A Novel Bearing Fault Classification Method Based on XGBoost: The Fusion of Deep Learning-Based Features and Empirical Features

• Published in: IEEE transactions on instrumentation and measurement 2021, Vol.70, p.1-9
• Main Authors: Xie, Jingsong, Li, Zhaoyang, Zhou, Zitong, Liu, Scarlett
• Format: Article
• Language: English
• Subjects: Adaptation models; Adaptive systems; Artificial neural networks; Bearing fault diagnosis; Classification; Datasets; deep convolutional neural network (DCNN); Deep learning; Fault diagnosis; Feature extraction; feature fusion; LiftingNet; Neural networks; Object recognition; Robustness; Vibrations; XGBoost
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2020.3042315

The key to intelligent fault diagnosis is to find relevant characteristics with the capability of representing different types of faults. However, the engineering problem is that a few simple empirical features (EFs) cannot obtain high classification accuracy, and complex feature engineering requires strong professional knowledge, which leads to limited applications on a general scale. In addition, intelligent feature extraction and classification methods without prior knowledge cannot guarantee that the model learned the general features used for classification, and its robustness and generalization are not strong when the objects with low-quality training data. Therefore, a fusion method of combining EFs and adaptive features extracted by a deep neural network is proposed. In this method, simple EFs that only need a few professional knowledge are adopted to realize general feature extraction and, hence, maintain the robustness of the model. A modified neural network structure (LiftingNet) is proposed to achieve the adaptive extraction of hidden features for specific objects, for realizing the high precision of bearing fault classification. In order to realize the fusing of EFs and adaptive features, XGBoost is utilized as the final classifier instead of common softmax. The feasibility and validity of the proposed method are verified by two data sets collected from motor bearings at stable work conditions. The experimental results show that the classification accuracy generated by the proposed method is improved. It also can maintain the robust performance on data sets even with various noises.