Predictive Maintenance for Ground Systems
When the Platform Fails, It's Rarely a Surprise, It's Just Too Late
If you’ve spent time in the field, you’ve seen this before. A vehicle checks green before a mission, but something is off. Maybe a servo is slow, a turret draws more current than usual, or engine torque feels sluggish. Nothing trips a fault code, so it rolls out.
Then it’s deadline after the mission, or worse, it fails during. The signs were there, but the systems we use to monitor readiness didn’t catch them. Not because the signals weren’t present, but because our models don’t know how to look for subtle change.
This is not about fault codes or alarms. It’s about understanding patterns of drift, the kind that spreads across sensors, builds over time, and only looks obvious in hindsight. The burden falls to the warfighter to notice, when that judgment should already be built into the system.
Airborne
Ground
Space
/ THE PROBLEM /
Thresholds Don’t Tell You What’s Coming
Most predictive maintenance today is really just threshold monitoring with new branding. If a signal crosses a line, it triggers an alert. If a code is thrown, it gets logged.
But degradation in ground systems doesn’t announce itself that clearly. It’s gradual. A cooling system loses efficiency. An actuator responds with more delay than it used to. Power consumption starts creeping up under normal loads. None of this triggers alerts, but it points to a trend.
What’s more, most systems treat each platform as an island. No fleet comparison, no context, no understanding of when a signal is out of pattern even if it hasn’t violated a limit. That’s not prediction. That’s reaction.
/ OUR SOLUTIONS /
Models That Learn Normal and Detect Deviation
Instead of hardcoding what failure looks like, we support teams in developing models that learn what healthy behavior is across time and sensors. Not based on fault labels, which are often missing or inconsistent, but from operational data itself.
The models learn from the patterns that appear during startup, transit, high load, and idle. They detect when behavior starts to diverge from what is typical for that platform and for others in its class.
When something shifts, the model can surface it in ways tailored to operational context. Not with vague alerts, but with specific signal-based changes backed by history. Our role is to help engineering teams design, evaluate, and integrate these capabilities into their existing sustainment pipelines.
/ TECHNICAL DEEPDIVE /
Focused Temporal Modeling for Real Operations
The system ingests multivariate time-series data from onboard telemetry. That includes engine temps, actuator loads, battery voltage, vibration, system pressures, and more. The signals are noisy and incomplete, but they contain structure.
We help defense OEMs and integrators implement models suited for this type of data:
- LSTM networks track long-range dependencies in slow-building degradation, like cooling loss or torque variance over time.
- Temporal Convolutional Networks work well for fast signals, like vibration bursts or sensor noise during gear changes.
- Lightweight attention mechanisms help identify which sensor behavior matters most during the early stages of drift.
We do not deliver prebuilt tools, we work directly with program teams to evaluate, build, or integrate architectures that match platform constraints and mission profiles.
These models can compare behavior not only against historical baselines, but also across peer assets if fleet data is available. When one vehicle starts drifting from fleet norms, even if it hasn’t failed yet, the system can flag it for review.
Outputs are designed to be operationally useful:
- Clear anomaly scores with change-over-time views
- Indicators of which sensors changed and when
- Integration with existing diagnostics or sustainment software, not full system replacement