• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

Computer Engineering & Science

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Working condition diagnosis of oil pump machine based on
continuous process neural network and extreme learning machine

LIU Zhi-gang1,XU Shao-hua2,LI Pan-chi1   

  1. (1.School of Computer and Information Technology,Northeast Petroleum University,Daqing 163318;
    2.College of Information Science and Engineering,Shandong University of Science and Technology,Qingdao
    266590,China)
  • Received:2015-12-28 Revised:2016-03-17 Online:2017-10-25 Published:2017-10-25

Abstract:

To tackle the shortcoming of common neural networks of low diagnostic accuracy in the working condition
diagnosis of the oil pump machine, we propose a method that selects continuous process neural networks as the
diagnostic model. Because the geometric shape characteristics of the indicator diagram are directly reflected
by the continuous signals of move and load, we select them as the feature input of the diagnostic model. In
order to increase the learning speed, we introduce an extreme learning algorithm to process neural networks.
The model training process is converted to a least square problem. The output matrix of the hidden layer is
calculated according to the samples, the Moore-Penrose generalized inverse is solved by the SVD algorithm and
then the output weights of the hidden layer are calculated. Diagnostic experiments show that the learning
speed is increased by about 5 times, the diagnostic accuracy is improved by about 8 percentage in comparison
with common neural networks, and the validity of the method is verified.

 

Key words: working condition diagnosis, process neural network, extreme learning, moore-Penrose generalized inverse, network