 |
|
|
|
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：124 、訪客IP：3.16.29.209
姓名 陳英真(CHEN-YING CHEN) 查詢紙本館藏 畢業系所 資訊管理學系在職專班 論文名稱 風扇壽命預測使用大數據分析－以 X 公司為例
(Prediction the useful life of cooling fan using machine learning techniques: The case of X-company)相關論文 檔案 [Endnote RIS 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 (2027-7-11以後開放)
摘要(中) 散熱風扇廣泛使用於不同設備的散熱系統中，而散熱風扇的穩定度將直接影響設備
的性能。有許多國際標準組織訂定標準來評估風扇的效能，且許多風扇製造商為提升散
熱風扇的穩定度及使用壽命，提出不同方式及改善方法，例如改善風扇組裝結構，或利
用電子電路監控風扇的運轉模式。
本研究採用資料探勘技術，並多種監督式學習技術進行風扇壽命預測模型的建立，
包括類神經網路(ANN)、隨機森林(RF)、支援向量機(SVM)以及羅吉斯迴歸(LR)。此外，
由於風扇的組成結構包含許多機械元件及電子元件，該些元件均係影響風扇壽命的因子，
引此本研究納入 26 個自變項進行風扇壽命預測模型的建立，用以提升散熱風扇的壽命
預測準確率。
最後，根據本研究結果顯示針對扇熱風扇在運轉 4360hr、6360hr 及 7360hr 的運轉
後是否會發生 FAIL 之情形進行探討，結果顯示均能有 78%以上的預測準確度，另在上
述四種技術當中，就研究結果顯示較佳的評估結果為支援向量機及隨機森林，此兩個演
算法技術可以幫助風扇壽命預測上，建立較佳的預測模型。摘要(英) Cooling fans are widely used in the cooling systems of different equipment, and the
stability of the cooling fans will directly affect the performance of the equipment. Many
international standards organizations have set standards to evaluate the performance of fans,
and many fan manufacturers have proposed different methods and improvement methods to
improve the stability and using life of cooling fans, such as improving the fan assembly
structure, or using electronic circuits to monitor the performance of fans.
This paper investigates uses supervised learning techniques, including artificial neural
network (ANN), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression
(LR) to construct a cooling fan life prediction model. Since the component of the cooling fan
includes many mechanical and electronic parts, these components are affect the life of the fan.
Therefore, collects 26 key parameters in this study to establish a fan life prediction model to
improve the accuracy of life prediction of cooling fans.
According to the results of experimental, it is studies whether FAIL will occur after the
cooling fan running for 4360hr, 6360hr and 7360hr. The results can be more than 78% accuracy
at each prediction model. In addition, among the above four learning technologies, the research
results show that the best evaluation results are SVM and RF. These two algorithm technologies
can help to establish the better prediction model for fan life prediction.關鍵字(中) ★ 散熱風扇
★ 資料探勘
★ 監督式學習
★ 壽命預測關鍵字(英) ★ Cooling fan
★ Data mining
★ Supervised learning
★ Life prediction論文目次 摘要 ............................................................................................................................................. i
Abstract ..................................................................................................................................... ii
目錄 ........................................................................................................................................... iii
圖目錄 ........................................................................................................................................ v
表目錄 ....................................................................................................................................... vi
第 1 章 緒論 .............................................................................................................................. 1
1.1 研究背景 .......................................................................................................................... 1
1.2 研究動機 .......................................................................................................................... 3
1.3 研究目的 .......................................................................................................................... 5
第 2 章 文獻探討 ...................................................................................................................... 7
2.1 風扇結構組成 .................................................................................................................. 7
2.2 影響風扇使用壽命之因素相關研究 .............................................................................. 9
2.2.1 使用震動因素探討風扇壽命之研究 ........................................................................ 9
2.2.2 使用運轉轉速及電流因素探討風扇壽命之研究 .................................................. 10
2.2.3 使用工作溫度因素探討風扇壽命之研究 .............................................................. 11
2.3 應用監督式研究方法預測產品壽命之研究 ................................................................ 11
第 3 章 研究方法 .................................................................................................................... 16
3.1 研究流程 ........................................................................................................................ 16
3.2 資料來源與前處理 ........................................................................................................ 17
3.3 研究變數 ........................................................................................................................ 18
3.3.1 風扇外形型式 ......................................................................................................... 19
3.3.2 軸承系統相關 .......................................................................................................... 20
3.3.3 扇框葉部件相關 ...................................................................................................... 20
3.3.4 量測特性值相關 ...................................................................................................... 20
3.4 研究方法及工具 ............................................................................................................ 20
3.4.1 類神經網路(Artificial Neural Network) ............................................................... 20
3.4.2 隨機森林(Random Forest) ..................................................................................... 21
國立中央大學資訊管理研究所 陳英真
3.4.3 支援向量機(Support Vector Machine) ................................................................. 21
3.4.4 羅吉斯迴歸(Logistic Regression) .......................................................................... 21
3.4.5 Orange ..................................................................................................................... 21
3.5 實驗設計與評估 ............................................................................................................ 22
3.5.1 實驗設計 .................................................................................................................. 22
3.5.2 驗證與評估方式 ...................................................................................................... 23
第 4 章 實驗結果與分析 ........................................................................................................ 25
4.1 實驗資料 ........................................................................................................................ 25
4.2 實驗結果 ........................................................................................................................ 25
4.3 變項重要性排序 ............................................................................................................ 28
4.4 綜合討論 ....................................................................................................................... 31
第 5 章 研究結論與建議 ........................................................................................................ 33
5.1 研究結論與貢獻 ............................................................................................................ 33
5.2 研究限制 ........................................................................................................................ 34
5.3 未來研究方向與建議 .................................................................................................... 34
參考文獻 .................................................................................................................................. 35參考文獻 王伯恭、吳建諭、郭德民(2012)。改良型風扇。中華民國專利號 M419833。台
北:經濟部智慧財產局。
王鋒谷、鄭懿倫(2009)。風扇壽命的預估方法。中華民國專利號 402524。台北:
經濟部智慧財產局。
林昀正 (2008)。具有風扇之散熱模組。中華民國專利號 200817876。台北:經濟
部智慧財產局。
岩壺卓三(2005)。滾動軸承的剩餘壽命診斷方法及剩餘壽命診斷裝置。中華民
國專利號 282850。台北:經濟部智慧財產局。
許家彰、黃世峰、徐偉書(2019)。風扇運作狀態診斷裝置及其方法。中華民國
專利號 698586。台北:經濟部智慧財產局。
劉念華(2005)。改良型風扇。中華民國專利號 200501546。台北:經濟部智慧財
產局。
謝玲玉。2012。風扇測試系統。中華民國發明專利公開第 201215902 號。台北:
經濟部智慧財產局。
Berkson,J.(1994). Application of the logistic function to bio-assay. Journal of the
American Statistical Association, 39(227), 357-365.
Benkedjouh, Tarak, et al. (2012). Fault prognostic of bearings by using support vector
data description. 2012 IEEE Conference on Prognostics and Health Management.
Carroll, J., Koukoura, S., McDonald, A., Charalambous, A., Weiss, S., & McArthur, S.
(2019). Wind turbine gearbox failure and remaining useful life prediction using
machine learning techniques. Wind Energy, 22(3), 360-375.
Canizo, Mikel, et al. (2017). Real-time predictive maintenance for wind turbines using
Big Data frameworks. 2017 IEEE International Conference on Prognostics and
Health Management (ICPHM).
Cortes, C., Vapnik, V. (1995). Support-vector networks. Mach Learn 20, 273–297.
Gebraeel, N., Lawley, M., Liu, R., & Parmeshwaran, V. (2004). Residual life
predictions from vibration-based degradation signals: a neural network
approach. IEEE Transactions on industrial electronics, 51(3), 694-700.
H. Oh, M. H. Azarian, D. Das and M. Pecht. (2013) A Critique of the IPC-9591
Standard: Performance Parameters for Air Moving Devices. IEEE Transactions
on Device and Materials Reliability, vol. 13, no. 1, 146-155.
He W., Miao Q., Azarian M., Pecht M. (2015). Health monitoring of cooling fan
bearings based on wavelet filter. Mechanical Systems and Signal Processing,
Vol. 64, Issue 65, 149-161.
Lao, Hongmou, and Saleh Zein-Sabatto. (2001). Analysis of vibration signal′s time-
frequency patterns for prediction of bearing′s remaining useful life. Proceedings
of the 33rd Southeastern Symposium on System Theory (Cat. No. 01EX460).
Lei Xie, Q. Miao, Yi Chen, Wei Liang and M. Pecht.(2012). Fan bearing fault diagnosis
based on continuous wavelet transform and autocorrelation. Proceedings of the
IEEE 2012 Prognostics and System Health Management Conference (PHM-2012
Beijing), 1-6.
Lixin, W., Zhenhuan, W., Yudong, F., & Guoan, Y. (2016). Remaining life predictions
of fan based on time series analysis and BP neural networks. 2016 IEEE
Information Technology, Networking, Electronic and Automation Control
Conference , 607-611.
McCulloch, W.S., Pitts, W. (1943). A logical calculus of the ideas immanent in nervous
activity. Bulletin of Mathematical Biophysics 5, 115–133.
Q. Miao, M. Azarian and M. Pecht. (2011). Cooling fan bearing fault identification
using vibration measurement. 2011 IEEE Conference on Prognostics and Health
Management, 1-5.
Performance Parameters (Mechanical, Electrical, Environmental and uality/Reliability)
for Air Moving Devices.(2006, Apr). IPC-9591, First Edition。
Singh, Savinay, et al. (2019). Predicting the Remaining Useful Life of Ball Bearing
Under Dynamic Loading Using Supervised Learning. IEEE International
Conference on Industrial Engineering and Engineering Management (IEEM).
S. Jin, Z. Wu, Y. Fu and G. Yang. (2017). The Remaining Life Prediction of the Fan
Bearing Based on Genetic Algorithm and Multi-Parameter Support Vector
Machine. 2017 5th International Conference on Mechanical, Automotive and
Materials Engineering (CMAME), pp. 42-47.
Tin Kam Ho. (1995). Random decision forests. Proceedings of 3rd International
Conference on Document Analysis and Recognition vol.1, 278-282.
Wen Wen , Xiao-qin, and Lin-ru You. (2016). A residual lifetime prediction method of
cooling fan based on the operating point offset distance. 2016 Chinese Control and
Decision Conference (CCDC).指導教授 胡雅涵(YA-HAN HU) 審核日期 2022-7-19 推文 plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
網路書籤 Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit