Fault Prediction Method Research of the Power Plant Fan

Jian Ping Sun; Ming Gao; Ya Lun Li

doi:10.4028/www.scientific.net/AMR.580.99

Paper Titles

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Digital Design and Manufacturing of Zero-Tooth-Difference Mechanism Based on UG
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The Contact Point Regress Method and Error Calculation’s Mathematical Model of the Spatial Cam Profile Inspection
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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 580Fault Prediction Method Research of the Power...

Fault Prediction Method Research of the Power Plant Fan

Abstract:

To solve the problem of the power plant fan fault prediction, proposed that combining with neural network method and nonparametric density function estimation methods based on parzen window the estimation to achieve fault detection. To improve the prediction performance of neural network, used PSO method, which can realize weights optimization of the neural network prediction, avoid falling into local optimum. Using sliding time window achieve the multi-step prediction of the neural network, and ensure the prediction accuracy. Then, fault is predicted by prediction residuals through density function estimation and hypothesis test. Finally, by using the vibration fault prediction of the air feeder of a power plant in Shanxi as research object to test this method, the simulation result illustrate this fault prediction algorithm can predict the fault of fan timely and effectively.

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Periodical:

Advanced Materials Research (Volume 580)

Pages:

99-104

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.580.99

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Online since:

October 2012

Authors:

Jian Ping Sun, Ming Gao, Ya Lun Li

Keywords:

Fault Prediction, Neural Network (NN), Non-Parametric Density Estimation, Particle Swarm Optimization (PSO), Power Plant Fan

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