Real-Time Prediction of Remaining Useful Life for Composite Laminates with Unknown Inputs and Varying Threshold

Prognostics and health management (PHM) has emerged as an essential approach for improving the safety, reliability, maintainability of composite structures. However, obstacle remains in its damage state estimation lifetime prediction due to unknown inputs. Thus, a self-calibration Kalman-filter-based framework residual life is proposed, which involves input items fatigue evolution model employs health-monitoring data estimate compensate them. Combined with time-varying structural failure threshold, remaining useful (RUL) laminates subjected loading predicted, providing novel solution problem inputs PHM. The simulation results demonstrate that developed method can performance degradation well, RUL accuracy within 5% existing such foreign impact damage.