School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Sensors (Basel). 2019 Jan 2;19(1):122. doi: 10.3390/s19010122.
Deep learning has been an important topic in fault diagnosis of motor bearings, which can avoid the need for extensive domain expertise and cumbersome artificial feature extraction. However, existing neural networks have low fault recognition rates and low adaptability under variable load conditions. In order to solve these problems, we propose a one-dimensional fusion neural network (OFNN), which combines Adaptive one-dimensional Convolution Neural Networks with Wide Kernel (ACNN-W) and Dempster-Shafer (D-S) evidence theory. Firstly, the original vibration time-domain signals of a motor bearing acquired by two sensors are resampled. Then, four frameworks of ACNN-W optimized by RMSprop are utilized to learn features adaptively and pre-classify them with Softmax classifiers. Finally, the D-S evidence theory is used to comprehensively determine the class vector output by the Softmax classifiers to achieve fault detection of the bearing. The proposed method adapts to different load conditions by incorporating complementary or conflicting evidences from different sensors through experiments on the Case Western Reserve University (CWRU) motor bearing database. Experimental results show that the proposed method can effectively enhance the cross-domain adaptive ability of the model and has a better diagnostic accuracy than other existing experimental methods.
深度学习是电机轴承故障诊断中的一个重要课题，它可以避免对广泛的领域专业知识和繁琐的人工特征提取的需求。然而，现有的神经网络在变载条件下具有较低的故障识别率和适应性。为了解决这些问题，我们提出了一种一维融合神经网络（OFNN），它将自适应一维卷积神经网络（ACNN-W）和证据理论中的 Dempster-Shafer（D-S）结合在一起。首先，对两个传感器采集的电机轴承原始振动时域信号进行重采样。然后，利用 RMSprop 优化的四个框架的 ACNN-W 自适应地学习特征，并使用 Softmax 分类器进行预分类。最后，利用 D-S 证据理论综合确定 Softmax 分类器输出的类向量，实现轴承的故障检测。通过在凯斯西储大学（CWRU）电机轴承数据库上的实验，该方法通过结合来自不同传感器的互补或冲突证据，适应不同的负载条件。实验结果表明，与其他现有实验方法相比，该方法可以有效地提高模型的跨域自适应能力，并具有更好的诊断精度。
