Data augmentation for bearing fault detection with a light weight CNN

Bearings are vital part of rotary machines. A failure of bearing has a negative impact on schedules, production operation and even human casualties. Therefore, in prior achieving fault detection and diagnosis (FDD) of bearing is ensuring the safety and reliable operation of rotating machinery systems. However, there are some challenges of the industrial FDD problems. First, there are unbalanced samples because industrial faults rarely occur. Conse-quently, the labeled data which can refer to failure information are limited in the industry and data augmentation methods are critical pre-processing be-fore training data driven models. Second, due to many learnable parameters in model and data of long sequence, both lead to time delay for FDD. There-fore, this paper proposes various data preprocessing methods and Light-Convolutional Neural Network (LCNN).