Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (7): 269-278.DOI: 10.3778/j.issn.1002-8331.2006-0030

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Data Augmentation Algorithm for Bearings Faults Diagnosis

LIN Ronglai, TANG Bingying, CHEN Ming   

  1. School of Mechanical Engineering, Tongji University, Shanghai 201804, China
  • Online:2021-04-01 Published:2021-04-02



  1. 同济大学 机械与能源工程学院,上海 201804


To address the problem of the inadequate faulty examples and various working conditions of industrial data in bearings faults diagnosis, a practical data augmentation based on order tracking is proposed. With the use of the angular invariance of order tracking, original signals are resampled in time domain in order to obtain the simulated signal that having the sharing pattern in angular domain. Then, the amplitude of the signal is recalculated to offset the energy change causing by resampling and environment noise. Finally, random zero padding is utilized to make the length of the signal consistently. According to experimental results, the newly proposed algorithm can increase the sample diversity and the quantity of dataset. Besides, it can alleviate existing problems in original dataset and effectively improve the classification accuracy and the generalization performance of diagnosis models.

Key words: data augmentation, signal processing, fault diagnosis, order tracking



关键词: 数据增强, 信号处理, 故障诊断, 阶次跟踪