系統識別號 U0026-2507202016351900
論文名稱(中文) 以神經網路對滾珠螺桿系統作故障預測與健康管理
論文名稱(英文) Prognostic and Health Management of Ball Screw System by Neural Network
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) 林軒毅
研究生(英文) Hsuan-Yi Lin
學號 N16070146
學位類別 碩士
語文別 中文
論文頁數 91頁
口試委員 指導教授-蔡南全
中文關鍵字 雙螺帽式滾珠螺桿  預壓感測器  故障診斷  時間序列  循環神經網路 
英文關鍵字 Double-nut Ball Screw  Preload Sensor  Fault Diagnosis  Time Series  Recurrent Neural Network 
中文摘要 本論文針對進給系統中的雙螺帽式滾珠螺桿(Double-nut Ball Screw, DBS)提出故障診斷(Fault Diagnosis)方法,在搭配財團法人工業技術研究院南創園區所研發之預壓感測器(Preload Sensor)下,結合門控循環單元II型(Gate Recurrent Unit-II, GRU-II),來判斷雙螺帽式滾珠螺桿是否發生故障。
本論文之研究目標在於: 藉由觀察預壓感測器之訊號之變化來(i) 判斷雙螺帽式滾珠螺桿是否發生故障; (ii) 建立故障預測系統。 為了達到上述目標,本論文首先對雙螺帽式滾珠螺桿建立故障資料庫,由於感測器所得之時序資料(Time Series)具有序列性及長度不一致,因此本論文採重新取樣(Resampling)來對訊號進行前處理,搭配GRU-II對訊號進行識別以判斷故障狀況為何。 相較於其他現行診斷方法,GRU-II特點在於: (i) 有效提升鑑別準確率、(ii) 運算過程中所需之參數較少、(iii) 針對噪音更具容錯性。
為了驗證本論文提出之故障診斷系統之效用,本論文使用深度學習套件PyTorch與Python建立一循環神經網路模型(GRU-II),針對先前實驗所得之故障資料庫進行模擬分析,除此之外,並將雜訊所造成之影響一併考入。 由模擬結果可得知: (i) 在無噪音干擾下,本論文提出之GRU-II相較於其他神經網路: 前饋神經網路(Feed-forward Neural Network)、長短期記憶模型(Long Short Term Memory Model)與門控循環單元模型(Gate Recurrent Unit Model),於準確率方面,分別優約13.3%、4.4%、4.4%。 (ii) 若考量訊號雜訊,GRU-II仍能維持準確率84.4%,依舊優於其他上述三種模型。 此外,於故障迴流道之召回率則可保持在100%,驗證了本論文提出之故障診斷方法有卓越的效能。
英文摘要 In this thesis, an innovative fault diagnosis method for Double-nut Ball Screw (DBS) systems is proposed. Different from traditional approaches based on accelerometers to examine the health status of ball screw and nuts, the methodology employed by this thesis is to use a preload sensor developed by Industry Technology Research Institute (ITRI) instead. With the major aim at diagnosing the failure of DBS system correctly and then establishing health management system, the combination of signal resampling and Gate Recurrent Uint-II (GRU-II) is established. This thesis at first creates a database which collects the failures that often occur in DBS systems, and then utilizes signal resampling subsequently to resolve the problems that the time series obtained by preload sensor is inconsistent in sequence and length. Following the pre-processing signals, GRU-II is manipulated to identify which failures are. In contrast to most current diagnosis method, GRU-II can achieve great accuracy as well as be insusceptible to noise. To evaluate the performance of GRU-II, it is set up and verified under Python environment. According to simulation results, it demonstrates that excellent accuracy can be achieved by the proposed GRU-II either with signal noise or not. Compared to the optimal approaches via other neural networks(Feed-forward, Long short Term Memory, Gate Recurrent Unit), the accuracy of GRU-II is better than those by 13.3%, 4.4% and 4.4% respectively. Furthermore, GRU-II can even maintain recall of recirculating mechanism at 100%. As mentioned above, the overall prognosis method in this thesis can be potentially applied to the real-world machine tools.
論文目次 摘要 I
誌謝 VII
表目錄 XI
圖目錄 XII
符號 XIV
第一章 緒論 1
1.1 前言 1
1.2 研究動機與目的 3
1.3 文獻回顧 5
1.4 論文架構 7
第二章 進給系統故障資料庫建立 8
2.1 系統架構與硬體介紹 8
2.2 行程、進給速度與預壓量之影響 12
2.3 常見故障原因分析 14
2.4 故障資料收集與分析 18
第三章 問題描述及現有方法討論 22
3.1 針對時間序列分類的問題描述 23
3.2 前饋神經網路(Feed-forward Neural Network) 25
3.3 循環神經網路(Recurrent Neural Network) 26
3.3.1 長短期記憶模型(Long Short Term Memory Model) 28
3.3.2 門控循環單元(Gate Recurrent Unit) 31
第四章 故障診斷架構之設計 34
4.1 訊號切割與重新取樣 34
4.2 神經網路設計 39
4.2.1 神經網路架構 40
4.2.2 損失函數選擇 43
4.2.3 優化器選擇 47
第五章 診斷策略模擬結果 52
5.1 評估性能指標與模型訓練流程 53
5.2 模型訓練過程 56
5.2.1 FNN超參數調整流程 57
5.2.2 LSTM超參數調整流程 60
5.2.3 GRU超參數調整流程 63
5.2.4 GRU-II超參數調整流程 66
5.3 鑑別結果比較 70
5.3.1 未加入噪音之測試結果與討論 70
5.3.2 加入噪音之測試結果與討論 76
第六章 結論與未來展望 84
參考文獻 87
附錄A 90
附錄B 91
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