請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/4469
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 詹魁元 | |
dc.contributor.author | Yu-Chen Chang | en |
dc.contributor.author | 張祐晨 | zh_TW |
dc.date.accessioned | 2021-05-14T17:42:29Z | - |
dc.date.available | 2015-08-20 | |
dc.date.available | 2021-05-14T17:42:29Z | - |
dc.date.copyright | 2015-08-20 | |
dc.date.issued | 2015 | |
dc.date.submitted | 2015-08-17 | |
dc.identifier.citation | [1] Electric vehicles, 2015. http://petroleum.co.uk/electric-vehicles.
[2] J. Perdiguero and J. L. Jiménez. Policy options for the promotion of electric vehicles: a review, 2012. [3] Electric vehicle geographic forecasts, 2014. http://www.navigantresearch.com/ research/electric-vehicle-geographic-forecasts. [4] Prius defect information report. Technical report, Toyota Motor Corporation, 2014. [5] Recall report. Technical report, Fiat Chrysler Automobiles, 2014. [6] A. Haldar and S. Mahadevan. Probability, reliability, and statistical methods in engineering design. John Wiley, 1st edition, 2000. [7] A. Volkanovski, M. Čepin, and B. Mavko. Application of the fault tree analysis for assessment of power system reliability. Reliability Engineering & System Safety, 94(6):1116 – 1127, 2009. [8] J. Guo, K. Zhang, L. Shi, K. Gu, W. Bai, B. Zeng, and Y. Liu. Fault diagnosis system based on dynamic fault tree analysis of power transformer. In Fuzzy Systems and Knowledge Discovery, 9th International Conference, pages 2679–2682, May 2012. [9] G. Biswal, R. Maheshwari, and M. Dewal. System reliability and fault tree analysis of seswrs based hydrogen-cooling system. In Advanced Power System Automation and Protection, volume 2, pages 1418–1423, Oct 2011. [10] J. E. Staley and P. S. Sutcliffe. Reliability block diagram analysis. Microelectronics and Reliability, 13:33–47, 1974. [11] B. Nystrom, L. Austrin, N. Ankarback, and E. Nilsson. Fault tree analysis of an aircraft electric power supply system to electrical actuators. In Probabilistic Methods Applied to Power Systems, pages 1–7, June 2006. [12] Failure mode and effects analysis - handbook supplement for machinery. Technical report, Ford Motor Company, 1996. [13] M.A. Djeziri, B. Ananou, and M. Ouladsine. Data driven and model based fault prognosis applied to a mechatronic system. In Power Engineering, Energy and Electrical Drives (POWERENG), 4th International Conference, pages 534–539, May 2013. [14] C. Ciandrini, M. Gallieri, A. Giantomassi, G. Ippoliti, and S. Longhi. Fault detection and prognosis methods for a monitoring system of rotating electrical machines. In Industrial Electronics (ISIE), IEEE International Symposium, pages 2085–2090, July 2010. [15] M. Pecht and R. Jaai. A prognostics and health management roadmap for information and electronics-rich systems. Microelectronics Reliability, 50(3):317–323, 2010. [16] M. Pecht and G. Jie. Physics-of-failure-based prognostics for electronic products. Transactions of the Institute of Measurement and Control, 31:309–322, 2009. [17] H. Seyedi, M. Fotuhi, and M. Sanaye-Pasand. An extended markov model to determine the reliability of protective system. In Power India Conference, IEEE, pages 5–9, 2006. [18] A.M. Bazzi, A. Dominguez-Garcia, and P.T. Krein. Markov reliability modeling for induction motor drives under field-oriented control. Power Electronics, IEEE Transactions, 27(2):534–546, Feb 2012. [19] V. Volovoi. Modeling of system reliability petri nets with aging tokens. Reliability Engineering & System Safety, 84(2):149–161, 2004. [20] V. Volovoi, G. Kavalieratos, M. Waters, and D. Mavris. Modeling the reliability of distribution systems using petri nets. In Harmonics and Quality of Power, 11th International Conference, pages 567–572, Sept 2004. [21] C.-M. Lin, H.-K. Teng, C.-C. Yang, H.-L. Weng, M.-C. Chung, and C.-C. Chung. A mesh network reliability analysis using reliability block diagram. In Industrial Informatics, 8th IEEE International Conference, pages 975–979, July 2010. [22] M.I. Ridwan, K.L. Yen, I.A. Musa, and B. Yunus. Reliability and availability assessment of transmission overhead line protection system using reliability block diagram. In Power and Energy (PECon), IEEE International Conference, pages 964–969, Nov 2010. [23] W. Wang, J.M. Loman, R.G. Arno, P. Vassiliou, E.R. Furlong, and D. Ogden. Reliability block diagram simulation techniques applied to the ieee std. 493 standard network. Industry Applications, IEEE Transactions, 40(3):887–895, May 2004. [24] P. Zhang, L. Portillo, and M. Kezunovic. Reliability and component importance analysis of all-digital protection systems. In Power Systems Conference and Exposition, IEEE PES, pages 1380–1387, Oct 2006. [25] Z. Zong and K.Y. Lam. Bayesian estimation of complicated distributions. Structural Safety, 22(1):81–95, 2000. [26] S. Gunawan and P. Papalambros. A bayesian approach to reliability-based optimization with incomplete information. Journal of Mechanical Design, 128(4):900–918, 2006. [27] I. Lee. Reliability-Based Design Optimization and Robust Design Optimization using Univariate Dimension Reduction Method. ProQuest LLC, 2008. [28] P.-Y. Lin. Optimal sampling augmentation and resource allocation for design with inadequate uncertainty. Master’s thesis, National Cheng Kung University. [29] Y.-C. Han. A bayesian based updated scheme for belt pulley system design with censored life data. Master’s thesis, National Cheng Kung University. [30] S. P. Kavulya, K. J., F. D. Giandomenico, and P. Narasimhan. Failure diagnosis of complex systems. In K. Wolter et al., editor, Resilience Assessment and Evaluation of Computing Systems. Springer. [31] C. M. Douglas and C. R. George. Applied Statistics and Probability for Engineers. John Wiley & Sons, Inc., 2011. [32] Military Handbook Reliability Prediction of Electronics Equipment. MIL-HDBK- 217F, 1995. [33] M.J. Cushing, D.E. Mortin, T.J. Stadterman, and A. Malhotra. Comparison of electronics-reliability assessment approaches. Reliability, IEEE Transactions, 42(4):542–546, Dec 1993. [34] G.-Q. Lu, X.-S. Liu, S.-H. Wen, J.N. Calata, and J.G. Bai. Strategies for improving the reliability of solder joints on power semiconductor devices. Soldering & Surface Mount Technology, 16:27–40, 2004. [35] T.E. Wong, C.Y. Lau, and H.S. Fenger. Cbga solder joint thermal fatigue life estimation by a simple method. Soldering & Surface Mount Technology, 16:41–45, 2004. [36] J.C. Suhling, H.S. Gale, R.W. Johnson, M.N. Islam, T. Shete, P. Lall, M.J. Bozack, J.L. Evans, P. Seto, T. Gupta, and J.R. Thompson. Thermal cycling reliability of leadfree chip resistor solder joints. Soldering & Surface Mount Technology, 16:77–87, 2004. [37] F. Liu, G. Meng, M. Zhao, and J. f. Zhao. Experimental and numerical analysis of bga lead-free solder joint reliability under board-level drop impact. Microelectronics Reliability, 49(1):79 – 85, 2009. [38] P. Chauhan, M. Pecht, M. Osterman, and S.W.R. Lee. Critical review of the engelmaier model for solder joint creep fatigue reliability. Components and Packaging Technologies, IEEE Transactions, 32(3):693–700, Sept 2009. [39] P. Chauhan, S. Mathew, M. Osterman, and M. Pecht. In situ interconnect failure prediction using canaries. Device and Materials Reliability, IEEE Transactions, 14(3):826–832, Sept 2014. [40] W. Engelmaier. Fatigue life of leadless chip carrier solder joints during power cycling. Components, Hybrids, and Manufacturing Technology, IEEE Transactions, 6(3):232–237, Sep 1983. [41] D.-S. Liu and C.-Y. Ni. A study on the electrical resistance of solder joint interconnections. Microelectronic Engineering, 63(4):363 – 372, 2002. [42] 1206 thick film chip resistor datasheet. Technical report, State of the art, Inc., 2008. [43] Lm2757 capacitor datasheet. Technical report, Texas Instruments, 2007. [44] 1n4001-1n4007 datasheet. Technical report, Diodes Incorporated. [45] The top five things that cause inverter failure, 2014. http://www.automation.com/ automation-news/article/the-top-five-things-that-cause-inverter-failure. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/4469 | - |
dc.description.abstract | 隨著汽車智慧化及駕駛自動化的發展,電子系統的可靠度對汽車安全性有著越來越大的影響力。電子系統的運用能協助偵側危險的駕駛行為同時也能適時的輔助一般駕駛者,但這些提升駕駛安全的功能往往因為電子系統在嚴苛的環境下行駛導致可靠度降低進而影響汽車安全性。
現存的可靠度分法如故障樹分析及失效模式與影響分析可以協助了解系統破壞的因果關係;馬可夫模型及可靠度分配透過可靠度的計算及量測幫助我們量化失效模式的發生機率。 儘管有許多可靠度相關的研究及分析方法,但對電子系統這種失效原因及結果相對複雜的系統而言,要找出每一種失效背後的原因仍是相當困難的,同時現實中對系統失效原因的診斷往往會因為高昂的量測成本而無法達成。 因此本研究欲提出一個找出電子系統最有可能的失效原因的診斷方法:透過物理破壞的角度,分析當焊接點受到環境及人為不確定因素產生電阻偏差值如何影響系統可靠度並找出客觀的失效原因排序;設計者可透過此分析結果進行量測,並透過貝氏更新法快速更新診斷結果,經由慢慢增加量測樣本直到診斷結果及結論得以建立。 本研究使用增壓器及變壓(頻)器系統做為範例演示整體診斷過程,過程中透過考量環境溫度及電子原件本身的發熱,可以得知電子系統的可靠度會因為開發階段不同的電路板設計和電子原件位置配置而受到影響,透過更改電路的原件配置,可將系統可靠度大幅提升。 本研究將分析診斷結果和`失效模式與影響分析表格(FMEA)'做整合,提供一個明確的系統可靠度改善方向;透過本研究提出的方法可以幫助電子系統在設計開發階段重新檢驗並提升可靠度,並使使用大量複雜電子系統的產品如汽車能在實際使用實能有更高的可靠度。 | zh_TW |
dc.description.abstract | The reliability of electrical systems on modern vehicles has an increasing impact on the on-road safety with then increase of smart technology implementations. These electrical systems help detecting dangerous driver behaviors, alleviate driving errors, prevent unintended actions, as well as provide alternatives to internal combustion engines. The goals of having more efficient and safer vehicles could be undermined by the low reliability of electrical systems at severe driving environment.
Existing reliability assessment methods, such as fault tree analysis and failure mode and effect analysis, focus on the cause and effects of component failure on system faults. On the other hands, Markov-chain based methods and reliability allocation techniques quantifies how these failure modes propagates within a complex system using reliability measure. Albeit abundant research activities, diagnosing the true origin of a system fault among all possible causes can be challenging. Incorporating these results for reliability improvements requires measurements that are costly in product development. Therefore in this research we develop a method to identifying the most likely origin of a electrical system fault with incremental data using Bayesian concept. We incorporate physics of failure in the welding joints of each components and consider the performance variations within each components. The result will be a subjective probability measure to rank the relative likely cause of a fault under varying environmental conditions. Designers can then use this result to sequentially identifying or measuring the health of each components until a conclusion is made. To deal with reliability with limited measurement samples, a reliability evaluating and updating scheme via Bayesian inference is established. We demonstrate the validity of the proposed method via a boost converter and an inverter. Consider the product development stage results in a electric layout that is to be realized. We show that due to the ambient temperature gradient and the rise of component temperature in operation, the reliability of a circuit rely on its configuration. We also use the examples to show the effect of the sample size on our probability measure. Combined with FMEA, the proposed method can help re-examine the electrical system in the earliest design stage toward high reliability target. For modern vehicles with a large number of complex electrical systems, our method can help improving the final reliability in real operation. | en |
dc.description.provenance | Made available in DSpace on 2021-05-14T17:42:29Z (GMT). No. of bitstreams: 1 ntu-104-R02522633-1.pdf: 3501451 bytes, checksum: 49e88212590fa217f70d95310869f45d (MD5) Previous issue date: 2015 | en |
dc.description.tableofcontents | 誌謝iii
摘要v Abstract vii 1 Introduction 1 1.1 Research background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Thesis organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 System Reliability Assessment and Diagnosis Methods 5 2.1 System reliability assessment and diagnosis . . . . . . . . . . . . . . . . 5 2.2 Methods mainly used to understand input and output relation . . . . . . . 8 2.2.1 Fault Tree Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.2 Failure Mode and Effect Analysis . . . . . . . . . . . . . . . . . 10 2.3 Methods mainly used to predict the foresee failures of system . . . . . . . 11 2.3.1 System Prognosis . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.4 Methods mainly used to assess reliability of complex system . . . . . . . 12 2.4.1 Markov Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4.2 Petri net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4.3 Reliability Block Diagram . . . . . . . . . . . . . . . . . . . . . 14 2.5 Reliability assessment in measurement field with inadequate data . . . . . 15 2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3 Bayes inference in Reliability Assessment 19 3.1 Bayesian reliability update scheme . . . . . . . . . . . . . . . . . . . . . 19 3.1.1 Bayes theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.1.2 Conjugate prior in Bayes inference . . . . . . . . . . . . . . . . . 21 3.1.3 Beta-Binomial inference . . . . . . . . . . . . . . . . . . . . . . 22 3.2 Reliability estimation via Bayes inference . . . . . . . . . . . . . . . . . 25 3.2.1 Reliability estimation . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2.2 Confident range . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.3 Reliability estimation example . . . . . . . . . . . . . . . . . . . . . . . 27 4 Proposed Diagnosis Method 29 4.1 Overall diagnosis flowchart . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.2 Diagnosis concept: Physics of failure . . . . . . . . . . . . . . . . . . . 31 4.3 Diagnosis methodology and process . . . . . . . . . . . . . . . . . . . . 32 4.3.1 Model of solder joint resistance . . . . . . . . . . . . . . . . . . 32 4.3.2 Analysis the critical region of certain failure modes . . . . . . . . 34 4.3.3 Analysis the probability of failure modes occur . . . . . . . . . . 35 4.3.4 Suggestion on new measurement . . . . . . . . . . . . . . . . . . 36 4.3.5 Diagnosis via Bayesian update scheme . . . . . . . . . . . . . . 37 4.3.6 Reliability improvement . . . . . . . . . . . . . . . . . . . . . . 37 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 5 Case Study 39 5.1 Diagnosis of failure modes on electrical vehicle . . . . . . . . . . . . . . 39 5.1.1 Identifying failure modes & possible causes . . . . . . . . . . . . 39 5.1.2 Failure due to boost converter . . . . . . . . . . . . . . . . . . . 40 5.1.3 Failure due to inverter . . . . . . . . . . . . . . . . . . . . . . . 49 5.1.4 Diagnosis result . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.2 Diagnosis of high reliability system . . . . . . . . . . . . . . . . . . . . 58 5.2.1 Diagnosis process in analysis stage . . . . . . . . . . . . . . . . 58 5.2.2 Diagnosis and reliability update in measurement field . . . . . . . 59 5.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 6 Conclusion and Future Work 61 6.1 Conclusion remark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 6.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Bibliography 63 | |
dc.language.iso | en | |
dc.title | 用於提升電子系統可靠度之貝氏失效診斷法 | zh_TW |
dc.title | A Bayesian-Based Fault Diagnosis Method for Reliability Improvement of Electric System | en |
dc.type | Thesis | |
dc.date.schoolyear | 103-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 鄭榮和,吳文方,屈子正 | |
dc.subject.keyword | 可靠度,車用電子系統,物理破壞,量測樣本,貝氏更新法,FMEA, | zh_TW |
dc.subject.keyword | Reliability,Vehicle electrical system,Physics of failure,Measurement data,Bayesian inference,FMEA, | en |
dc.relation.page | 67 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2015-08-17 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
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-104-1.pdf | 3.42 MB | Adobe PDF | 檢視/開啟 |
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。