Predictive Maintenance
for a Defense Fleet
A defense fleet operator shifted from scheduled, calendar-based servicing to data-driven condition-based maintenance — using explainable ML models and ISO 13374 data models to predict component degradation, optimize spares, and improve fleet readiness.
Discuss Your Project →A defense fleet operator held back by scheduled servicing and siloed data
A defense fleet operator engaged We As Web to reduce unplanned downtime and sustainment costs across its platforms. Historically, the organization relied on scheduled, calendar-based servicing. While the operator possessed vast amounts of maintenance logs, sensor telemetry, and parts records, this critical data was siloed and rarely leveraged to anticipate platform failures. The client sought to transition to a modern condition-based maintenance (CBM+) approach, which defense sustainment organizations are increasingly adopting to keep fleets mission-ready.
What prompted the project
To modernize their sustainment operations, the defense fleet operator had to balance availability, cost, and explainability — with humans staying firmly in control of every decision.
Maximize Availability
Keep more platforms mission-ready while simultaneously lowering the overall lifecycle cost of the fleet.
Adopt Condition-Based Maintenance (CBM+)
Transition away from reactive and calendar-based servicing toward data-driven, predictive interventions grounded in real component state.
Optimize Spares Inventory
Accurately anticipate parts demand to reduce stock-outs that ground platforms, while avoiding capital being tied up in unnecessary inventory.
Human-in-the-Loop Decision Support
Implement a system that keeps maintenance engineers in control of every decision, rather than relying on fully automated "black-box" actions.
Why an expert partner was required
The initiative faced several structural and data-centric hurdles — particularly the lack of a unified, trustworthy view of fleet health.
What We As Web delivered
We As Web delivered a comprehensive predictive-maintenance analytics platform designed as a decision-support tool — not an autonomous decision-maker.
Unified Data Foundation
Integrated, cleaned, and normalized maintenance, telemetry, and supply data around the ISO 13374 condition-monitoring data model.
ML Degradation & RUL Models
Machine-learning models that detect early-warning signatures of component degradation and estimate remaining useful life (RUL).
Explainable Indicators
Models surface explainable indicators — not black-box outputs — rigorously validated against historical failure records.
Decision-Support Dashboards
Dashboards and alerts that help sustainment teams plan maintenance, prioritize platforms, and optimize spares.
From calendar-based servicing to data-driven sustainment
The shift to predictive analytics provided immediate and long-term sustainment benefits — across fleet health, supply chain, and human control.
Successfully created a single, governed data foundation for fleet health — a crucial prerequisite for any credible predictive-maintenance program.
Delivered trusted, explainable ML models that accurately flag early signs of component wear and estimate remaining useful life.
The operator successfully shifted from calendar-based servicing to condition-based interventions, directly targeting the specific components that drive the most downtime.
Enabled spares-holding optimization against predicted demand, significantly reducing both operational stock-outs and unnecessary capital expenditure.
Explainable AI for mission-critical sustainment
A focused stack — machine learning, digital-twin modeling, and the ISO 13374 condition-monitoring standard — designed for trust in a defense context where wrong decisions are costly.
When fleet readiness depends on explainable prediction
In defense sustainment, "the model said so" is not a decision. By grounding the analytics layer in the ISO 13374 condition-monitoring standard, building explainable ML models, and keeping engineers firmly in the loop, we gave this defense fleet operator the ability to predict component degradation and plan interventions before failures occur.
The result is a sustainment organization that runs on data — not on the calendar, and not on reactive firefighting. Spare parts are aligned to predicted demand, engineers trust the indicators they see, and the fleet stays mission-ready at a lower lifecycle cost. For any organization running critical platforms, this is what predictive maintenance looks like when it's designed for trust, not just accuracy.