A Novel Data Augmentation and Composite Multiscale Network for Mechanical Fault Diagnosis
Yuan Wei, Zhijun Xiao, Shulin Liu, Kai‐Uwe Schröder, H. Peng, Ababacar Michel Sarr, Xiaohui Gu
Abstract
Vibration signals are widely applied in mechanical fault diagnosis methods. Strong and non-stationary noise can easily affect vibration signals, and the vibration signals will differ depending on the load conditions. Additionally, there aren’t many vibration signals with faults that can be employed because mechanical equipment typically operates as intended. A brand-new data augmentation and composite multi-scale network (DACMSN) for mechanical fault identification is proposed as a solution to these issues. The method of data augmentation is to generate samples simply and efficiently through a mathematical permutation and combination method. Each generated sample consists of two original samples, and each generated sample contains all fault information of the original sample. The dense multi-scale convolution neural network (DMSCNN) and the multi-scale residual network (MSRN) make up the composite multi-scale network (CMSN). The CMSN has rich multi-scale feature extraction capabilities and coarse-fine feature extraction capabilities. Convolutional kernels of various sizes are used in DMSCNN, which also takes into account the results of previous layers to initially discover fault features. MSRN further performs multi-scale feature extraction on each sub network of DMSCNN, achieving the effect of fully exploring fault features and effectively accelerating loss backpropagation. The proposed model can demonstrate unique advantages through the use of these mechanisms, such as a wider feature extraction scale and a more complete ability to extract coarse-fine features. This article compares the proposed method with different methods using noise and variable load dataset for tests. The experimental results demonstrate that the proposed method can perform well for fault diagnosis even in noisy environments.