The application scenarios of rotating machinery are becoming increasingly complicated due to the rapid development of the manufacturing industry. The remaining useful life (RUL) prediction of rolling bearings has gradually been considered in many industry fields for ensuring the safety and reliability of whole systems. As an effective way to analyze data, the relevance vector machine (RVM) approach holds great potential for RUL prediction. However, the redundant features of rolling bearing vibration signals can easily lead to overfitting and low accuracy of the RVM model for RUL prediction. To conquer these issues, inspired by the idea of the boosting algorithm and ensemble learning, this paper proposes a new RVM model, called the weight-tracking relevance vector machine (WTRVM). Within the proposed WTRVM model, an adaptive sequential optimal feature selection method is designed to avoid overfitting by selecting the best features. The error between the prediction value of the RVM model and the true value is counted for the RVM model training and weight tracking. The most accurate model can be obtained when all selected features have been trained. Finally, the proposed WTRVM algorithm is experimentally demonstrated to be effective for the RUL prediction of rolling bearings.

由于制造业的快速发展，旋转机械的应用场景变得越来越复杂。为了确保整个系统的安全性和可靠性，滚动轴承的剩余使用寿命（RUL）预测已在许多行业领域逐渐被考虑。作为一种有效的数据分析方法，关联向量机（RVM）方法在RUL预测中具有巨大的潜力。但是，滚动轴承振动信号的冗余特征很容易导致RVM模型用于RUL预测的过拟合和低精度。为了解决这些问题，受boost算法和集成学习的启发，本文提出了一种新的RVM模型，称为权重跟踪相关向量机（WTRVM）。在建议的WTRVM模型中，一种自适应顺序最优特征选择方法旨在通过选择最佳特征来避免过拟合。计算RVM模型的预测值和真实值之间的误差，以进行RVM模型训练和权重跟踪。训练所有选定的特征后，可以获得最准确的模型。最后，通过实验证明了所提出的WTRVM算法对于滚动轴承的RUL预测是有效的。