Rotary Machine Fault Diagnosis Using Scalogram Image and Convolutional Neural Network with Batch Normalization

Abstract

Deep learning or deep neural network is a type of machine learning which exploits deep structures of neural networks with many layers of nonlinear data processing units. Deep neural networks have the ability to automatically extracting abstract data presentation for raw data such as time series signals, texts, and images. Deep learning has been extensively applied in vibration signal-based fault diagnosis for rotary machine since it can learn features from the raw signal of a rotary machine without requiring hand-crafted feature extraction. Batch normalization is a technique proposed to accelerate the training process and convergence of deep neural networks. In this paper, we propose a method for diagnosing faults in a rotary machine based on vibration signal using convolutional neural network with batch normalization technique, which is an advantaged variant of traditional convolutional neural network. The effectiveness of the proposed method is then verified with experiments on bearings and gearboxes fault data.