国家重点研发计划重点专项项目(2018YFB1701400); 国家自然科学基金项目(U1704158); 河南省科技攻关重点项目(212102210103)
Henan Normal University
National Key R&D Program of China (2018YFB1701400); National Natural Science Foundation of China (U1704158);Henan Province Technologies Research and Development Project of China (212102210103)
深度迁移学习技术已经成功应用于跨工况的滚动轴承剩余寿命(Remaining Useful Life, RUL)预测问题,但针对在线场景的RUL评估,仍有如下不足:1）在线工况存在漂移,无法确定同工况的历史数据,故不能直接构建回归预测模型;2）在线目标轴承只有正常状态和早期故障数据,无法直接与离线轴承的快速退化期数据进行迁移学习.从时序退化信息迁移的角度,本文提出一种面向未知工况的轴承在线RUL 评估方法.首先,构建融合第三方退化信息的时间序列迁移递归预测模型,利用张量长短时记忆网络提取离线工况全寿命数据的单调性和趋势性等时序信息,并迁移到在线递归预测过程;其次,利用迁移成分分析对所预测的在线退化序列和已有离线序列进行公共特征空间适配,提取域无关特征,并构建支持向量机回归模型,实现在线轴承剩余寿命评估.在IEEE PHM Challenge 2012轴承数据集上进行实验,结果表明,本文方法可在只有早期故障数据的情况下准确预测退化趋势,为未知工况下的轴承在线RUL评估提供一种有效的解决方案.
Deep transfer learning technology has been successfully applied to the remaining useful life (RUL) prediction of bearings across different working conditions. However, the existing methods encounter the following problems in online scenarios: 1) Due to the drift of online working conditions, it is hard to accumulate the historical data from the same working condition and then build a regression model for the prediction directly. 2) Online target bearing only has the data of normal state and early fault state, so it is challenging to directly perform transfer learning with the offline bearing data of fast degradation period. To solve these problems, an online RUL estimation method for unknown working conditions is proposed from the transfer of temporal degradation information. First, a new time series transfer recursive prediction model integrating prior degradation information is constructed. Employing Tensor Long Short-Term Memory, the temporal information of the whole-life data in the offline working conditions, such as monotonicity and tendency, is extracted and transferred to the online recursive prediction process for getting online degradation sequence. Second, the Transfer Component Analysis is adopted to adapt the common feature space of the predicted online degradation sequence and existing offline sequences. By extracting domain-invariant features and constructing support vector machine model, the RUL of online bearing can be evaluated. Experiments are conducted on the IEEE PHM Challenge 2012 bearing dataset. The results show that, the proposed method can accurately estimate the degradation trend only using early fault data, and provide an effective solution for the online RUL estimation under an unknown working condition.