Noise interference in the collected signals results in nonlinear and non-stationary signals during rotating machinery operations. Therefore, the recognition rate of feature extraction and rolling bearing life prediction is extremely low. Recurrent neural network can effectively process multi-time series data. The residual life prediction of rolling bearings based on deep learning has become a promising tool. Deep learning tools are independent of characteristics of rolling bearings or multi-view sequence data. This paper presents a method of predicting the residual life of rolling bearings using an improved cyclic neural network. First, an improved mean multi-scale fuzzy entropy(IMMFE) integrating the complementary ensemble empirical decomposition with adaption noise is proposed to extract the classical rolling bearing characteristic values as a new performance degradation evaluation index to improve the correlation of residual life characteristics. Second, a lasso regression and cyclic bidirectional long short-term memory-gated recurrent unit-Lasso regression (BiLSTM-GRU-LR) neural network is established to predict the residual life of rolling bearings. The algorithm was experimentally validated using the American Intelligent Maintenance Center in the United States (IMS) bearing life data. The experimental results show that the proposed BiLSTM-GRU-LR method, using IMMFE feature sets, has good robustness and high identification accuracy when predicting the residual life of rolling bearings under fault conditions.

在旋转机械操作期间，收集到的信号中的噪声干扰会导致非线性和非平稳信号。因此，特征提取和滚动轴承寿命预测的识别率极低。循环神经网络可以有效处理多时间序列数据。基于深度学习的滚动轴承剩余寿命预测已成为一种很有前途的工具。深度学习工具独立于滚动轴承或多视图序列数据的特征。本文提出了一种使用改进的循环神经网络预测滚动轴承剩余寿命的方法。第一的，提出了一种改进的平均多尺度模糊熵（IMMFE），将互补集成经验分解与自适应噪声相结合，提取经典滚动轴承特征值作为新的性能退化评估指标，以提高剩余寿命特征的相关性。其次，建立了套索回归和循环双向长短期记忆门控循环单元-套索回归（BiLSTM-GRU-LR）神经网络来预测滚动轴承的剩余寿命。该算法使用美国智能维护中心 (IMS) 的轴承寿命数据进行了实验验证。实验结果表明，所提出的 BiLSTM-GRU-LR 方法，使用 IMMFE 特征集，