Multi-level wavelet packet fusion in dynamic ensemble convolutional neural network for fault diagnosis

Abstract

Due to the complicated structure and varying operating conditions of machinery in various applications, intelligent identification of the health state based on the vibration data is still a great challenge in fault diagnosis. In this paper, a variant of the convolutional neural network, named dynamic ensemble convolutional neural network was proposed for fault diagnosis by intelligent fusion of the multi-level wavelet packet. First, wavelet packet transform was employed to construct multi-level wavelet coefficients matrixes for representing the nonstationary vibration signal comprehensively. Then, several paralleled convolutional neural networks with shared parameters were built, not only to learn the multi-level fault features automatically, but also to restrain the overfitting of the deep learning partially. At last, a dynamic ensemble layer was applied to fuse multi-level wavelet packet by assigning weights dynamically. The validation on two experimental datasets of the planetary gearbox under varying speed demonstrated that the developed method can fuse the fault features in multi-level wavelet packet thoroughly, and improve the effectiveness and robustness for fault diagnosis of gearbox under whether the sufficient or limited fault data conditions.