Application of Multi-Scale Convolutional Neural Networks and Extreme Learning Machines in Mechanical Fault Diagnosis

Abstract

Extracting fault features in mechanical fault diagnosis is challenging and leads to low diagnosis accuracy. A novel fault diagnosis method using multi-scale convolutional neural networks (MSCNN) and extreme learning machines is presented in this research, which was conducted in three stages: First, the collected vibration signals were transformed into images using the continuous wavelet transform. Subsequently, an MSCNN was designed to extract all detailed features of the original images. The final feature maps were obtained by fusing multiple feature layers. The parameters in the network were randomly generated and remained unchanged, which could effectively accelerate the calculation. Finally, an extreme learning machine was used to classify faults based on the fused feature maps, and the potential relationship between the fault and labels was established. The effectiveness of the proposed method was confirmed. This method performs better in mechanical fault diagnosis and classification than existing methods.