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Author:

Guo, Yiliang (Guo, Yiliang.) | Fan, Qingwu (Fan, Qingwu.) | Liu, Xudong (Liu, Xudong.)

Indexed by:

EI

Abstract:

Aiming at the large amount of data and complex structure of the heating pipe network system, this paper studies the fault diagnosis method of heating pipe network leakage based on deep belief network. Firstly, using the theory of graph theory to establish the hydraulic calculation model of the leakage condition of the heating pipe network, then according to the pressure change value of the pressure monitoring point in the pipe network; the deep belief network is used to establish the fault diagnosis model of the heating pipe network leakage, and the feasibility of the method is verified by experiments. The experimental results show that the proposed method has higher accuracy for the prediction of leakage pipe segments, and is superior to BP (Back Propagation Neural Network) neural network and support vector machine traditional fault diagnosis method. © 2019 IEEE.

Keyword:

Backpropagation Heating Graph theory Neural networks Fault detection Failure analysis Support vector machines

Author Community:

  • [ 1 ] [Guo, Yiliang]Information Department, Beijing University of Technology, Beijing, China
  • [ 2 ] [Fan, Qingwu]Information Department, Beijing University of Technology, Beijing, China
  • [ 3 ] [Liu, Xudong]Information Department, Beijing University of Technology, Beijing, China

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Source :

Year: 2019

Page: 303-304

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 7

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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