Fuzzy-Membership-Based Framework for Task Transfer Learning Between Fault Diagnosis and RUL Prediction

Abstract

Fault classification and remaining useful life (RUL) prediction are two important but different issues in data-driven prognostics and health management. Generally, they are resolved separately in present deep-learning-based methods, with one network orientated for one individual task, bringing low training efficiency and waste of computing resources. Although the output labels for these two tasks are entirely different, one is discrete and the other is continuous, they both reflect the equipment's health status and, thus, are related. To address these two tasks altogether in one deep network, this article proposes a new framework for task transfer learning. Long short-term memory plus two dense layers is built as the source model, and fuzzy membership is first adopted to construct new labels for source model training, with the target to bridge the gap between classical discrete and continuous output labels. Furthermore, three kinds of fuzzy membership functions (triangular, trapezoidal, and Gaussian) are designed for labeling and comparison. To verify the proposed framework, experimental validation is conducted on the C-MAPSS turbo-engine dataset. The degradation phase classification and RUL prediction are designed as discrete classification and continuous regression tasks, respectively. Results confirm that the fuzzy-membership-based labeling can effectively improve the source model performance in task transfer learning. In addition, the trapezoidal performs better than the triangular and Gaussian among the three membership functions.