Interaction models for remaining useful life estimation
This addresses predictive maintenance for industrial devices, though it appears incremental as it builds on existing feature extraction approaches.
The paper tackles remaining useful life estimation for industrial equipment by proposing a scalable model that combines multiple feature extractor blocks, achieving state-of-the-art results on the C-MAPSS benchmark.
The paper deals with the problem of controlling the state of industrial devices according to the readings of their sensors. The current methods rely on one approach to feature extraction in which the prediction occurs. We proposed a technique to build a scalable model that combines multiple different feature extractor blocks. A new model based on sequential sensor space analysis achieves state-of-the-art results on the C-MAPSS benchmark for equipment remaining useful life estimation. The resulting model performance was validated including the prediction changes with scaling.