Remaining Useful Life Prediction of Multi-Component System Clustering with Time-Varying Kernel Density

Aiming at the uncertainty of the degradation procedure of a complex multi-component system in a time-varying environment and the influence of the random dependence between different components, a remaining useful life prediction model for time-varying adaptive kernel density estimation based on the dependence between multiple components is established. First, the random dependence between multi-component systems is clustered. Considering that different components in the same cluster are affected by the random dependence, the components between different clusters do not affect each other. Judge the dependence characteristics between components, and use the dependence characteristics of different components to conduct clustering degradation modelling. Secondly, the parameters of the system change with time. Considering that monitoring data near the current time has a greater impact on the remaining useful life prediction compared with historical data, data in the vicinity of the current time is accorded a more significant weight, and a time-varying kernel density estimation prediction method is designed, in which the nearest-neighbour adaptive window-width selection method is used. Finally, give an example to demonstrate the effectiveness by the proposed method.