非负自编码网络基于部分特征表示的变工况滚动轴承状态识别

Rolling bearing state recognition under variable condition using part-based representation of nonnegativity constrained autoencoder networks

为获取数据基于部分特征表示及提升稀疏性,在深度网络中嵌入非负约束,并提出基于非负约束自编码深度网络的滚动轴承状态识别方法。堆栈多个自编码器以及分类层,构建状态特征自学习与状态识别一体化模型。以轴承振动信号时频幅值谱作为网络输入,采用嵌入非负约束限制的无监督逐层预训练和有监督微调算法实现模型优化。深度网络逐层自编码提取数据内在特征,非负约束和加噪编码提升了深度网络的基于部分特征表示能力,并降低了工况变化、噪声干扰等因素影响。将所提方法分别应用于两类滚动轴承的振动数据分析,对时变工况下4种不同状态轴承以及恒定工况下8种不同状态轴承的平均识别准确率分别为97.99%和97.32%,其中保持器不同磨损程度轴承平均识别准确率为95.64%,同时所提方法在不同加噪情况下表现出良好抗噪能力。

To learn part-based representation of data and enhance sparseness, this study demonstrates the embedding of nonnegativity constraints in the deep network. A state recognition method for rolling bearing is proposed based on the deep autoencoder neural network with nonnegative constrains. Multiple autoencoders and a classification layer are stacked to formulate an integrated model for feature self-learning and state recognition. The bearing vibration time-frequency spectrogram is taken as input, and the model is optimized by combining unsupervised layer-wise pre-training and supervised finetuning. Both of them are with the nonnegativity constraints embedding. The deep network encodes and extracts the intrinsic feature of data layer by layer. The nonnegative constrains and denoising encoding improve the part-based representation ability of deep network. And the influence of condition variation and noise interference is decreased. The proposed method is applied to the vibration data analysis of two kinds of rolling bearings. The average recognition accuracy of four different state bearings under variable conditions and eight different state bearings under constant conditions are 97.99% and 97.32%, respectively. The average recognition accuracy of bearings with different retainer wear levels is 95.64%. Meanwhile, the proposed method shows good anti-noise capability under different levels of noise.