A new nonlinear ensemble framework based on dynamic-matched weights for tool remaining useful life prediction

In the field of remaining useful life (RUL) prediction, the most prominent task is constructing an accurate prediction model. However, it is difficult for single prediction models to satisfy multiple application situations. Therefore, a new nonlinear ensemble RUL prediction framework based on dynamic-matched weights is proposed in this paper. In the proposed framework, the neural network-based method and the stochastic process-based method are first aggregated through a nonlinear weighting formulation to mitigate data limitations and lack of a priori knowledge. Then, a novel ensemble weight dynamic matching algorithm is designed to achieve time-varying weight matching and improve the prediction accuracy. Finally, the ensemble RUL prediction result is characterized by the probability density function (PDF) of the remaining life. Through two milling cutter experiments, the proposed nonlinear ensemble RUL prediction framework is verified with better comprehensive performance. The cumulative relative accuracy (CRA) of the prediction results is greater than 0.6, which outperforms the commonly used tool RUL prediction method.