Fluid Mechanics and Flight Mechanics

Fusion diagnosis method based on neighboring modular weighted D-S

  • HU Jinhai ,
  • XIA Chao ,
  • PENG Jingbo ,
  • ZHANG Yu ,
  • REN Litong
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  • 1. Aeronautics and Astronautics Engineering Institute, Air Force Engineering University, Xi'an 710038, China;
    2. Co-Innovation Center for Advanced Aero-Engine, Beijing 100083, China

Received date: 2015-04-01

  Revised date: 2015-06-17

  Online published: 2015-08-03

Supported by

National Natural Science Foundation of China (51105374);Aeronautical Science Foundation of China (20142196019);Natural Science Basic Research Plan in Shaanxi Province of China (2015JM5207)

Abstract

The traditional Dempster-Shafter (D-S) method has disadvantages in dealing with the sensors fusion problems whose decision results are highly deviated and contradicted; therefore, it will also fail in the fusion diagnosis of rotor weak fault in the situation of high information conflict. According to the related ideas and concepts of opinion propagation of complex network, social learning theory and consensus decision of multi-agent, and with a focus on avoiding the direct fusion of the sensors which have great conflict, this paper proposes a new fusion diagnosis method based on neighboring modular weighted D-S. Firstly, we judge whether the sensors are adjacent to each other based on the preliminary results. Only for the neighboring sensors whose distance is within the limited range can the fusion be carried out. For the neighboring sensors which are in the same module, we carry out the fusion based on weighted D-S fusion method according to the preliminary decision results and the weights of different sensors. Subsequently, according to the obtained fusion result, we carry out the module division and fusion again. We repeat the above steps until all the nodes and modules are not neighboring, then output the decision result of a module, whose weights' summation is the maximum. Finally, we use expert authority's decision method to determine the final weight and fusion result, which are used as the consensus decision result of the sensor network. The method is tested through a simulation test of rotor fault and it proves that the presented method can effectively deal with the fusion diagnosis of rotor weak fault in the situation of fusion diagnosis of rotor weak fault with high information conflict.

Cite this article

HU Jinhai , XIA Chao , PENG Jingbo , ZHANG Yu , REN Litong . Fusion diagnosis method based on neighboring modular weighted D-S[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016 , 37(4) : 1174 -1183 . DOI: 10.7527/S1000-6893.2015.0187

References

[1] SI L, WANG Z B, TAN C, et al. A novel approach for coal seam terrain prediction through information fusion of improved D-S evidence theory and neural network[J]. Measurement, 2014, 54:140-151.
[2] YANG L, LEE J. Bayesian belief network-based approach for diagnostics and prognostics of semiconductor manufacturing system[J]. Robotics and Computer-Integrated Manufacturing, 2012, 28(1):66-74.
[3] HE J P, TU Y Y, SHI Y Q. Fusion model of multi monitoring points on dam based on bayes theory[J]. Procedia Engineering, 2011, 15:2133-2138.
[4] BASIR O, YUAN X H. Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory[J]. Information Fusion, 2007, 8(4):379-386.
[5] MOOSAVIAN A, KHAZAEE M, NAJAFI G, et al. Spark plug fault recognition based on sensor fusion and classifier combination using Dempster-Shafer evidence theory[J]. Applied Acoustics, 2015, 93:120-129.
[6] XU C, ZHANG H, PENG D G, et al. Study of fault diagnosis of integrate of D-S evidence theory based on neural network for turbine[J]. Energy Procedia, 2012, 16:2027-2032.
[7] FAN X F, ZUO M J, Fault diagnosis improved D-S of machines based on D-S evidence theory:Part 2. Application of the improved D-S evidence theory in gearbox fault diagnosis[J]. Pattern Recognition of the Letters, 2006, 27(5):377-385.
[8] 高峰, 唐卓贞. 基于D-S证据理论的船舶电子设备状态预测方法[J]. 船电技术, 2011(2):45-48. GAO F, TANG Z Z. Status prediction algorithm for electronic equipment based on the D-S evidential theory[J]. Marine Electric Technicial, 2011(2):45-48(in Chinese).
[9] 胡金海, 谢寿生, 骆广琦, 等. 基于Dempster-Shafer证据理论的航空发动机磨损状况融合诊断[J]. 机械科学与技术, 2008, 27(3):343-346. HU J H, XIE S S, LUO G Q et al. Fusion diagnosis of aero-engine wearing condition based on Dempster-Shafer proof theory[J]. Mechanical Science and Technology for Aerospace Engineering, 2008, 27(3):343-346(in Chinese).
[10] 杨建平, 黄洪钟, 苗强, 等. 基于证据理论的航空发动机早期故障诊断方法[J]. 航空动力学报, 2008, 23(12):2327-2331. YANG J P, HUANG H Z, MIAO Q, et al. Diagnosis method of aeroengine early fault based on the Dempster-Shafer evidence theory[J]. Journal of Aerospace Power, 2008, 23(12):2327-2331(in Chinese).
[11] FAN X F, ZUO M J. Fault diagnosis of machines based on D-S evidence theory:Part 1. D-S evidence theory and its improvement[J]. Pattern Recognition Letters, 2006, 27(5):366-376.
[12] 胡金海, 余治国, 翟旭升, 等. 基于改进D-S证据理论的航空发动机转子故障决策融合诊断研究[J]. 航空学报, 2014, 35(2):436-443. HU J H, YU Z G, ZHAI X S, et al. Research of decision level fusion diagnosis of aeroengine rotor fault based on improved D-S theory[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(2):436-443(in Chinese).
[13] 李军, 锁斌, 李顺. 基于证据理论的多传感器加权融合改进方法[J]. 计算机测量与控制, 2011, 19(10):2592-2595. LI J, SUO B, LI S. Improved multi-sensor weighted fusion method based on evidence theory[J]. Computer Measurement & Control, 2011, 19(10):2592-2595(in Chinese).
[14] 谭青, 向阳辉. 加权证据理论信息融合方法在故障诊断中的应用[J]. 振动与冲击, 2008, 27(4):112-116. TAN Q, XIANG Y H. Application of weighted evidential theory and its information fusion method in fault diagnosis[J]. Journal of Vibration and Shock, 2008, 27(4):112-116(in Chinese).
[15] 梁威, 魏宏飞, 周锋. D-S证据理论中一种冲突证据的融合方法[J]. 计算机工程与应用, 2011, 47(6):144-147. LIANG W, WEI H F, ZHOU F. Fusion method of conflict evidence in D-S theory[J]. Computer Engineering and Applications, 2011, 47(6):144-147(in Chinese).
[16] 王茹. 复杂网络Opinion动力学研究[D]. 武汉:华中师范大学, 2009:39-76. WANG R. Opinion dynamics on the complex networks. Wuhan:Central China Normal University, 2009:39-76(in Chinese).
[17] LIU P Q, WANG X F. Social learning with bounded confidence and heterogeneous agents[J]. Physica A, 2013,392:2368-2374.
[18] 杨文. 多智能体系统一致性问题研究[D]. 上海:上海交通大学, 2009:77-85. YANG W. Consensus problem in multi-agent systems[D]. Shanghai:Shanghai Jiao Tong University, 2009:77-85(in Chinese).
[19] 徐玖平, 陈建中. 群决策理论与方法及实现[M]. 北京:清华大学出版社, 2009:332-347. XU J P, CHEN J Z. The theory and methods of group decision making with its realization[M]. Beijing:Tsinghua University Press, 2009:332-347(in Chinese).
[20] HU J H, REN L T, ZHANG Y, et al.The methods of establishing of aero-engine vibration sensor network sensitive factor based on multi-feature fusion and transformation[C]//The 5th International Symposium on Jet Propulsion and Power Engineering. 2014.
[21] 翟旭升, 胡金海, 谢寿生, 等. 基于DSmT的航空发动机早期振动故障融合诊断方法[J]. 航空动力学报, 2012, 27(2):301-306. ZHAI X S, HU J H, XIE S S, et al. Diagnosis of aero-engine with early vibration fault symptom using DSmT[J]. Journal of Aerospace Power, 2012, 27(2):301-306(in Chinese).

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