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.
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机译:深度学习一直是电机轴承故障诊断中的重要主题,它可以避免需要广泛的专业知识和繁琐的人工特征提取。然而,现有的神经网络在可变负载条件下具有较低的故障识别率和较低的适应性。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与宽核(ACNN-W)和Dempster-Shafer(D-S)证据理论相结合。首先,对由两个传感器获取的电机轴承的原始振动时域信号进行重新采样。然后,利用RMSprop优化的ACNN-W的四个框架来自适应地学习特征并使用Softmax分类器对其进行预分类。最后,采用D-S证据理论综合确定Softmax分类器输出的分类矢量,以实现轴承的故障检测。通过在Case Western Reserve University(CWRU)电机轴承数据库上进行的实验,通过结合来自不同传感器的补充或冲突证据,该方法可以适应不同的负载条件。实验结果表明,与现有的其他实验方法相比,该方法可以有效地增强模型的跨域自适应能力,具有更好的诊断精度。
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