School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China.
Sensors (Basel). 2022 Nov 17;22(22):8906. doi: 10.3390/s22228906.
With the rapid development of fault prognostics and health management (PHM) technology, more and more deep learning algorithms have been applied to the intelligent fault diagnosis of rolling bearings, and although all of them can achieve over 90% diagnostic accuracy, the generality and robustness of the models cannot be truly verified under complex extreme variable loading conditions. In this study, an end-to-end rolling bearing fault diagnosis model of a hybrid deep neural network with principal component analysis is proposed. Firstly, in order to reduce the complexity of deep learning computation, data pre-processing is performed by principal component analysis (PCA) with feature dimensionality reduction. The preprocessed data is imported into the hybrid deep learning model. The first layer of the model uses a CNN algorithm for denoising and simple feature extraction, the second layer makes use of bi-directional long and short memory (BiLSTM) for greater in-depth extraction of the data with time series features, and the last layer uses an attention mechanism for optimal weight assignment, which can further improve the diagnostic precision. The test accuracy of this model is fully comparable to existing deep learning fault diagnosis models, especially under low load; the test accuracy is 100% at constant load and nearly 90% for variable load, and the test accuracy is 72.8% at extreme variable load (2.205 N·m/s-0.735 N·m/s and 0.735 N·m/s-2.205 N·m/s), which are the worst possible load conditions. The experimental results fully prove that the model has reliable robustness and generality.
随着故障预测与健康管理（PHM）技术的快速发展，越来越多的深度学习算法被应用于滚动轴承的智能故障诊断中，虽然它们都可以达到 90%以上的诊断准确率，但在复杂的极端变载条件下，模型的通用性和鲁棒性无法得到真正验证。本研究提出了一种基于主成分分析的混合深度神经网络的滚动轴承故障诊断端到端模型。首先，为了降低深度学习计算的复杂性，通过主成分分析（PCA）进行数据预处理，实现特征降维。将预处理后的数据输入到混合深度学习模型中。模型的第一层使用卷积神经网络（CNN）算法进行去噪和简单特征提取，第二层利用双向长短期记忆网络（BiLSTM）对具有时间序列特征的数据进行更深层次的提取，最后一层使用注意力机制进行最优权重分配，进一步提高诊断精度。该模型的测试精度与现有的深度学习故障诊断模型完全可比，特别是在低载时；在恒载下的测试精度为 100%，在变载下接近 90%，在极端变载下（2.205 N·m/s-0.735 N·m/s 和 0.735 N·m/s-2.205 N·m/s）的测试精度为 72.8%，这是最坏的可能负载条件。实验结果充分证明了该模型具有可靠的鲁棒性和通用性。
