  Within the field of prognostics and health management (PHM), health
indicators (HI) can be used to aid the production and, e.g. schedule
maintenance and avoid failures. However, HI is often engineered to a specific
process and typically requires large amounts of historical data for set-up.
This is especially a challenge for SMEs, which often lack sufficient resources
and knowledge to benefit from PHM. In this paper, we propose ModularHI, a
modular approach in the construction of HI for a system without historical
data. With ModularHI, the operator chooses which sensor inputs are available,
and then ModularHI will compute a baseline model based on data collected during
a burn-in state. This baseline model will then be used to detect if the system
starts to degrade over time. We test the ModularHI on two open datasets, CMAPSS
and N-CMAPSS. Results from the former dataset showcase our system's ability to
detect degradation, while results from the latter point to directions for
further research within the area. The results shows that our novel approach is
able to detect system degradation without historical data.
