School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China.
School of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
Sensors (Basel). 2022 Aug 28;22(17):6480. doi: 10.3390/s22176480.
Accurate identification of the degradation stage is key to the prediction of the remaining useful life (RUL) of bearings. The 3σ method is commonly used to identify the degradation point. However, the recognition accuracy is seriously disturbed by the random outliers in the normal stage. Therefore, this paper proposes an adaptive recognition method for the degradation stage based on outlier cleaning. Firstly, an improved multi-scale kernel regression outlier detection method is adopted to roughly search the abnormal signal segments. Then, a method for the accurate locating of the start and end points of abnormal impulses is established. After that, indexes are constructed for screening abnormal segments and an iterative strategy is proposed to achieve an accurate and efficient removal of abnormal impulses. After outlier cleaning, the 3σ approach is used to set the degradation warning threshold adaptively to realize the degradation stage recognition of the bearings. The PHM 2012 rotating machinery dataset is used to verify the effectiveness of the proposed method. Experimental results show that the proposed method can accurately locate and remove the outliers adaptively. After the cleaning of the outliers, the identification of the degradation stage is no longer disturbed by the selection of the reference signal of the normal stage and the robustness and the accuracy of the degradation stage identification have been improved significantly.
准确识别退化阶段是预测轴承剩余使用寿命（RUL）的关键。3σ 方法通常用于识别退化点。然而，在正常阶段，随机异常值会严重干扰识别精度。因此，本文提出了一种基于异常值清理的自适应退化阶段识别方法。首先，采用改进的多尺度核回归异常检测方法粗略搜索异常信号段。然后，建立了一种准确确定异常脉冲起始点和结束点的方法。之后，构建了用于筛选异常段的指标，并提出了一种迭代策略，以实现异常脉冲的准确高效去除。异常值清理后，使用 3σ 方法自适应设置退化警告阈值，以实现轴承的退化阶段识别。使用 PHM 2012 旋转机械数据集验证了所提方法的有效性。实验结果表明，所提方法能够自适应地准确定位和去除异常值。异常值清理后，退化阶段的识别不再受正常阶段参考信号选择的干扰，退化阶段识别的鲁棒性和准确性得到了显著提高。
