請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88510完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳文方 | zh_TW |
| dc.contributor.advisor | Wen-Fang Wu | en |
| dc.contributor.author | 彭筱媛 | zh_TW |
| dc.contributor.author | Hsiao-Yuan Peng | en |
| dc.date.accessioned | 2023-08-15T16:37:25Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-15 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-29 | - |
| dc.identifier.citation | [1] F. Camci and R. B. Chinnam, “Health-state estimation and prognostics in machining processes,” IEEE Transactions on Automation Science and Engineering, Vol. 7, No. 3, pp. 581-597, Jul. 2010.
[2] M. J. Xu, P. Baraldi, Z. Yang, and E. Zio, “A two-stage estimation method based on Conceptors-aided unsupervised clustering and convolutional neural network classification for the estimation of the degradation level of industrial equipment,” Expert Systems with Applications, Vol. 213, Part B, Article 118962, Mar. 2023. [3] J. Dalzochio, R. Kunst, E. Pignaton, A. Binotto, S. Sanyal, J. Favilla, and J. Barbosa, “Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges,” Computers in Industry, Vol. 123, Article 103298, Jul. 2021. [4] N. Beganovic and D. Söffker, “Remaining lifetime modeling using State-of-Health estimation,” Mechanical Systems and Signal Processing, Vol. 92, pp. 107-123, Apr. 2017. [5] N. Ma, F. Yang, L. Tao, and M. L. Suo, “State-of-health assessment for aero-engine based on density-distance clustering and fuzzy Bayesian risk,” IEEE Access, Vol. 9, pp. 9996-10011, Feb. 2021. [6] G. W. Vogl, B. A. Weiss, and M. Helu, “A review of diagnostic and prognostic capabilities and best practices for manufacturing,” Journal of Intelligent Manufacturing, Vol. 30, No. 1, pp. 79–95, Jan. 2019. [7] D. Wang, K. L. Tsui, and Q. Miao, “Prognostics and health management: a review of vibration based bearing and gear health indicators,” IEEE Access, Vol. 6, Dec. 2018. [8] Y. H. Wu, M. Y. Liu, H. Song, C. Li, and X. L. Yang, “A temperature and magnetic field-based approach for stator inter-turn fault detection,” IEEE Sensors Journal, Vol. 22, No.18, pp. 17799-17807, Nov. 2022. [9] S. Kumar, P. Kumar, and G. Kumar, “Degradation assessment of bearing based on machine learning classification matrix,” Eksploatacja i Niezawodnosc-Maintenance and Reliability, Vol. 23, No. 2, pp. 395-404, May 2021. [10] D. A. Tobon-Mejia, K. Medjaher, N. Zerhouni, and G. Tripot, “A data-driven failure prognostics method based on mixture of gaussians hidden Markov models,” IEEE Transactions on Reliability, Vol.61, No 2, pp. 491-503, Jun. 2012. [11] S. Ramezani, A. Moini, M. Riahi, and A. C. Marquez, “A model to determining the remaining useful life of rotating equipment, based on a new approach to determining state of degradation,” Journal of Central South University, Vol. 27, No. 8, pp. 2291-2310, Sep. 2020. [12] M. A. Khan, B. Asad, K. Kudelina, T. Vaimann, and A. Kallaste, “The bearing faults detection methods for electrical machines—the state of the art,” Energies, Vol 16, No. 1, Article 296, Jan 2023. [13] A. V. Oppenheim, R. W. Schafer, and J. R. Buck, Discrete Time Signal Processing 2nd Edition. New Jersey, USA: Prentice Hall, 1998. [14] S. Li and J. Wen, “A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform,” Energy and Buildings, Vol. 68, pp. 63-71, Feb. 2014. [15] B. S. Everitt, S. Landau, M. Leese, and D. Stahl, Cluster Analysis, 5th Edition. London, UK: Wiley, 2011. [16] I. T. Jolliffe, Principal Component Analysis, 2nd Edition. NY, USA: Springer, 2002. [17] M. Suo, B. Zhu, Y. Zhang, R. An, and S. Li, “Fuzzy Bayes risk based on Mahalanobis distance and Gaussian kernel for weight assignment in labeled multiple attribute decision making,” Knowledge-Based Systems, Vol. 152, pp. 26-39, Jul. 2018. [18] T. J. Ross, Fuzzy Logic with Engineering Applications, 2nd Edition. West Sussex, UK: John Wiley &Sons, 2004. [19] D. L. Donoho and I. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika, Vol. 81, No. 3, pp. 425-455, Aug. 1994. [20] H. Z. M. Shafri and M. R. M. Yusof, “Determination of optimal wavelet denoising parameters for red edge feature extraction from hyperspectral data,” Journal of Applied Remote Sensing, Vol. 3, Article 033533, May 2009. [21] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd Edition. MA, USA: Morgan Kaufmann, 2011. [22] R. R. Yager, “On ordered weighted averaging aggregation operators in multicriteria decisionmaking,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 18, No. 1, pp. 183-190, Jan. 1988. [23] D. C. Montgomery, E. A. Peck, and G. G. Vining, “Chapter 3 Multiple Linear Regression,” in Introduction to Linear Regression Analysis, 5th Edition, New Jersey, USA: Wiley, 2012, pp. 67-128. [24] B. L. Bowerman, R. T. O’Connell, and E. S. Murphree, Business Statistics in Practice, Using Modeling, Data, and Analytics, 8th Edition. NY, USA: McGraw-Hill Education, 2017. [25] N. Oza, “C-MAPSS aircraft engine simulator data,” NASA’s Open Data Portal, https://data.nasa.gov/dataset/C-MAPSS-Aircraft-Engine-Simulator-Data/xaut-bemq, Sep. 2010. [26] Y. Liu, Z. Liu, H. Zuo, H. Jiang, P. Li, and X. Li, “A DLSTM-network-based approach for mechanical remaining useful life prediction,” Sensors, Vol. 22, No. 15, Article 5680, Aug. 2022. [27] J. Fraden, Handbook of Modern Sensors, Physics, Designs, and Applications, 4th Edition. NY, USA: Springer Science+Business Media, 2010. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88510 | - |
| dc.description.abstract | 在競爭激烈環境下，降低工業機台或設備之維護成本並提高其效率對企業至關重要，而藉助各種感測器監控機台或設備之健康狀態並依監控所得數據釐定維護策略是大家所能想到的作為。本研究針對故障偵測和診斷提出了一種綜合考慮多個數據源和多個評估指標、可應用於工業機台或設備健康狀態評估的模型建構流程，該流程透過建立評估指標與數據的綜合分析，能夠準確評估機台設備的健康狀態，並預測故障時間點。本研究所提流程屬於數據驅動，無需深入瞭解機台或設備的運作原理，也無需標記故障數據，可降低專業人力成本。本研究以收集到的航空引擎衰退模擬數據進行案例分析和驗證，證明所提流程的有效性。 | zh_TW |
| dc.description.abstract | In a highly competitive environment, reducing maintenance costs and improving the efficiency of industrial machinery or equipment is crucial for businesses. Monitoring the state-of-health for machinery or equipment through various sensors and determining maintenance strategies based on the monitored data is a widely considered approach. Within the context, this study proposes a comprehensive state-of-health assessment model for industrial machinery or equipment by considering multiple data sources and evaluation metrics. Through the integration of data and metrics, the state-of-health and failure time of a machine or a piece of equipment can be predicted. The proposed model is a data-driven one which does not require the in-depth understanding of operational principles of the machine or equipment. Neither does it has to label failure data. The operational cost of the machine or equipment can thus be reduced. The effectiveness of the proposed model is demonstrated and validated through a case study consisting of a batch of degradation data monitored for a certain type of aero-engine. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-15T16:37:25Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-15T16:37:25Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 摘要 I
ABSTRACT II 目錄 III 圖目錄 VI 表目錄 VIII 第一章 緒論 1 1.1 研究背景與動機 1 1.2 文獻回顧及研究目的 2 1.2.1 過去的研究及成果 2 1.2.2 當前的研究困境 3 1.2.3 研究目的 4 1.3 論文架構 4 第二章 研究方法 6 2.1 研究流程概述 6 2.2 數據預處理 6 2.2.1 小波去噪 7 2.3 聚類分析 10 2.3.1 主成分分析 11 2.3.2 聚類分析原理 11 2.4 風險計算 12 2.4.1 決策系統 12 2.4.2 模糊貝葉斯風險 13 2.5 健康狀態評估模型 15 2.5.1 模糊理論 15 2.6 驗證 18 2.7 小結 18 第三章 模型建構 19 3.1 使用數據 20 3.2 數據預處理 20 3.2.1 刪除數據不變指標 20 3.2.2 小波去噪 20 3.2.3 數據集分割 22 3.2.4 正規化 22 3.3 聚類分析 23 3.3.1 主成分分析 23 3.3.2 產生標籤 23 3.4 風險計算 24 3.4.1 模糊貝葉斯風險計算 24 3.4.2 配重與指標篩選 25 3.5 健康狀態評估模型 26 3.5.1 健康分數 26 3.5.2 自我健康程度 26 3.5.3 狀態隸屬程度 27 3.6 驗證 30 第四章 案例分析與討論 31 4.1 案例數據集簡介 31 4.2 建立模型 32 4.2.1 數據預處理 32 4.2.2 聚類分析 38 4.2.3 風險計算 42 4.2.4 健康狀態評估模型 44 4.2.5 驗證 48 4.3 小結 53 第五章 結論 56 參考文獻 58 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 多指標評估 | zh_TW |
| dc.subject | 機台 | zh_TW |
| dc.subject | 設備 | zh_TW |
| dc.subject | 健康狀態評估 | zh_TW |
| dc.subject | 數據驅動 | zh_TW |
| dc.subject | Equipment | en |
| dc.subject | Machinery | en |
| dc.subject | Multi-Indicator Evaluation | en |
| dc.subject | Data-Driven | en |
| dc.subject | State-of-Health Assessment | en |
| dc.title | 數據驅動之工業機台或設備健康狀態評估 | zh_TW |
| dc.title | Data-Driven State-of-Health Assessment for Industrial Machinery or Equipment | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 蔡曜陽;黃奎隆 | zh_TW |
| dc.contributor.oralexamcommittee | Yao-Yang Tsai;Kwei-Long Huang | en |
| dc.subject.keyword | 機台,設備,健康狀態評估,數據驅動,多指標評估, | zh_TW |
| dc.subject.keyword | Machinery,Equipment,State-of-Health Assessment,Data-Driven,Multi-Indicator Evaluation, | en |
| dc.relation.page | 61 | - |
| dc.identifier.doi | 10.6342/NTU202302351 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-08-01 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 機械工程學系 | - |
| 顯示於系所單位： | 機械工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-111-2.pdf 未授權公開取用 | 4.65 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。