Degradation assessment (DA) is one of the most important technologies to implement the health management and predictive maintenance of rotating machinery. As the main task of DA, anomaly/degrade point detection and remaining useful life (RUL) prediction method of rolling element bearings is investigated in this paper. To detect the abnormal point more accurately, root mean square value is considered as the health monitoring indicator and 3$\sigma$ rule is used to adaptively monitor the abnormal point. To predict precisely the RUL, a three-stage strategy is proposed. Firstly, twenty-four basic characteristics are extracted from vibration signal, which are reconstructed by using basic characteristics based complete ensemble empirical mode decomposition with adaptive noise (BC-CEEMDAN), and then the trend curves are extracted to reduce the fluctuation. Next, the most sensitive features are selected by employing a linear combination of monotonicity and correlation criteria. Finally, by input the selected features into the gated recurrent unit (GRU) neural network, we achieve the efficient health indicator with BC-CEEMDAN-GRU. To verify the effectiveness of the proposed approach, experiments on two bearing datasets are carried out, and the advantage is emphasized by comparison with the five existing methods.

退化评估（DA）是实现旋转机械的健康管理和预测性维护的最重要技术之一。作为DA的主要任务，研究了滚动轴承的异常/退化点检测和剩余使用寿命预测方法。为了更准确地检测异常点，将均方根值视为健康监控指标，并且3$\sigma$规则用于自适应监视异常点。为了精确预测RUL，提出了一个三阶段策略。首先，从振动信号中提取出二十四个基本特征，并利用基于基本特征的完整集合经验模态分解和自适应噪声（BC-CEEMDAN）进行重构，然后提取趋势曲线以减小波动。接下来，通过采用单调性和相关性准则的线性组合来选择最敏感的特征。最后，通过将选定的特征输入到门控循环单元（GRU）神经网络中，我们使用BC-CEEMDAN-GRU实现了有效的健康指标。为了验证所提方法的有效性，我们在两个轴承数据集上进行了实验，