Estimation of remaining useful life and machines State of Health (SoH) plays a vital role in performing predictive maintenance for complex systems today. Despite the numerous researches about this topic and the huge numbers of requests from companies, it still remains a challenge. On one hand, current techniques rely on machine learning techniques not taking into account the huge amount of computational power needed and the costs of such server architectures. On the other hand, it can be very challenging to develop an edge-computing solution considering strong computational power limits. To address this issue, in this thesis work it is shown a possible edge-computing intended approach, based on up to date parameter estimations techniques such as Kalman filtering, residual error processing and global sensitivity analysis.