首页|Multi-level features fusion network-based feature learning for machinery fault diagnosis
Multi-level features fusion network-based feature learning for machinery fault diagnosis
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NSTL
Elsevier
Bearings are one of the most critical components in rotating machinery. Since the failures of bearings will cause unexpected machine damages, it is significant to timely and accurately recognize the defects in bearings. However, due to the nonlinear and nonstationary property of vibration signals, it is still a challenging problem to implement feature extraction and fault diagnosis based on vibration signals As a representative deep neural network (DNN), convolutional neural network (CNN) has been widely used for feature learning of vibration signals for machinery fault diagnosis. Due to the hierarchical structure of CNN, multi-level features will be generated by the layer-by-layer convolutional calculation in the deep network. Thus, it is interesting to select the layer-by-layer features in a concatenation layer for multi-level features fusion. In this paper, a novel CNN, multi-level features fusion network (MLFNet) is proposed for feature learning of vibration signals. Firstly, a multi-scale convolution is developed in MLFNet, where multi-branches with different kernel sizes are utilized to extract fault-related features. Secondly, the features at different layers are coupled by a concatenation layer to preserve discriminate information. Thirdly, an adaptive weighted selection based on dynamic feature selection is proposed for multi-level feature fusion. The effectiveness of MLFNet for machinery fault diagnosis is verified on two bearing test-beds. The experimental results demonstrate that MLFNet has good performance of feature extraction on vibration signals. MLFNet obtained the recognition accuracy of 99.75% for case 1 (single condition) and case 2 (varying condition). It has a better performance on bearing fault diagnosis in comparison with these typical DNNs and the state-of-art methods. (c) 2022 Elsevier B.V. All rights reserved.
NSTL
Elsevier
