Multiscale Rényi entropy wavelet convolutional network with entropy-guided attention for planetary gearbox fault diagnosis

The fault characteristics of planetary gearbox under strong noise conditions show the characteristics of multiscale coupling and weak characteristics, which lead to the problems of insufficient feature extraction and weak antinoise ability in traditional diagnosis methods. In response to this challenge, this article proposes a multiscale Rényi entropy wavelet convolutional network with entropy-guided attention. First, Rényi entropy wavelet convolution is designed. Through different scales of time–frequency feature extraction and feature uncertainty measurement, the feature extraction ability under strong noise is improved. Second, through multiscale decomposition convolution, the features can more accurately locate the distribution characteristics of the fault on the time–frequency plane and enhance the richness of feature expression. Then, the correlation coefficient interaction mechanism is designed, and the correlation coefficient is used to effectively fuse the parallel branch features, so that the information between heterogeneous features can be more comprehensively represented. Finally, the entropy-guided attention mechanism is used to focus on the fault features more accurately. Comparative test verification is carried out on the public gearbox test vibration data set. This method provides an effective solution for intelligent fault diagnosis of planetary gearboxes.