The emergence of prognostics and health management as a condition-based maintenance approach has greatly improved productivity, maintainability, and most essentially, reliability of systems. Invariably, a rolling-element bearing (REB) is the heart of rotating components; however, its failure can have daunting effects ranging from costly unexpected breakdown to catastrophic life-threatening situations. Consequently, the need for accurate condition monitoring and prognostics of REBs cannot be overemphasized. In view of achieving a more comprehensive condition assessment for prognostics of REBs, this study proposes a kernel principal component analysis (KPCA) feature fusion technique for degradation assessment and a deep learning model for prognostics. The deep learning method-deep long short-term memory (DLSTM) has shown an evident comparative advantage over the basic LSTM model and standard recurrent neural networks for time-series forecasting. Subsequently, the proposed prognostics model-KPCA-DLSTM performance was validated with a run-to-failure experiment on REBs and evaluated for accuracy against other prognostics methods reported in other works of literature using standard performance metrics. The proposed method was also used for REB remaining useful life (RUL) prediction and the results show that the KPCA-DLSTM does not only reflect a more monotonic bearing degradation trend but also yields better prognostics results.

作为基于状态的维护方法，预测和健康管理的出现极大地提高了系统的生产率，可维护性以及最重要的可靠性。滚动轴承（REB）始终是旋转组件的核心。但是，它的失败可能会产生令人生畏的影响，从代价高昂的意外崩溃到灾难性的威胁生命的情况。因此，不能过分强调对REB进行准确的状态监测和预后的需求。为了实现对REB的预测的更全面的状态评估，本研究提出了一种用于降级评估的内核主成分分析（KPCA）特征融合技术和一种用于预测的深度学习模型。深度学习方法-深度长短期记忆（DLSTM）已显示出比基本LSTM模型和标准的递归神经网络进行时间序列预测的明显比较优势。随后，通过在REB上运行至失败的实验验证了所提出的预测模型KPCA-DLSTM的性能，并使用标准性能指标对其他文献中报道的其他预测方法进行了准确性评估。所提出的方法还用于REB剩余使用寿命（RUL）预测，结果表明KPCA-DLSTM不仅反映了单调轴承的退化趋势，而且产生了更好的预后结果。建议的预测模型KPCA-DLSTM的性能已通过REB的运行至失败实验进行了验证，并使用标准性能指标与其他文献中报道的其他预测方法进行了准确性评估。所提出的方法还用于REB剩余使用寿命（RUL）预测，结果表明KPCA-DLSTM不仅反映了单调轴承的退化趋势，而且产生了更好的预后结果。建议的预测模型KPCA-DLSTM的性能已通过REB的运行至失败实验进行了验证，并使用标准性能指标与其他文献中报道的其他预测方法进行了准确性评估。所提出的方法还用于REB剩余使用寿命（RUL）预测，结果表明KPCA-DLSTM不仅反映了单调轴承的退化趋势，而且产生了更好的预后结果。