A Dynamic Prediction Method for Rolling Bearings Residual Life via Multi-Stage Exponential Model

The accurate prediction of residual life in rolling bearings is pivotal for ensuring the safe operation and maintenance of equipment. To overcome the limitations of conventional methods that depend on entire sample datasets, a novel dynamic prediction approach has been introduced. This approach utilizes a multi-stage exponential model for more accurate residual life estimation in rolling bearings. This method entails the computation of multiple signal characteristics from sample data to depict the bearing degradation process comprehensively. An intersection clustering technique is applied to identify and group sensitive characteristics that indicate the bearing's degraded state. Subsequently, utilizing these selected sensitive features, demarcation points are established to automatically delineate degradation stages. A multi-stage exponential model is then formulated for dynamic residual life prediction, tailored to the distinct degradation phases. Furthermore, the initial parameters of the model are optimized employing the particle swarm optimization (PSO) enhanced expectation maximization (EM) method. Through experimental verification, compared with the four existing popular prediction methods, the prediction error was reduced by 14.36% to 20.81%, which proves the effectiveness and feasibility of the proposed method.