Investigation of Model-Based Diagnostic Methods, 09-R8104Printer Friendly Version
Inclusive Dates: 09/28/09 – 04/01/11
Background - This project explored various diagnostic techniques utilizing empirical data and physics-based models. Previous projects addressed anomaly detection technologies. This project is related to those previous efforts by addressing diagnostics, which is the next step of the detection, diagnostic, and prognostic process that enables a Condition Based Maintenance (CBM) system. In improving and automating the diagnostic process, project team members developed a configurable physics-based model to be fused with empirical sensor data acquired from in-flight data. The improved and automated methods developed for this project represent advancement beyond the state of the art for aerospace propulsion engine diagnostics.
Approach - The main objectives of this project were to create a configurable physics-based model and to develop analysis methodologies that fuse the physics-based information with sensor data to provide a high level of diagnostic automation and precision.
- With this approach, pattern/regime recognition and classification
algorithms and particle swarm intelligence-based diagnostic algorithms were
created and tested. The pattern recognition algorithms proved reliable for
operational regime classification and sensor validation. The particle swarm
diagnostic algorithms fuse the formerly disparate data sets of empirical
data and physics-based models. These algorithms reduce diagnostic time by
automatically directing maintenance personnel to the failed module, even if
the failure is not predefined as a fault.