Research studies on data-driven approaches to rotating components and rolling element bearing (REB) prognostics have recently witnessed a rapid increase. These data-driven methods rely on sensor data for condition monitoring and degradation assessments; however, the problem of mining features from these sophisticated data using appropriate intelligent methods and choosing a practically reliable predictive model(s) has become a global concern. Vibration monitoring for REBs have over the years shown great effectiveness. Although monotonic statistical features serve as reliable health indicators (HIs), relying on a single feature for optimal bearing degradation assessment is inefficient. By fusing highly monotonic features using appropriate methods, a more reliable HI can be constructed and from this, various degradation states/stages and time to start prediction (TSP) can be identified by mapping known failure modes/degradation states to cluster points from clustering algorithms. Emphatically, the choice of regression algorithms for prognostics poses more concern as engineers and data scientists are faced with choosing between Bayessian machine learning (ML) and deep learning (DL) methods. This study presents a methodology for constructing a reliable HI for bearing prognostics, choosing a reliable TSP, and provides a comparison between ML and DL methods for bearing prognostics. As representatives of both domains, the Gaussian process regression (GPR) and the deep belief network (DBN) are introduced and compared. The results provide a reliable paradigm for prognosible feature representation for REBs and for choosing between both domains while considering their dependencies, efficiencies, and deficiencies.

数据驱动的旋转部件和滚动轴承（REB）预测方法的研究最近见证了快速增长。这些数据驱动的方法依赖于传感器数据来进行状态监视和降级评估。然而，使用适当的智能方法从这些复杂数据中挖掘特征并选择实用可靠的预测模型的问题已成为全球关注的问题。多年来，REB的振动监测已显示出巨大的效果。尽管单调统计特征可以用作可靠的健康指标（HIs），但依靠单个特征进行最佳轴承退化评估效率不高。通过使用适当的方法融合高度单调的特征，可以构建更可靠的HI，并且由此，通过将已知的故障模式/退化状态映射到来自聚类算法的聚类点，可以识别各种退化状态/阶段和开始预测的时间（TSP）。着重说，由于工程师和数据科学家面临着在贝叶斯机器学习（ML）和深度学习（DL）方法之间进行选择的挑战，因此，用于预测学的回归算法的选择引起了更多关注。这项研究提出了一种方法，用于构建轴承预后的可靠HI，选择可靠的TSP，并提供ML和DL方法进行轴承预后的比较。作为这两个领域的代表，引入并比较了高斯过程回归（GPR）和深度信念网络（DBN）。