A Maximum Entropy Multisource Information Fusion Method to Evaluate the MTBF of Low-Voltage Switchgear
作者： Jing-Qin Wang , Zhi-Gang Zhang , Ching-Hsin Wang , Li Wang
DOI: 10.1155/2018/2746871
关键词: Posterior probability 、 Computer science 、 Bayesian probability 、 Principle of maximum entropy 、 MATLAB 、 Test method 、 Mean time between failures 、 Reliability engineering 、 Switchgear 、 Bayesian network
摘要: When analyzing the reliability of low-voltage switchgear by Bayesian method, maximum entropy multisource information fusion method was proposed to obtain prior and then evaluate reliability. The historical data collected organized from a manufacturer. According expert experience data, creditability analysis compatibility test were presented Smirnov method. Based on high compatibility, result is determination information. Therefore, distribution type confirmed using parameter received bootstrap with MATLAB. Then posterior obtained MTBF switchgear. Finally, years 2007 2010 taken as illustrate get evaluation reduces experimental period cost, which an improvement for management also other systems simple sample data. Compared traditional networks, can fuse experts has advantages use effectively.
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core.ac.uk 参考文章(26)
Ioannis Rigas, Evgeniy Abdulin, Oleg Komogortsev, Towards a multi-source fusion approach for eye movement-driven recognition Information Fusion. ,vol. 32, pp. 13- 25 ,(2016) , 10.1016/J.INFFUS.2015.08.003
Guang Cheng, Moment Consistency of the Exchangeably Weighted Bootstrap for Semiparametric M-estimation Scandinavian Journal of Statistics. ,vol. 42, pp. 665- 684 ,(2015) , 10.1111/SJOS.12128
Stefan Weber, Distribution-Invariant Risk Measures, Entropy, and Large Deviations Journal of Applied Probability. ,vol. 44, pp. 16- 40 ,(2007) , 10.1239/JAP/1175267161
Rami Issa Al-Ruzouq, Data fusion of multi-source imagery based on linear features registration Journal of remote sensing. ,vol. 31, pp. 5011- 5021 ,(2010) , 10.1080/01431160903193505
Amit Kumar Singh, Harendra Pratap Singh, Karmeshu, Analysis of Finite Buffer Queue: Maximum Entropy Probability Distribution With Shifted Fractional Geometric and Arithmetic Means IEEE Communications Letters. ,vol. 19, pp. 163- 166 ,(2015) , 10.1109/LCOMM.2014.2377236
Baoping Cai, Yonghong Liu, Qian Fan, Yunwei Zhang, Zengkai Liu, Shilin Yu, Renjie Ji, None, Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network Applied Energy. ,vol. 114, pp. 1- 9 ,(2014) , 10.1016/J.APENERGY.2013.09.043
Ambedkar Dukkipati, Shalabh Bhatnagar, M. Narasimha Murty, On measure-theoretic aspects of nonextensive entropy functionals and corresponding maximum entropy prescriptions Physica A-statistical Mechanics and Its Applications. ,vol. 384, pp. 758- 774 ,(2007) , 10.1016/J.PHYSA.2007.05.020
Jens-Peter Kreiss, Efstathios Paparoditis, Bootstrap methods for dependent data: A review Journal of the Korean Statistical Society. ,vol. 40, pp. 357- 378 ,(2011) , 10.1016/J.JKSS.2011.08.009
Lingling Li, Dongjuan Ma, Zhigang Li, Cox-Proportional Hazards Modeling in Reliability Analysis—A Study of Electromagnetic Relays Data IEEE Transactions on Components, Packaging and Manufacturing Technology. ,vol. 5, pp. 1582- 1589 ,(2015) , 10.1109/TCPMT.2015.2480229
Bogdan Grechuk, Anton Molyboha, Michael Zabarankin, Maximum Entropy Principle with General Deviation Measures Mathematics of Operations Research. ,vol. 34, pp. 445- 467 ,(2009) , 10.1287/MOOR.1090.0377