Adaptive resize-residual deep neural network for fault diagnosis of rotating machinery

Accurate fault diagnosis technology is essential for ensuring reliable operation of rotating machinery. However, complex conditions and various damage forms bring challenges to present diagnosis technology. In this study, a novel fault diagnosis method is proposed utilizing the newly developed adaptive resize-residual deep neural networks. The usage of the proposed method consists of three steps. First, the continuous wavelet transform is used to transfer the acquired vibration signals into time–frequency images. Second, the histogram equalization algorithm is applied to enhance the contrast of these images. Finally, the enhanced images are used as the input of newly proposed adaptive resize-residual networks, in which the adaptive resize block can deduce the dimensions of input data by self-learning and feed them into the residual block for pattern recognition. Two experimental cases are designed to evaluate the performance of proposed method. The experimental results indicate that the proposed adaptive resize-residual network obtains superior recognition accuracy and outperforms many state-of-the-art methods.