What you'll learn
- How MDDV/LOG captures step-level maintenance data automatically at the point of performance
- Integration across IMDS, FLIS, and REMIS for predictive sustainment
- Using ADS AI Joe™ and the Fleet Troubleshooting Analyzer for fleet-wide fault analysis
- Serial-number tracking as the foundation of predictive logistics and CBM+
- Updating Dash-6 requirements, TCTOs, and DMSMS decisions from standardized fleet data
Why CBM+ depends on better data
Condition-Based Maintenance Plus (CBM+) shifts sustainment from reactive, schedule-driven maintenance to predictive, condition-driven action. But predictions are only as good as the data behind them. The number one driver of low aircraft availability is a shortage of crucial parts, and intelligent, budget-constrained acquisition requires accurate and timely information. MDDV is built to gather detailed, granular data on essential parts automatically and accurately — the foundation every CBM+ program needs.
A single platform across enterprise systems
MDDV consolidates fleet health data, fault trends, and logistics information, visualizing and analyzing across IMDS (Integrated Maintenance Data System), FLIS (Federal Logistics Information System), and REMIS (Reliability and Maintainability Information System). By unifying these sources, maintainers, logisticians, and program offices can move from reactive maintenance to predictive sustainment — and MDDV can automatically feed those same enterprise systems with standardized maintenance data, analytics, trends, and forecasts.
MDDV/LOG: granular data at the point of performance
MDDV/LOG is a built-in module that automatically logs usage, inspection, and discrepancy data at the point of performance — down to the level of an individual step in a procedure or checklist — and links it to the time- and condition-based triggers defined in TCTOs, job packages, and platform schedules. Each entry automatically captures the user, date/time, tail number, unit, and base, plus removal and installation of serial-number-controlled parts. This structured data is immediately available to AI/ML engines for real-time anomaly detection, predictive forecasting, and maintenance planning — the foundational and most critical level of CBM+.
Fleet Troubleshooting Analyzer
After a fleet has used MDDV for roughly five to six months, MDDV/LOG has collected granular troubleshooting data across the fleet. The optional Fleet Troubleshooting Analyzer then applies ML and AI — using ADS AI Joe™ — to generate recommendations for improved troubleshooting and rapid systemic fault identification. Where permitted, it can update existing troubleshooting procedures with those recommendations in real time as maintainers work.
Serial Number Wizard
The Serial Number Wizard fetches the full list of serial-number-controlled parts for a platform from enterprise systems (REMIS, Teamcenter, and others). For any MDS, it automatically surfaces the serial number of each of those parts — hundreds or thousands — and lets maintainers correct enterprise data on the fly when it is wrong. The correct serial number then auto-populates every relevant form and the MDDV/LOG, enabling tracking of removal frequency, time-to-repair, and replacement intervals. Tracking parts by serial number is the crucial piece for CBM+ and the foundation of predictive logistics.
From data lifecycle to readiness
By automating the data lifecycle from collection through analysis, MDDV helps eliminate unnecessary inspections, service assets only when needed, and predict failure and obsolescence. The same standardized data updates acquisition timelines and Dash-6 requirements — Programmed Depot Maintenance cycles, Time-Compliance Technical Orders (TCTOs), Time-Change Items, and inspection cycles — and supports resolution of Diminishing Manufacturing Sources and Material Shortages (DMSMS), extending the service life of legacy and recapitalized fleets.
