Summary
Many safety-critical engineered systems must remain reliable and safe throughout their intended lifetime to support both public safety and economic stability. System diagnostics plays a vital role in predicting future conditions and guide decisions to prevent unexpected failures or mitigate impacts. However, the existing forecasting methods often depend on large amounts of experimental data or real-world system failure data, which are difficult to obtain, especially for safety-critical systems, where laboratory degradation experiments are time-consuming and field failure events are rare. There
