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北京工业大学 激光工程研究院 北京,100020
纸质出版日期:2018-7-5,
网络出版日期:2018-3-13,
收稿日期:2017-10-21,
修回日期:2018-1-11,
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吴迪, 葛廷武, 秦文斌等. 光纤激光器故障模式分析[J]. 发光学报, 2018,39(7): 1002-1007
WU Di, GE Ting-wu, QIN Wen-bin etc. Fault Mode Analysis on Fiber Laser[J]. Chinese Journal of Luminescence, 2018,39(7): 1002-1007
吴迪, 葛廷武, 秦文斌等. 光纤激光器故障模式分析[J]. 发光学报, 2018,39(7): 1002-1007 DOI: 10.3788/fgxb20183907.1002.
WU Di, GE Ting-wu, QIN Wen-bin etc. Fault Mode Analysis on Fiber Laser[J]. Chinese Journal of Luminescence, 2018,39(7): 1002-1007 DOI: 10.3788/fgxb20183907.1002.
为满足实际工程、生产应用中专业及非专业人员对故障光纤激光器快速修理、修复的需要,研究了一种能够实现快速故障模式及故障原因分析系统。首先,采集实际使用与实验中损坏激光器的故障模式与故障原因记录为数据样本。接着,利用故障树与故障模式影响及危害性分析模型建立光纤激光器扩展故障树,即构建起故障模式与故障原因的对应关系。然后,利用贝叶斯网络分析并得出各故障模式与故障原因的先验概率与后验概率计算方法,再使用Python语言编写交互式窗口,并完成对数据库数据的调用与相应概率计算。最后,通过交互式界面输出当前故障对应的可能故障原因及发生概率,从而实现对光纤激光器故障的快速分析。程序模拟耗时小于1 s,模拟结果与统计结果相吻合,基本满足指导相关人员对故障快速排查并修理的要求。
In order to meet the needs of professional and nonprofessional personnel about fast repairing the fault fiber laser in the practical engineering and production applications
a fast fault mode and fault cause analysis system was reaearched. First
the fault mode and fault cause of the damaged laser in actual use were collected as samples. The extended fault tree of fiber laser was established by the fault tree analysis and failure mode effect criticality analysis
i.e
. the corresponding relationship between failure mode and fault cause was constructed. Then
Bayesian network was used to analyze and obtain the prior probability and posterior probability of each failure mode and fault cause. Using Python to write interactive window and call database data
the corresponding probability was calculated. Finally
the possible fault mode and correspond probability were output through interactive interface
so as to achieve rapid analysis of fiber laser fault. The analysis system simulation results coincide with the reality
calculating less than 1 s
so the system can basically meet the relevant personnel to guide the rapid troubleshooting and repair requirements.
光纤激光器故障树故障模式影响及危害性分析Python
fiber laserfault tree analysisfailure mode effect criticality analysisPython
石永山. 国外光纤激光器研究进展[J]. 光电技术应用, 2013(6):1-5. SHI Y S. Development progress of foreign fiber laser[J]. Electro-opt. Technol. Appl., 2013(6):1-5. (in Chinese)
LIN J, LU D, DAI Y. A review on fiber lasers[J]. China Commun., 2012(8):1-15.
杨青, 俞本立. 光纤激光器的发展现状[J]. 光电子技术与信息, 2002(5):13-18. YANG Q, YU B L. The survey of optic fiber lasers[J]. Optoelectron. Technol. Inform., 2002(5):13-18. (in Chinese)
胡人文. 光纤激光器的应用及发展综述[J]. 科技信息, 2012(33):43-82. HU R W. A review on application and development of fiber laser[J]. Sci. Technol. Inform., 2012(33):43-82. (in Chinese)
AMMAR M, HOQUE K A, MOHAMED O A. Formal analysis of fault tree using probabilistic model checking:a solar array case study[J]. IEEE Xplore, 2016:1-6.
LI Z, GU J, XU T, et al.. Reliability analysis of complex system based on dynamic fault tree and dynamic Bayesian network[J]. IEEE Xplore, 2017:1-6.
方敏, 周书粤, 陈永梅, 等. 故障树结构调整的多值决策图变量排序方法[J]. 西安电子科技大学学报, 2017(6):20-25. FANG M, ZHOU S Y, CHEN Y M, et al.. Variable sorting based on fault tree structure adjustment for multi-valued decision diagrams[J]. J. Xidian Univ., 2017(6):20-25. (in Chinese)
甘传付, 曹宏炳, 黄允华, 等. 基于FMECA、FTA的故障诊断和故障预报[J]. 系统工程与电子技术, 2002, 24(11):127-130. GAN C F, CAO H B, HUANG Y H, et al.. FMECA and FTA based fault diagnosis and fault prognosis[J]. Systems Eng. Electron., 2002, 24(11):127-130. (in Chinese)
MAO C, CANAVERO F. System-level vulnerability assessment for EME:from fault tree analysis to Bayesian networks-Part Ⅰ:methodology framework[J]. IEEE Trans. Electromagn.Compatibil., 2016, 58(1):180-187.
MAO C, CANAVERO F G, CUI Z, et al.. System-level vulnerability assessment for EME:from fault tree analysis to Bayesian networks-Part Ⅱ:illustration to microcontroller system[J]. IEEE Trans. Electromagn.Compatibil., 2016, 58(1):188-196.
倪绍徐, 张裕芳, 易宏, 等. 基于故障树的智能故障诊断方法[J]. 上海交通大学学报, 2008, 42(8):1372-1375. NI S X, ZHANG Y F, YI H, et al.. Intelligent fault diagnosis method based on fault tree[J]. J. Shanghai Jiaotong Univ., 2008, 42(8):1372-1375. (in Chinese)
朱大奇, 于盛林. 基于故障树最小割集的故障诊断方法研究[J]. 数据采集与处理, 2002, 17(3):341-344. ZHU D Q, YU S L. Diagnosis approach based on minimal cut sets of fault trees[J]. J. Data Acquisit. Proc., 2002, 17(3):341-344. (in Chinese)
RAMABHOTLA S, BAYNE S, GIESSELMANN M. Reliability optimization using fault tree analysis in the grid connected mode of microgrid[J]. IEEE Xplore, 2016:136-141.
王广彦, 马志军. 基于贝叶斯网络的故障树分析[J]. 系统工程理论与实践, 2004, 24(6):78-83. WANG G Y, MA Z J. The fault tree analysis based on bayesian networks[J]. System Eng. Theory Practice, 2004, 24(6):78-83. (in Chinese)
VOLK M, JUNGES S, KATOEN J P. Fast dynamic fault tree analysis by model checking techniques[J]. IEEE Trans. Indust. Inform., 2018, 14(1):370-379.
HIRAOKA Y, MURAKAMI T, YAMAMOTO K, et al.. Method of computer-aided fault tree analysis for high-reliable and safety design[J]. IEEE Trans. Reliab., 2016, 65(2):687-703.
AMMAR M, HAMAD G B, MOHAMED O A, et al.. Efficient probabilistic fault tree analysis of safety critical systems via probabilistic model checking[J]. IEEE Xplore, 2017:1-8.
BAKLOUTI A, NGUYEN N, CHOLEY J Y, et al.. Free and open source fault tree analysis tools survey[C]. Systems Conference 2017, Montreal, Canada, 2017:1-8.
周忠宝, 董豆豆. 贝叶斯网络在可靠性分析中的应用[J]. 系统工程理论与实践, 2006, 26(6):95-100. ZHOU Z B, DONG D D. Application of bayesian networks in reliability analysis[J]. System Eng. Theory Practice, 2006, 26(6):95-100. (in Chinese)
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