Abstract
Piezoelectric vibration sensors (PVSs) are widely applied to vibration detection in aerospace engines due to their small size, high sensitivity, and high-temperature resistance. The precise prediction of their remaining useful life (RUL) under high temperatures is crucial for their maintenance. Notably, digital twins (DTs) provide enormous data from both physical structures and virtual models, which have potential in RUL predictions. Therefore, this work establishes a DT framework containing six modules for sensitivity degradation detection and assessment on the foundation of a five-dimensional DT model. In line with the sensitivity degradation mechanism at high temperatures, a DT-based RUL prediction was performed. Specifically, the PVS sensitivity degradation was described by the Wiener-Arrhenius accelerated degradation model based on the acceleration factor constant principle. Next, an error correction method for the degradation model was proposed using real-time data. Moreover, parameter updates were conducted using a Bayesian method, based on which the RUL was predicted using the first hitting time. Extensive experiments on distinguishing PVS samples demonstrate that our model achieves satisfying performance, which significantly reduces the prediction error to 8 h. A case study was also conducted to provide high RUL prediction accuracy, which further validates the effectiveness of our model in practical use.