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

Abstract

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