The Engine Fault Diagnosis with RBFNN Based on AIC

Qiang Huang; Xiao Zhuo Ouyang; Cheng Wang

doi:10.4028/www.scientific.net/AMR.468-471.1066

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 468-471The Engine Fault Diagnosis with RBFNN Based on AIC

The Engine Fault Diagnosis with RBFNN Based on AIC

Abstract:

In this paper, an engine diagnosis method with high precision and quickly response is proposed. Firstly, the Akaike Information Criterion (AIC) is used to improve the performance of the neural network to build the fault diagnosis model. Then the vibration signals are analyzed to estimate the states of the diesel engine. Finally, the five states of diesel engine are set to validate the veracity of diagnosis method. According to experiment and simulation researches, it indicates that the diagnosis method with RBF neural network based on AIC is effective. The veracity of identification is 100% to the single fault. It is a valuable reference to the vibration diagnosis for other complex rotary machines.

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Periodical:

Advanced Materials Research (Volumes 468-471)

Pages:

1066-1069

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.468-471.1066

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Online since:

February 2012

Authors:

Qiang Huang, Xiao Zhuo Ouyang, Cheng Wang

Keywords:

Akaike Information Criterion, Fault Diagnosis, Neural Network (NN)

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References

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