博碩士論文 110226033 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:106 、訪客IP:13.59.197.237
姓名 蔡文傑(Wen-Chieh Tsai)  查詢紙本館藏   畢業系所 光電科學與工程學系
論文名稱 工業機台上衰減係數之優化研究
(Optimization Research of Decay Coefficient on Industrial Machine)
相關論文
★ 腦電波傅利葉特徵頻譜之研究★ 光電星雲生物晶片之製作
★ 電場控制器光學應用★ 手機照相鏡頭設計
★ 氣功靜坐法對於人體生理現象影響之研究★ 針刺及止痛在大鼠模型的痛覺量測系統
★ 新光學三角量測系統與應用★ 離軸式光學變焦設計
★ 腦電波量測與應用★ Fresnel lens應用之量測
★ 線型光學式三角量測系統與應用★ 非接觸式電場感應系統
★ 應用田口法開發LED燈具設計★ 巴金森氏症雷射線三角量測系統
★ 以Sol-Gel法製備高濃度TiO2用於染料敏化太陽能電池光電極之特性研究★ 生產線上之影像量測系統
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 ( 永不開放)
摘要(中) 本論文將衰減係數架構應用於實際工業機台上,透過該機台的振動變化量計算機台之衰減係數,不只可以判斷出機台現在所處的狀態與行為,還能依照不同的狀態與行為來判斷機台當下的健康程度。在同機型但不同個體的機台情況下還能對其健康程度做排序,由於衰減係數的計算,是透過振動變化量的自我比對,因此該計算模型可以套用在不同的機台上。
本論文將感測器與工業上的機台做結合,透過感測器來接收機台的振動數據,再依照振動數據來分析計算該機台的衰減係數,並與工廠端所提供的機台生產瑕疵來做對應,光是看振動很難直接看出差異,但透過衰減係數量化後,可以直接看出差異性。且衰減係數不止能對單一機台做狀態判斷,還可以對運作行為相同的機台做健康程度的比較,判斷出機台間的差異,如第一台的衰減係數為0.000166、第二台的衰減係數為0.000336,代表第二台較第一台更需要被維修,可以讓工廠端更準確對機台進行維修及換料等。實驗結果證明,透過振動衰減係數量測分析,(1)對新舊鑽石片切削力與工件破裂情況的比對100%吻合,(2) 加工機器的穩定狀況與工件破裂情況的比對100%吻合。
摘要(英) This paper applies the decay coefficient on practical industrial machinery. By calculating the vibration variation of the machine, the decay coefficient can be determined, enabling the identification of the current state and behavior of the machine, as well as assessing its health condition based on different states and behaviors. It is also possible to rank the health conditions of machines of the same model but different units. Since the calculation of the decay Coefficient involves self-comparison of vibration variations, this model can be applied to different machines.
The paper combines sensors with industrial machinery. Vibration data from the machine is collected through sensors, and the decay coefficient of the machine is calculated based on this data. This coefficient is then correlated with the provided information on production defects by the factory. It is difficult to directly discern differences just by observing vibration patterns, but quantifying them through the decay coefficient allows for direct comparison. Furthermore, the decay coefficient can not only determine the status of an individual machine but also compare the health conditions of machines with similar operating behaviors, highlighting the differences between them. For example, the decay coefficient of the first machine is 0.000166, and decay coefficient of the second machine is 0.000336, which means that the second machine needs to be repaired more than the first machine, which allows the factory to more accurately repair and refuel the machine.
The experimental results have demonstrated that through the analysis of vibration damping coefficients, (1) a 100% correlation was observed between the cutting forces of new and used diamond blades and the occurrence of workpiece fractures, and (2) a 100% correlation was observed between the stability of the machining equipment and the occurrence of workpiece fractures.
關鍵字(中) ★ 振動
★ 衰減係數
關鍵字(英) ★ vibration
★ decay coefficient
論文目次 摘要 I
ABSTRACT II
誌謝 IV
目錄 V
圖目錄 VIII
表目錄 XII
第一章 緒論 1
1-1. 研究背景 1
1-2. 研究動機 2
1-3. 研究貢獻 3
第二章 文獻回顧 4
2-1. 機台的維護方法 4
2-1-1. 修正性維護 4
2-1-2. 預防性維護 5
2-1-3. 預測性維護 6
2-2. 機台衰減的因素 7
2-2-1. 自由振動與受迫振動 7
2-2-2. 線性振動與非線性振動 9
2-3-1. 自激振動與參數振動 10
2-3. 人工智慧 10
2-3-1. 監督式學習 11
2-3-2. 非監督式學習 12
2-3-3. 增強式學習 13
2-3-4. 半監督式學習 14
2-4. 振動OA值 15
2-5. 狀態預測 16
第三章 理論 19
3-1. 衰減係數 20
3-1-1. 物理意義 20
3-1-2. 限制 22
3-1-3. 影響因素 22
3-1-4. 時間區間 23
3-2. 振動訊號來源 25
3-3. 純量 26
3-4. 過濾訊號 27
3-4-1. 排除人為碰撞訊號 27
3-4-2. 排除共振訊號 30
3-5. 感測器設備 31
3-5-1. CC2650MODA藍芽晶片 31
3-5-2. LIS2DH12三軸加速規 32
3-5-3. SHT21溫度、濕度感測器 33
3-5-4. 電路設計 34
第四章 實驗 38
4-1. 實驗架構 38
4-2. 同機台不同行為 39
4-3. 不同機台同行為 49
4-4. 時間區間 63
第五章 研究結論與未來展望 66
5-1. 結論 66
5-2. 未來展望 67
參考文獻 68
參考文獻 [1]. Online resources : 維護. (n.d.).Wikipedia.
[2]. Ho , C. Y., Fan, C. L., & Lin, J. W. (2007). A Study on Maintenance Management Systems and Their Production Performance. 第八卷(第二期).
[3]. Online resources : Reliability Centred Maintenance Analysis. (n.d.). MTain. https://web.archive.org/web/20160816021108/http:/www.mtain.com/logistics/logrcm.htm
[4]. Online resources: 機器學習. (n.d.). Wikipedia.
[5]. Alzubaidi, L., Zhang, J., Humaidi, A.J. et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8, 53 (2021).
[6]. Mari Cruz Garcia, Miguel A. Sanz-Bobi, Javier del Pico,SIMAP: Intelligent System for Predictive Maintenance: Application to the health condition monitoring of a windturbine gearbox,Computers in Industry,Volume 57, Issue 6,2006,Pages 552-568,
ISSN 0166-3615
[7]. K. A. Kaiser and N. Z. Gebraeel, "Predictive Maintenance Management Using Sensor-Based Degradation Models," in IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol. 39, no. 4, pp. 840-849, July 2009, doi: 10.1109/TSMCA.2009.2016429.
[8]. Miljkovic, D. (2015). Brief Review of Vibration Based Machine Condition Monitoring.
[9]. Chang, S. C. (2016). 即時診斷馬達故障之嵌入式系統.
[10]. Sadeghi, I. (2017). Online Fault Diagnosis of Large Electrical Machines Using Vibration Signal-a Review. IEEE.
[11]. Online resources: 如何理解單自由度系統振動. (2020, November 22). MdEditor.
[12]. Online resources:自激振動. (n.d.). 中文百科.
[13]. Online resources:參數振動. (n.d.). 中文百科.
[14]. Tsai, Y. T., Chen, C. C., & Liao, Z. X. (2012). A Study of Performance Degradation Predictions and Fault Diagnoses for Mechanical Systems. 技術學刊, 第二十七卷(第三期).
[15]. Online resources: 人工智慧史. (n.d.). Wikipedia.
[16]. Online resources: 人工智慧. (n.d.). Wikipedia.
[17]. Online resources: 機器學習. (n.d.). Wikipedia.
[18]. Online resources: 線性迴歸. (n.d.). Wikipedia.
[19]. Online resources: CC2650 SimpleLinkTM Multistandard Wireless MCU. (2016 7). TexasInstruments.
[20]. Online resources:LIS2DH12. (2017). ST.
指導教授 張榮森(Rong-Seng Chang) 審核日期 2023-7-24
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明