Prognosis for stochastic degrading systems with massive data: A data-model interactive perspective

Fast development of various advanced sensing techniques has provided the possibility acquiring massive monitoring data of stochastic degrading systems. In this circumstance, deep learning techniques have become the emerging topic in prognostics under massive data. However, the capability of quantifying the prognosis uncertainty is limited by such methods. In addition, existing studies have attempted to combine deep learning methods and stochastic degradation model based methods, but they are basically handled separately, which cannot guarantee that the extracted health indicator (HI) by deep learning method match the adopted degradation model. To address prognostic issues under massive data, this paper proposes a data-model interaction based prognostic method by constructing a closed-loop prediction mechanism between the HI extraction and degradation modeling. The main idea is to construct a novel optimization objective function related with the prognosis performance so as to establish an interactive linkage among the HI extraction, degradation modeling and prognosis. To do so, the extracted HI is hopefully matching the adopted degradation model. In the implementation process, the stacked denoising autoencoder is used to extract the HI from massive data while the Wiener process is used to model the extracted HI for prognosis. Based on the proposed interactive mechanism, these two parts are synchronously optimized. As such, the proposed method hold promise to integrate the advantages of the deep learning in handling the massive data and stochastic process method in quantifying the prognosis uncertainty. Finally, the effectiveness and superiority of the proposed method are demonstrated by a case study associated with turbofan engines.