Improving Data Quality for Prognostic Learning Systems Considering Complex Degradation Patterns

Prognostic learning systems (PLS) primarily rely on and delve into understanding the behavior of degradation lifecycles. Linear degradation often serves as the prevailing trend for labeling performance indicators (PI) in Remaining Useful Life (RUL) or Health Index (HI) estimations. Nevertheless, some studies explore deriving HI directly from PI, capturing state of health (SoH) variations more effectively. Disregarding the impact of operating condition disturbances on PI measurements reveals that systems do not strictly follow a linear degradation path during deterioration. At certain Health Stages (HS), a system maintains a specific SoH before continuing to deteriorate, sometimes even briefly returning to a better SoH. These multivariate conditions (MVC) arise from factors such as integrated control systems and system resilience. In this less-explored perspective, following procedural data preprocessing, this study aims to enhance data quality in terms of RUL ground truth labeling by considering aspects of temporary stability and performance backup. To assess data quality improvements, a comparative study investigates various adaptive learning systems across multiple evaluation stages and metrics, focusing on generalization as the main criterion for accuracy assessment. Recognizing the inherent non-linearity in PI degradation patterns has proven to be a pivotal element in cleansing feature spaces for PLS, consequently bolstering its generalization capability. This enhancement ultimately contributes to the development of more effective maintenance schedules.