Intelligent Diagnosis of Aircraft Electrical Faults Based on RMBP Neural Network

doi:10.16182/j.issn1004731x.joss.201809034

Abstract

Abstract: To the characteristics of multiple properties, hard to remove and high cost of time and manpower of aircraft electrical faults maintenance in aircraft maintenance of civil aviation, construction of intelligent aircraft electrical faults diagnosis system using RMBP neural network is proposed. RMBP algorithm is used to study sample data in the intelligent faults diagnosis system as it can overcome the faults of long time of convergence and easy to go into local minima of common BP algorithm, and is suitable for training large-scale neural network,. Experience data are collected, samples are made, samples training and experiment are carried out. Results of experiment show that intelligent diagnosis system of aircraft electrical faults can diagnose the faults of test samples correctly, which verifies that it can meet the requirement of aircraft electrical faults maintenance of civil aviation.

Key words: RMBP neural network, expert system, machine learning, faults diagnosis, aircraft electrical faults maintenance of civil aviation

CLC Number:

Jia Lishan, Liu Zhe, Sun Yi. Intelligent Diagnosis of Aircraft Electrical Faults Based on RMBP Neural Network[J]. Journal of System Simulation, 2018, 30(9): 3493-3501.

References

[1] 杨慧, 刘玉洁, 吴俊. 基于FBP神经网络的航空发动机故障诊断[J]. 计算机工程, 2005, 31(17): 187-188.
YANG Hui, LIU Yujie, WU Jun.Failure-diagnosis of Aero Engines Based on FBP Neural Network[J]. Computer Engineering, 2005, 31(17): 187-188.
[2] 杨慧, 邓征. 基于自适应神经网络的飞机突发故障诊断研究[J]. 计算机工程与设计, 2014, 35(3): 1010-1015.
YANG Hui, DENG Zheng.Adaptive neural network based diagnosis research of burst fault[J]. COMPUTER ENGINEERING AND DESIGN, 2014, 35(3): 1010-1015.
[3] 阳同光, 蒋新华, 付强. 混合蛙跳脊波神经网络观测器电机故障诊断研究[J]. 仪器仪表学报, 2013, 34(1): 193-199.
Yang Tongguang, Jiang Xinhua, Fu Qiang.Study on motor fault diagnosis using ridgelet neural network observer optimized by shuffled frog leaping algorithm[J]. Chinese Journal of Scientific Instrument, 2013, 34(1): 193-199.
[4] 周国雄, 廖迎新, 沈学杰. 基于神经网络的孵化过程组合预测方法[J]. 系统仿真学报, 2014, 26(4): 892-896.
Zhou Guoxiong, Liao Yingxin, Shen Xuejie.Method of Combination Prediction for Incubator Based on Network[J]. Journal of System Simulation, 2014, 26(4): 892-896.
[5] 苏译, 彭敏放, 朱亮, 等. 基于信息还原与膜计算的配电网故障定位[J]. 仪器仪表学报, 2014, 35(12): 2700-2708.
Su yi, Peng Minfang, Zhu Liang, et al. Fault section location for distribution networks based on information restoration and membrane computing[J]. Chinese Journal of Scientific Instrument, 2014, 35(12): 2700-2708.
[6] 李志农, 朱明, 禇福磊, 等. 基于经验小波变换的机械故障诊断方法研究[J]. 仪器仪表学报, 2014, 35(11): 2423-2432.
Li Zhinong, Zhu Ming, Chu Fulei, et al.Mechanical fault diagnosis method based on empirical wavelet transform[J]. Chinese Journal of Scientific Instrument, 2014, 35(11): 2423-2432.
[7] 丁立伟, 聂婷,李停. 基于BP网络和专家系统的铝点解槽分层故障诊断[J]. 计算机测量与控制, 2014, 22(11): 3476-3479.
Ding Liwei, Nie Ting, Li Ting.Aluminum Cell Hierarchical Fault Diagnosis Method Based on BP Network and Expert System[J]. Computer Measurement & Control, 2014, 22(11): 3476-3479.
[8] 符慧林, 甘国文, 谢七月. 基于神经网络的电站锅炉入炉煤粉含碳量在线测量研究[J]. 计算机测量与控制, 2015, 23(4): 1084-1086, 1105.
Fu Huilin, Gan Guowen, Xie Qiyue.Study on Online Measuring of Carbon Content of Injected Pulverized Coal of Utility Boiler Based on Neural Network[J]. Computer Measurement & Control, 2015, 23(4): 1084-1086, 1105.
[9] 王一卉, 姜长泓. 模糊神经网络专家系统在动力锂电池组故障诊断中的应用[J]. 电测与仪表, 2015, 52(14): 118-123.
Wang Yihui, Jiang Changhong.Fuzzy neural network expert system for fault diagnosis in power lithium battery application[J]. Electrical Measurement & Instrumentation, 2015, 52(14): 118-123.
[10] 江时雨, 陈建宏, 李涛, 等. 水下隧道涌水灾害风险评价与预测[J]. 中国安全科学学报, 2013, 23(12): 101-106.
JIANG Shiyu, CHEN Jianhong, LI Tao, et al.Assessment and Prediction of Water Gushing Disaster Risk in Underwater Tunnel[J]. China Safety Science Journal, 2013, 23(12): 101-106.
[11] 赵淑芳. 基于BP神经网络的煤矿矿用设备安全监测研究[J]. 太原理工大学学报, 2013, 44(5): 619-622.
ZHAO Shufang.The Study of Coal Mine Safety Monitoring Equipment Based on BP Neural Network[J]. Journal of Taiyuan University of Technology, 2013, 44(5): 619-622.
[12] 王赟, 陆国君. 基于小波包分析的风力发电机组故障预诊断神经网络专家系统[J]. 制造业自动化, 2013, 35(18): 147-150.
WANG Yun, LU Guo-jun.The application of wavelet multi-resolution analysis in wind turbine fault-detection[J]. Manufacturing Automation, 2013, 35(18): 147-150.
[13] 杨紫, 陈明锐, 沈重. 基于神经网络的WSN森林火灾预测与定位研究[J]. 计算机仿真, 2015, 32(11): 315-320, 424.
YANG Zi, CHEN Ming-rui, SHEN Chong.Forest Fire Forecasting and Positioning Reseach Based on Neural Network[J]. Computer Simulation, 2015, 32(11): 315-320, 424.
[14] Yongyao Yang, Mahdi Mahfouf, George Panoutsos.Development of a Pasimonious GA-NN ensemble model with a case study for Charpy impact energy prediction[J]. Advances in Engineering Software (S0965-9978), 2011, 42(7): 435-443.
[15] George Panoutsos, Mahdi Mahfouf.A neural-fuzzy modeling framework based on granular computing: Concepts and applications[J]. Fuzzy Sets and Systems (S0165-0114), 2010, 161(21): 2808-2830.
[16] Volodymyr Mnih, Koray Kavukcuoglu, David Silver, et al.Human-level control through deep reinforcement learning[J]. Nature (S0028-0836), 2014, 518(2): 529-533.
[17] Martin Riedmiller, Heinrich Braun.A Direct Adaptive Method for Faster Backpropogation Learning: The RPROP Algorithm[C]// Proceedings of the IEEE International Conference on Neural Networks, 1993. San Francisco, USA: IEEE, 1993: 586-591.
[18] 贾立山, 谈至明, 王知. 基于随机参数调整的改进反向传播学习算法[J]. 同济大学学报(自然科学版), 2011, 39(5): 751-757.
JIA Lishan, TAN Zhiming, WANG Zhi.Modified BP Algorithm Based On Random Adjustment of Parameters[J]. JOURNAL OF TONGJI UNIVERSITY(NATURAL SCIENCE), 2011, 39(5): 751-757.