析的目標,並且搭配類神經網路對該包絡譜特徵進行學習預測。經過模型驗證後,該方法在軸承的Z軸方向上的辨識成功率達到99.61%,一方面對本團隊之高性價比三軸加速計模組進行可應用性驗證,另一方面也驗證了本方法對滾動軸承的健康診斷可行性。
In this research work, a cost-effective and high-bandwidth piezoelectric micro-electromechanical three-axis accelerometer module developed by our group is implemented to establish a health diagnosis and prediction system for motor rolling bearings. In Industry 4.0 era, intelligent control and predicted maintenance of machine tools play a vital role. Among the smart manufacturing applications (Industry 4.0), rolling bearings are an indispensable part of many machine tools. Failures caused by bearing damage account for up to 80% of the total mechanical failures. Therefore, this research will focus on the rolling bearing health analysis and diagnosis.
This research aims to achieve the best rolling bearing health diagnosis accuracy with a few steps of preprocessing. This thesis will start from explaining the method of analyzing the vibration data of rolling bearings. Next, the vibration data will be collected from various health-status bearings at three different rotating speeds of 500, 1,000, 1,500 rpm, respectively. Then, the three-axis acceleration vibration data of each experiment will be applied with the Ensemble Empirical Mode Decomposition (EEMD) method to separate the intrinsic mode function. The envelope analysis will be adopted on IMF3 to acquire frequency features. These features will be used to perform learning and prediction of the health states for rolling bearings through an Artificial Neural Network (ANN). The identification rate of this method in Z-axis direction of the bearing reached 99.61% after the neural network model validation and testing. On one hand, the applicability of the cost-effective accelerometer module was verified; on the other hand, the feasibility of the proposed method in this study was also verified in terms of rolling bearing health diagnosis.
摘要
致謝
圖目錄
表目錄
第一章 前言------------1
1-1研究動機與背景------------1
1-2文獻回顧------------4
第二章 研究方法與理論------------7
2-1 時間序列分割------------7
2-2 振動資料分析方法------------9
2-2-1 軸承缺陷振動特徵頻率------------10
2-2-2 包絡譜分析方法(Envelope Analysis)------------13
2-2-3 經驗模態分解(EMD)------------16
2-2-3 總體型經驗模態分解(EEMD)------------20
2-3 機器學習模型------------22
2-3-1 神經網路架構------------22
2-3-2 權重參數更新------------25
2-3-3 過擬合問題------------29
2-3-4 模型複雜度設計------------31
2-3-5 模型評估驗證指標------------31
第三章 實驗設計------------33
3-1 量測平台架設------------33
3-2 軸承規格------------35
3-3 缺陷軸承------------36
3-4 壓電式微機電三軸加速度計感測模組------------37
3-5 資料擷取------------41
3-6 實驗流程規劃------------43
第四章 實驗結果與討論------------45
4-1 原始量測數據------------45
4-2 總體型經驗模態分解預處理------------46
4-3 IMF分量選擇------------48
4-4 類神經網路模型訓練------------63
4-4-1 訓練和測試資料------------63
4-4-2 特徵融合------------64
4-4-3模型辨識能力結果------------65
4-5 實驗總結------------67
第五章 結語------------69
參考文獻------------71
附錄A------------75
附錄B------------78
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