How failure gets predicted.
Plain-language guides to the economics, methods and models behind predictive maintenance, from the cost case to physics-informed neural networks, grounded in published research.
Predictive maintenance for UAV fleets.
Why calendar and threshold maintenance fall short for drone fleets, how a model forecasts battery and motor failures, and eight questions to ask before you buy.
Read the guide Last updated: September 23, 2026Battery health from flight logs alone.
The peer-reviewed research behind Vermilion's UAV battery model: state of health predicted within 2.26% from voltage, current and throttle alone.
Read the guide Last updated: July 16, 2026Predictive vs preventive maintenance: the real cost.
What predictive maintenance really costs versus preventive: upfront investment, program economics, DOE savings data, and how to decide asset by asset.
Read the guide Last updated: July 16, 2026Physics-informed neural networks, explained.
What a physics-informed neural network is in plain language: how physics gets into the training, why it needs less data, and what it changes for predictive maintenance.
Read the guide Last updated: July 16, 2026Predicting remaining useful life, method by method.
What remaining useful life means, the four families of RUL prediction methods, what each needs before it can work, and how to choose one for equipment that rarely fails.
Read the guide Last updated: July 17, 2026Why black-box AI fails in maintenance.
Why black-box AI breaks down on critical equipment, from missing run-to-failure data to predictions nobody can verify, and what to demand instead.
Read the guideRun your operation smoothly,
predictably, indefinitely.
See Vermilion reason about a real failure mode in your environment. Thirty minutes, with one of our reliability engineers.