1. Maintenance Innovation Challenge 2016
Data-Driven & Goal-Driven Condition-Based Predictive Maintenance (DCPM/GCPM)
PROBLEM STATEMENT
Increase aircraft availability by developing automated Condition -Based Maintenance Plus (CBM+) turnkey capabilities
Detect abnormal precursors in C-130 Digital Flight Data Recorder (DFDR) and Automated Inspection Repair Corrosion and Aircraft Tracking (AIRCAT) engine data
Fuse abnormal behavior detections with C-130 debrief and AIRCAT STD on-board fault detection reports
Apply Reliability and Maintainability Information System (REMIS) and other repair data to determine maintenance orders and to maintain the CBM+ system
Reduce Versatile Depot Automatic Test Station (VDATS) costs and improve repair reliability
BENEFITS
DCPM is an affordable solution to unexpected precursor abnormality detection & characterization to extend CBM+
GCPM provides a turn-key capability that automatically retrains DCPM to learn dynamic normal health behaviors
DCPM/GCPM identifies when VDATS needs the more expensive recalibration process based on self-test data
DCPM/GCPM is extensible, scalable, cross-platform, and supports multiple users and roles in Linux and Windows as part of the 581st Prognostic Aircraft Maintenance Software Suite (PAMSS) for the C-130
Improves confidence in automated maintenance recommendations with the user on-the-loop
TECHNOLOGY SOLUTION
DCPM uses ANOM TRL 7 data fusion and neural networks to detect unknown abnormal fault precursors within DFDR & AIRCAT engine data signatures
DCPM detects and characterizes abnormal behavior in VDATS self-test outputs
GCPM tells DCPM when to retrain, what to retrain and test on, and when to promote to real-time operations
Smoking Gun finds high confidence correlations with Reliability and Maintainability Information System (REMIS) data
Bayesian Fusion Node (BFN) services support all levels of data fusion defined by the DF&RM DNN technical architecture
Correlate abnormalities to historical REMIS data and use REMIS to determine when to retrain
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