Remaining Useful Life Prediction based on Degradation Model: Application to a Scale Replica Assembly Plant

Failure prognostics can improve industrial systems’ availability and reliability by determining the occurrence of failure and estimating the system’s remaining useful life (RUL) before deterioration. This paper presents a prognostic method based on data-driven and degradation model approaches to accurately predict the RUL of a scale replica system under different operating conditions. For this purpose, vibration data collected off a faulty system over many days is analyzed in both time and frequency domains to excavate data features related to the degradation and then identify the health indicator (HI) and the failure mechanism. At that point, the constructed HI, thanks to the feature importance ranking and principal component analysis (PCA) techniques, is used as a mathematical input for an exponential degradation model. Finally, the results are validated using real-world vibration-based degradation information leading to an effective predictive strategy for industrial systems.