The selection of electrical equipment condition-based maintenance alternatives is a multi-attribute decision-making problem. Choosing the proper maintenance scheme can not only accurately grasp the operation state of power equipment, but also weaken the blindness of maintenance work and improve economic benefits. Therefore, it is particularly important to choose a scientific decision-making method. In this paper, a multi-attribute decision-making method based on cloud model and grey D-S evidence theory is proposed. Firstly, cloud model is applied to deal with qualitative criteria, which reduces the fuzziness and randomness of qualitative language and remains linguistic information as much as possible in the transformation process. Secondly, on the basis of the concept of grey correlation degree, a new method to calculate basic probability assignment (BPA) or mass function in D-S evidence theory is presented which diminishes the grey character in decision-making process. Finally, the example analysis and sensitivity analysis verify the effectiveness and practicability of the proposed model.
TsangAHC. Condition-based maintenance: Tools and decision making. J Qual Maint Eng. 1995; 1(3): 3–17.
2.
PillayAWangJ. Modified failure mode and effects analysis using approximate reasoning. Reliab Eng Syst Saf. 2003 Jan; 79(1): 69–85.
3.
LiBHuangM. Underground Risk Assessing Method Based on Rough Set and D-S Evidence Theory. Ind Mine Autom. 2011; (11): 38–40.
4.
ZhangCHuYChanFTSSadiqRDengY. A new method to determine basic probability assignment using core samples. Knowl-Based Syst. 2014 Oct; 69: 140–9.
5.
LiDLiuCGanW. A new cognitive model: cloud model. Int J Intell Syst. 2009 Mar; 24(3): 357–75.
6.
WangDLiuDDingHSinghVPWangYZengX, et al. A cloud model-based approach for water quality assessment. Environ Res. 2016 Jul; 148: 24–35.
7.
PearceDwAtkinsonGMouratoS. Cost-benefit Analysis and the Environment: Recent Developments [Internet]. 2006 [cited 2017 Jun 13]. Available from: https://trid.trb.org/view/795431.
8.
SabahiFAkbarzadeh-TM-R. A qualified description of extended fuzzy logic. Inf Sci. 2013 Sep; 244: 60–74.
9.
WangGXuCZhangQWangX. A Multi-step Backward Cloud Generator Algorithm. In: YaoJYangYSłowińskiRGrecoSLiHMitraS, et al., eds. Rough Sets and Current Trends in Computing [Internet]. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012 [cited 2016 Dec 8]. p. 313–22. Available from: http://link.springer.com/10.1007/978-3-642-32115-3_37.
10.
WeiHZhengY. Congestion prediction based on rough set and D-S theory. J Fuzhou Univ Sci Ed. 2008; (1): 69–72.
11.
DassowJManeaFMercaşR. Regular languages of partial words. Inf Sci. 2014 Jun; 268: 290–304.
12.
FanJLiangR-Z. Stochastic learning of multi-instance dictionary for earth movers distance-based histogram comparison. 2016; 1–11.
13.
WangJPengLZhangHChenX. Method of multi-criteria group decision-making based on cloud aggregation operators with linguistic information. Inf Sci. 2014 Aug; 274: 177–91.
14.
YuanJLiC. Risk Assessment of Power Grid Enterprises Information Security Based On Grey Incidence and D-S Evidence Theory. Shanxi Electr Power. 2014; (2): 11–5.
15.
ZhuJGaoHLiC. Information security risk evaluation based on D-S evidence theory. J North China Electr Power Univ. 2008; (04): 102–8.
16.
AlexandridisKMaruY. Collapse and reorganization patterns of social knowledge representation in evolving semantic networks. Inf Sci. 2012 Oct; 200: 1–21.
17.
HanLZhouF. Knowledge fusion based on D-S evidence theory and its application. J Beijing Univ Aeronaut Astronaut J Beijing Univ Aeron Astron. 2006; (1): 65–68+73.
18.
HaoLWangHZhangCLiYHeB. Multi-objective and group decision-making research based on D-S evidence theory in condition-based maintenance for transmission and transformation equipment. Dianwang Jishu Power Syst Technol. 2014; 38(6): 1627–33.
19.
LiLZhangL. Fault Diagnosis of Induction Motor Rotor Based on Gray Correlation and D-S Evidence Theory. Comput Meas Control Comput Meas Contrl. 2012; (6): 1492–4.
20.
TianLXingJ-G. Discussion on making decision about electric equipment for condition based maintenance. Power Syst Technol. 2004; 28(16): 60–3.
21.
YangL-JXiongX-FZhangY. Research on condition-based maintenance policy of electric power equipment based on grey correlation degree and TOPSIS. Dianli Xitong Baohu Yu KongzhiPower Syst Prot Control. 2009; 37(18): 74–8.
22.
WangMTangDBaiYXiaZ. A compound cloud model for harmoniousness assessment of water allocation. Environ Earth Sci [Internet]. 2016 Jun [cited 2016 Dec 8]; 75(11). Available from: http://link.springer.com/10.1007/s12665-016-5782-3.
23.
BellmanREZadehLA. Decision-making in a fuzzy environment. Manag Sci. 1970; 17(4): b-141-64.
24.
LiangRShiLWangHMengJWangJJ-YSunQ, et al. Optimizing top precision performance measure of content-based image retrieval by learning similarity function. In: 23rd International Conference on Pattern Recognition, ICPR 2016, December 4, 2016–December 8, 2016. Institute of Electrical and Electronics Engineers Inc.; 2017. p. 2954–8. (Proceedings – International Conference on Pattern Recognition).
25.
LiangRXieWLiWWangHWangJJ-YTaylorL. A novel transfer learning method based on common space mapping and weighted domain matching. In: 28th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2016, November 6, 2016–November 8, 2016. Institute of Electrical and Electronics Engineers Inc.; 2016. p. 299–303. (Proceedings – 2016 IEEE 28th International Conference on Tools with Artificial Intelligence, ICTAI 2016).
26.
LiRTangLLingWLiZWangW. Risk decision-making based on cloud theory and prospect theory for conditional maintenance of power transformer. Electr Power Autom Equip. 2013 Feb; 33(2): 104–8.
27.
JiangSWangHYangN. The Algorithm of Mass Function Based Entropy and its Application in Fault Diagnosis of Hydraulic Pumps. Mach Tool Hydraul Mach Tool Hydrau. 2007; (12): 185–7.
28.
DenoeuxT. A neural network classifier based on Dempster-Shafer theory. IEEE Trans Syst Man Cybern Part Syst Hum. 2000 Mar; 30(2): 131–50.
29.
LiTFengQZhangK. Threat assessment based on entropy weight grey incidence and D-S theory of evidence. Appl Res Comput. 2013; (2): 380–2.
30.
HeXTongXSunM. Distributed Power System Fault Diagnosis Based on Bayesian Network and Dempster-Shafer Evidence Theory. Autom Electr Power Syst. 2011; (10): 42–7.
31.
LiuYLiuZChenJ. Auxiliary Decision Method for Power Grid Fault Diagnosis Based on D-S Evidence Theory. East China Electr Power. 2014; (12): 2842–6.
32.
ZhangYXiongXZhouJHuZWangR. Research on sequence of maintenance of circuit breakers based on grey fuzzy comprehensive evaluation. Power Syst Technol. 2008 Apr 20; 32(8): 21–4.
33.
YuanZSunCLiALiJLiaoR. Assessment on condition based maintenance policy of transformer using fuzzy multiple attribute desion making. High Volt Eng. 2004; (8): 33–5.
34.
YuanZSunCLiJDuLLiaoR. Study on condition-based maintenance policy of transformer based on fuzzy multiple expert and multiple attribute group decision making. Autom Electr Power Syst. 2004 Jun; 28(11): 66–70.
