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

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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

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