Intelligent condition prognostics for helicopter main gearboxusing discrete wavelet and Kalman filter

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Journal of Vibration and Shock ›› 2012, Vol. 31 ›› Issue (17) : 159-164.

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Intelligent condition prognostics for helicopter main gearboxusing discrete wavelet and Kalman filter

LIU Li-Sheng , YANG Yu-hang

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Abstract

Main gearbox (MGB) is a key component of helicopter transmission system, it often runs in the atrocious condition of high revolving speed and high burthen, it is very important to achieve condition prognostics for the safe of helicopter. This paper puts forward a intelligent condition prognostic system for helicopter MGB using discrete wavelet transform (DWT), Kalman filter and Elman neural network. The mother wavelet of Daubechies 44 (db44) is selected to extract feature vectors in the process of DWT. Kalman filter is used for feature vectors prognostics, and Elman neural network is taken for fault distinguish and classification. In the algorithm of Kalman filter, a new prognostic method is proposed, and it is verified in the system by experiment. The results indicate that the prognostic outcome of the Kalman filter with this method is better, which is more applicable for the prognostics of feature vectors, and the intelligent condition prognostic system composed of DWT, Kalman filter and Elman neural network is feasible, which can predict the future condition of MGB accurately.

Key words

Main gearbox (MGB) / Discrete wavelet transform (DWT) / Parseval theorem / Kalman filter / Elman neural network

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LIU Li-Sheng;YANG Yu-hang. Intelligent condition prognostics for helicopter main gearboxusing discrete wavelet and Kalman filter[J]. Journal of Vibration and Shock, 2012, 31(17): 159-164