A light intelligent diagnosis model based on improved Online Dictionary Learning sample-making and simplified convolutional neural network

Abstract

Accurately, apace and intelligently identifying the diverse faults of rotating machines is of great significance. However, high diagnostic accuracy is usually accompanied by lower model efficiency. To address this, a light intelligent diagnosis model based on improved Online Dictionary Learning (ODL) sample-making and simplified Convolutional Neural Network (CNN) is proposed. Within the sampling time, ODL based on Orthogonal Matching Pursuit (OMP) is used to select time-domain multi-channel signals to make RGB samples, which results in samples with smaller size and stronger features. Benefiting from the high-quality samples, the CNN model is simplified, only small-scale one-dimensional convolution kernels that undertake different tasks and global average pooling (GAP) layer are used, which greatly improve diagnostic efficiency of the network while ensuring diagnostic accuracy. Three different fault diagnosis cases of rotating machine suggest that the proposed model has high diagnostic accuracy along with high efficiency.