Deep imbalanced regression using cost-sensitive learning and deep feature transfer for bearing remaining useful life estimation

Abstract

Deep learning (DL) techniques have revolutionized the landscape of prognostic and health management (PHM), with the capability to learn discriminative representations from “big data”. However, realistic industry data often show imbalanced distributions, which greatly weakens the method relying on manually balanced datasets. To fill the research gap of bearing remaining useful life (RUL) estimation with imbalanced data, a novel framework is proposed to learn imbalanced regression using cost-sensitive learning and deep feature transfer (CSL-DFT), which introduces the idea of discretization and makes full use of techniques of imbalanced learning. Our CSL-DFT includes these main points: discretization & label distribution smoothing, deep feature transfer via CORrelation ALignment (CORAL), and cost-sensitive learning via class-balanced re-weighting. Considering this is the first application of deep imbalanced regression (DIR) in RUL prediction, a variety of imbalanced bearing training sets are designed based on experimental data, and verified the effectiveness of CSL-DFT. Comparison with other methods further shows its superior performance and rationality of design.