The availability of data and AI-based methods have in recent years advanced the field of prognostics and health management. However, making accurate predictions of Remaining Useful Life (RUL) on products being used in operational environments, based on lifetime models from accelerated ageing experiments in the lab, is not straightforward. This paper introduces a recently patented method [1] for correlating lab-measured accumulated damage – gathered through accelerated aging experiments – with the RUL of products in the field, specifically with respect to the degradation mechanisms observed during testing. The method bridges the gap between prediction models developed from datasets recorded in a lab, to datasets available in the field. The method is based on the following steps: (i) mapping of the degradation process, (ii) exposure of samples to different levels of degradation, (iii) insertion of degraded samples in the final products, (iv) measurement of product-level responses to the different levels of degradation, and (v) comparison of those degradation responses to data from products in the field. One example of a degradation mechanism (wire bond lift-off) in power semiconductor packages is used to illustrate the method. However, the method is technology agnostic and can generally also be used for batteries, electric motors, or any application where a degradation can, with good accuracy, be modelled in the lab.