A data-driven modular architecture with denoising autoencoders for health indicator construction in a manufacturing process
This addresses the challenge for SMEs in prognostics and health management by enabling health indicator construction without extensive historical data, though it appears incremental as it builds on existing autoencoder methods.
The authors tackled the problem of constructing health indicators for manufacturing processes without historical data, proposing ModularHI, a modular approach using denoising autoencoders, and demonstrated its ability to detect system degradation on open datasets like CMAPSS.
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.