基于神经网络和证据理论的滚动轴承故障预测方法

传统的故障预测方法难以对不同工况下的滚动轴承故障进行有效预测,为此,提出了一种基于BP神经网络和DS证据理论的滚动轴承故障预测方法。首先采用擅长于处理非平稳信号的小波包分解对多个传感器采集的原始振动数据进行特征分析,然后对BP神经网络的结构和参数进行优化设置并使用多个BP神经网络分别进行故障预测模型训练,最后利用DS证据理论将多个神经网络得到的预测结果进行融合并输出最终预测结果。实验结果表明,该方法能对不同工况下的滚动轴承故障进行有效预测,故障预测平均准确率达96.37%;且与相关文献提出的方法相比,所提出的方法得到的滚动轴承故障预测准确率有所提升。

A Rolling Bearing Fault Prediction Method Based on Neural Network and Evidence Theory

In view of the low efficiency found in the traditional fault prediction methods to predict rolling bearing faults under different working conditions, a new fault prediction method of rolling bearing based on BP neural network and DS evidence theory has thus been proposed. Firstly the wavelet packet decomposition, which is good at processing non-stationary signals, is used to analyze the characteristics of the original vibration data collected by multiple sensors. Next, the structure and parameters of BP neural network are optimized, with multiple BP neural networks used to train the fault prediction model respectively. And finally, the DS evidence theory is used to fuse the prediction results obtained by the multiple neural networks with the final prediction result output. The experimental results show that the proposed method can effectively predict the fault of rolling bearing under different working conditions, with the average accuracy of fault prediction attaining 96.37%. Compared with the methods proposed in the related literature, the accuracy of the rolling bearing fault prediction obtained by the proposed method has been improved.