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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳誠亮(Cheng-Liang Chen) | |
| dc.contributor.author | Jun-Jie Lai | en |
| dc.contributor.author | 賴駿傑 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:40:27Z | - |
| dc.date.available | 2021-08-06 | |
| dc.date.available | 2022-11-24T03:40:27Z | - |
| dc.date.copyright | 2021-08-06 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-29 | |
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Sutrisno, E.; Oh, H.; Vasan, A.; Pecht, M., Estimation of remaining useful life of ball bearings using data driven methodologies. 2012; p 1-7. Purarjomandlangrudi, A.; Ghapanchi, A.; Esmalifalak, M., A data mining approach for fault diagnosis: An application of anomaly detection algorithm. Measurement 2014, 55, 343–352. Yang, F.; Xiaodiao, H.; Rongjing, H.; Jie, C., 1828. Residual useful life prediction of large-size low-speed slewing bearings -- a data driven method. Journal of Vibroengineering 2015, 17 (8), 4164-4179. Wu, B.; Li, W.; Qiu, M., Remaining Useful Life Prediction of Bearing with Vibration Signals Based on a Novel Indicator. Shock and Vibration 2017, 2017, 1-10. Greenwich, M., A unimodal hazard rate function and its failure distribution. Statistical Papers 1992, 33, 187-202. Jiang, R.; Ji, P.; Xiao, X., Aging property of unimodal failure rate models. Reliability Engineering System Safety 2003, 79, 113-116. Zheng, Y., Predicting Remaining Useful Life Based on Hilbert–Huang Entropy with Degradation Model. Journal of Electrical and Computer Engineering 2019, 2019, 1-11. Wen, J.; Gao, H.; Zhang, J., Bearing Remaining Useful Life Prediction Based on a Nonlinear Wiener Process Model. Shock and Vibration 2018, 2018, 1-13. Badmus, N.; Faweya, O.; Adeleke, K., GENERALIZED BETA-EXPONENTIAL WEIBULL DISTRIBUTION AND ITS APPLICATIONS. 2020. Mabel, Y.; Novita, M.; Nurrohmah, s., Discrete Weibull-geometric distribution. Journal of Physics: Conference Series 2021, 1725, 012033. Villa-Covarrubias, B.; Pina-Monarrez, M.; Barraza, J.; Baro, M., Stress-Based Weibull Method to Select a Ball Bearing and Determine Its Actual Reliability. Applied Sciences 2020, 10, 1-15. Yang, H.; Mathew, J.; Ma, L., Vibration feature extraction techniques for fault diagnosis of rotating machinery: A literature survey. Asia-Pacific Vibration Conference 2003. Caesarendra, W.; Tjahjowidodo, T., A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing. Machines 2017, 5, 21. Cheng, J.; Yang, Y.; Yu, D., The envelope order spectrum based on generalized demodulation time–frequency analysis and its application to gear fault diagnosis. Mechanical Systems and Signal Processing 2010, 24 (2), 508-521. Li, H.; Yin, Y., Bearing Fault Diagnosis Based on Laplace Wavelet Transform. Indonesian Journal of Electrical Engineering 2012, 10, 2139-2150. Li, H.; Fu, L.; Zheng, H., Bearings Fault Detection and Diagnosis Using Envelope Spectrum of Laplace Wavelet Transform. Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09 2009. McInerny, S. A.; Dai, Y., Basic vibration signal processing for bearing fault detection. Education, IEEE Transactions on 2003, 46, 149-156. 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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81279 | - |
| dc.description.abstract | "剩餘使用壽命 (RUL) 是對機械元件壽命的預估，其被視為機器健康狀況的重要指標。正常來說，機器的健康狀況會隨著時間的推移而變差。因此在工廠中常常需要定期的維護及更換機械元件。然而，如何決定維護的週期事件重要的事。提早維護可以防止機器損壞，但要付出更多不必要的成本；若是維修太晚，可能會損壞機器並造成人員傷亡。若是能準確的預測RUL便可提供合適的時機，並為操作人員保持安全的環境。 在本研究中，數據來源來自IEEE在2012的挑戰。有一些論文使用數據驅動模型(data-based model)，例如人工神經網絡來解決這個問題。然而，數據驅動的模型通常缺乏對機械設備狀況的概述。此外，數據驅動模型中參數的物理意義知之甚少。 在我們的參考研究中，使用韋伯加速失效時間回歸（Weibull Accelerated Failure Time Regression, WAFTR）模型，其中用韋伯可靠性函數來敘述RUL。通過將可靠性函數中的參數η進行指數展開來修正韋伯可靠性函數。在這項研究中，我們很好奇是否可以將參數展開為不同的形式。此外，我們考慮了更多的可靠性參數進行展開修飾 (η、β)。此外，我們假設實際的 RUL 是線性衰減的。希望透過我們的模型可以使預測的RUL能趨近線性衰減。為了量化預測的表現，我們計算了預測 RUL 和實際 RUL 的均方誤差 (MSE)，MSE越小代表預測結果越好。最後，我們發現應用單一模型會使可靠性參數失去其物理意義。我們進一步介紹了多重模型，其誤差在 20% 以內，並展現了更有意義的可靠性參數。 " | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:40:27Z (GMT). No. of bitstreams: 1 U0001-2407202115470300.pdf: 6603251 bytes, checksum: f81599766e1c015dbeddefb103a7ca0f (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 致謝 ii 中文摘要 iii Abstract iv List of Figures vi List of Tables viii Chapter 1 Introduction 1 1.1 Background 1 1.2 Paper review 7 1.3 Weibull Distribution Function 10 1.4 Features 14 1.5 Principal Component Analysis (PCA) 16 Chapter 2 Methodology 19 2.1 Reliability Model 21 2.2 Model Parameters 25 2.3 Target 35 2.4 Model regression 36 Chapter 3 Result and Discussion 41 3.1 Single model to multiple models 54 3.2 The MSE value with model that is trained with continuously splitting datasets 69 Chapter 4 Conclusion and Future Works 71 4.1 Conclusion 71 4.2 Future works 73 Reference 74 | |
| dc.language.iso | en | |
| dc.subject | 可靠性函數 | zh_TW |
| dc.subject | 韋伯方程式 | zh_TW |
| dc.subject | 機械剩餘壽命 | zh_TW |
| dc.subject | reliability function | en |
| dc.subject | remaining useful life | en |
| dc.subject | Weibull distribution | en |
| dc.title | 使用韋伯可靠性函數建立軸承剩餘壽命預測模型 | zh_TW |
| dc.title | Weibull Reliability Regression Model for Bearing Remaining Useful Life Prediction | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 錢義隆(Hsin-Tsai Liu),吳哲夫(Chih-Yang Tseng),余柏毅 | |
| dc.subject.keyword | 韋伯方程式,機械剩餘壽命,可靠性函數, | zh_TW |
| dc.subject.keyword | Weibull distribution,remaining useful life,reliability function, | en |
| dc.relation.page | 76 | |
| dc.identifier.doi | 10.6342/NTU202101707 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-07-30 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 化學工程學研究所 | zh_TW |
| 顯示於系所單位： | 化學工程學系 | |
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