Gear fault detection analysis method based on fractional wavelet transform and back propagation neural network
A gear fault detection analysis method based on Fractional Wavelet Transform (FRWT) and Back Propagation Neural Network (BPNN) is proposed. Taking the changing order as the variable, the optimal order of gear vibration signals is determined by discrete fractional Fourier transform. Under the optimal order, the fractional wavelet transform is applied to eliminate noise from gear vibration signals. In this way, useful components of vibration signals can be successfully separated from background noise. Then, a set of feature vectors obtained by calculating the characteristic parameters for the de-noised signals are used to characterize the gear vibration features. Finally, the feature vectors are divided into two groups, including training samples and test samples, which are input into the BPNN for learning and classification. Experimental results showed that this gear fault detection analysis method could well maintain the useful signal components related to gear faults and effectively extract the weak fault feature. The accuracy rate reached 96.67% in the identification of the type of gear fault.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles published by TSP are under an Open Access license, which means all articles published by TSP are accessible online free of charge and as free of technical and legal barriers to everyone. Published materials can be re-used if properly acknowledged and cited Open Access publication is supported by the authors' institutes or research funding agencies by payment of a comparatively low Article Processing Charge (APC) for accepted articles.