Rotating bearing degradation is a physical process that typically evolves in stages characterized by different speeds of evolution of the characteristic health indicators. Therefore, it is opportune to apply different predictive models in the different stages, with the aim of balancing accuracy and calculation complexity in light of the varying needs and constraints of the different stages. This paper proposes a condition-based adaptive trend prediction method for rotating bearings. The empirical mode decomposition–self-organizing map (EMD–SOM) method is applied to analyze vibration signals and calculate a confidence value (CV) on the bearing health state. Four different degradation stages, normal, slight degradation, severe degradation and failure, are identified by using the CV value and CV change rate. At each stage, we develop a different prediction strategy tailored to the specific degradation profile. In operation, upon recognition of the stage, the corresponding prognostics models are selected to estimate the health trend. A case study on datasets from 17 test bearings demonstrates and validates the feasibility of the proposed method. The experiment results show that the adaptive prediction method is accurate and reduces computational complexity, which can be important for online applications, especially in case of limited computing resources. 
An adaptive method for health trend prediction of rotating bearings