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Fault diagnosis for centrifugal refrigeration system based on probabilistic neural network.

[In Chinese. / En chinois.]

Author(s) : LIANG Q., HAN H., CUI X.

Type of article: Article

Summary

Applies the probabilistic neural network (PNN) to diagnose seven types of typical faults for a refrigeration system, including system-level faults and component-level faults. Elaborates the establishment of the fault diagnosis model based on PNN and the optimal processes of finding out the best spread value in detail. Studies the influence of sample size on the best spread value and the correct diagnose rate. Compares the performance of the PNN and the prevailing back-propagation (BP) neural network. The results show that the overall correct diagnosis rate of the PNN model is 3.48% higher than that of the BP network, which consumes much less diagnosing time, and the diagnosis of single training with the PNN is more reliable than that of the BP network. Although the diagnosis results of these two networks show that the system-level faults is more difficult to be identified than the component-level faults, great improvement still has been observed by using PNN.

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Pages: 101-107

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Details

  • Original title: [In Chinese. / En chinois.]
  • Record ID : 30018381
  • Languages: Chinese
  • Source: HV & AC - vol. 45 - n. 311
  • Publication date: 2015/11

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