An Explainable and Lightweight Improved 1-D CNN Model for Vibration Signals of Rotating Machinery

Abstract

Previous 1D convolutional neural networks (1D CNN) models for vibration fault diagnosis have high computational complexity and poor interpretability, which cannot meet the higher requirements of model storage, computational efficiency and reliability for airborne and portable devices. Considering these challenges, an Explainable and Lightweight 1D CNN (ELCNN) model based on Square Global Average Pooling (S-GAP) and improved for vibration signals is proposed. The feature extraction and classification layers of 1D CNN are optimized to minimize the model parameters and computational complexity and improve interpretability while ensuring diagnostic accuracy. The model compresses the number of convolutional layers, removes unnecessary bias, activation function and pooling layer, and replaces the fully connected layer (FCL) with S-GAP. Improved 1D CNN models of different methods are evaluated and analyzed on public datasets of rolling bearings. Results show that the ELCNN improved for vibration signals is more lightweight, anti-noise and explainable than other models, and the diagnostic accuracy is further improved.