Computer Science ›› 2017, Vol. 44 ›› Issue (2): 239-243, 266.doi: 10.11896/j.issn.1002-137X.2017.02.039

Previous Articles     Next Articles

Online Detection of Incipient Fault Based on Large-scale Neural Networks

SI Wen-jie and YANG Fei-fei   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Neural networks have been widely used for the system modeling and pattern recognition.However,in order to approximate the unknown parameters or system dynamics,it needs enough neurons to achieve sufficiently accurate approximation,which leads to increase of the computational cost.The computation would restrict the online application of the large-scale neural networks.Because CPU processing cannot keep pace with online data capture,the commonly available graphics processors are used for the bulk of data processing in online systems.First,the input of the system was analyzed by persistent excitation characteristics of RBF neural network,reducing the number of neurons and optimizing design optimization algorithm to improve the approximation error.Secondly,LabVIEW and LabVIEW GPU analysis toolkit were used to achieve algorithm implementation and parallel computing.Finally,online experiment of stall detection was conducted in a low speed axial compressor based on LabVIEW.Experimental results show that the proposed method can meet compressors stall detection of online operating system.

Key words: Neural network,Persistent excitation,LabVIEW,GPU,Large-scale computing,Online experiment

[1] ANDERSON J A.An introduction to neural networks[M].MIT press,1995.
[2] HAGAN M T,DEMUTH H B,BEALA M H.Neural network design[M].Boston:Pws Pub.,1996.
[3] ZENG D,GAO L,LIN L,et al.Application of LabVIEW in online monitoring and automatic control of fermentation process[J].Control & Computer,2006(22):48-50.
[4] HORNG J H.Hybrid MATLAB and LabVIEW with neural network to implement a SCADA system of AC servo motor[J].Advances in Engineering Software,2008,39(3):149-155.
[5] LIU J M,XU Z Z,SUN D H.Virtual instrument and neural network in application of grain moisture detection[C]∥Procee-dings of the 2014 International Conference on Control Enginee-ring and Information Systems.2014:155-158.
[6] LU F S,SONG J Q,YIN F K,et al.Survey of CPU/GPU synergetic parell computing[J].Computer Science,2011,8(3):5-9.(in Chinese) 卢风顺,宋君强,银福康,等.CPU-GPU协同并行计算研究综述[J].计算机科学,2011,38(3):5-9.
[7] GAO Z,CECATI C,DING S X.A survey of fault diagnosis and fault-tolerant techniques-Part I:fault diagnosis With model-based and signal-based approaches[J].IEEE Transactions on Industrial Electronics,2015,62(6):3757-3767.
[8] WU Y,JIANG B,LU N,et al.ToMFIR-based incipient fault de-tection and estimation for high-speed rail vehicle suspension system[J].Journal of the Franklin Institute,2015,352(4):1672-1692.
[9] HWANG W,HUH K.Fault Detection and Estimation for Electromechanical Brake Systems Using Parity Space Approach[J].Journal of Dynamic Systems,Measurement,and Control,2015,137(1):014504.
[10] KURDILA A J,NARCOWICH F J,WARD J D.Persistency of excitation in identification using radial basis function approxi-mants[J].SIAM Journal on Control and Optimization,1995,33(2):625-642.
[11] WANG C,HILL D J.Deterministic learning theory for identification,recognition,and control[M].CRC Press,2009.
[12] POLYCARPOU M M,TRUNOV A B.Learning approach tononlinear fault diagnosis:detectability analysis[J].IEEE Tran-sactions on Automatic Control,2000,45(4):806-812.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .