轴承的个体异质性及工况差异性使得其性能退化轨迹不尽相同，导致了训练轴承建立的深度学习模型与测试轴承的失配问题，对此，本文提出了基于卷积自编码器与自组织映射的轴承剩余使用寿命（Remaining Useful Life，RUL）灰色预测方法。该方法引入了以轴承自身监测数据为驱动的批量归一化的卷积自编码器对轴承性能退化特征进行深度提取，并结合自组织映射算法进行性能退化指标（Degradation Indicator, DI）自主构建。采用动态时间规划算法对各个轴承退化轨迹进行相似匹配分析，以相匹配的全寿命轴承的DI灰色模型回归曲线在寿命终点取值作为参考,进行测试轴承的失效阈值设置。以测试轴承历史DI为驱动，采用全阶时间幂灰色预测模型对测试轴承RUL进行滚动预测。实验结果表明，本文提出方法在保留轴承退化趋势的个性差异性的同时，实现了轴承失效阈值自主合理设置，提高了轴承RUL的预测精度。
The individual heterogeneity and working condition difference of bearings lead to the different performance degradation tracks of bearings, which results in the mismatch between the deep learning model established by full-life bearing and the test bearing. Aiming to this problem, this paper proposed a Remaining Useful Life (RUL) grey prediction method based on convolutional autoencoder (CAE) and self-organizing maps (SOM). In this method, a batch normalized CAE driven is introduced to extract the deep features of bearing performance degradation, and the self-organizing maps (SOM) algorithm is used to construct the performance degradation indicator (DI). The algorithm of dynamic time warping (DTW) is applied to match the bearing degradation trajectories, and the DI curve of the bearing with similar degradation trajectory is used as the reference to set the failure threshold of the test bearing. Driven by the history DIs of the test bearing, the Gray Forecasting Model with Full Order Time Power terms (FOTP-GM) is used to predict the RUL of the test bearings. The experimental results show that the proposed method not only retains the individual differences of bearing degradation trend, but also realizes the independent and reasonable setting of bearing failure threshold and improves the prediction accuracy of RUL.