RUL prediction of bearings plays an essential role in avoiding unwanted downtime and improving machines' reliability. A linear reliability indicator approach for RUL prediction is proposed. Multiple deep auto-encoder models are established to extract deep features, and a clustering method is employed to select the original feature set. Monotonicity is set as a criterion for evaluating and selecting original optimal features from the original feature set and a linear reliability indicator is established through it. Based on linear reliability indicator and feature interpolation, the original feature set is transformed into a mapping feature set. Finally, the mapping feature set is trained by a reliability evaluation model and the particle filter is employed to predict RUL. To demonstrate the excellent capability of the proposed approach, the vibration signal from the public PRONOSTIA bearing datasets is used. Experiment results show that the proposed approach can achieve high accuracy in RUL prediction.

轴承的RUL预测对于避免不必要的停机时间并提高机器的可靠性起着至关重要的作用。提出了一种用于RUL预测的线性可靠性指标方法。建立了多个深度自动编码器模型以提取深度特征，并采用聚类方法选择原始特征集。设置单调性作为从原始特征集中评估和选择原始最佳特征的标准，并由此建立线性可靠性指标。基于线性可靠性指标和特征插值，原始特征集将转换为映射特征集。最后，通过可靠性评估模型训练映射特征集，并使用粒子滤波器预测RUL。为了证明所提出方法的卓越能力，使用来自公共PRONOSTIA轴承数据集的振动信号。实验结果表明，该方法在RUL预测中可以达到较高的精度。