An AI-powered predictive maintenance platform built to forecast tractor health, optimise service schedules, and connect legacy and modern fleets into one intelligent system.
Overview
A leading Indian tractor OEM wanted to modernise its nationwide service network across both legacy mechanical tractors and modern ECU-equipped models.
The existing service approach was largely calendar-based, which meant maintenance decisions were not always aligned with real operating conditions, component health, field usage, or early failure signals.
DigiWagon built an AI-powered predictive maintenance platform that captures tractor telemetry during service visits, forecasts component health, predicts Remaining Useful Life, and dynamically recommends maintenance actions.
To extend the intelligence layer across the complete fleet, the solution also included a mechanical-to-digital retrofit kit that brought legacy, ECU-less tractors onto the same monitoring platform as newer connected models.
Why it mattered
The OEM’s service network relied on fixed maintenance schedules and reactive diagnosis. This created a gap between how tractors were actually used in the field and how they were serviced at workshops.
Legacy tractors had no onboard electronics, while newer ECU-equipped models generated useful telemetry that was not being fully used after service sessions.
The OEM needed a system that could read operating signals, identify abnormal behaviour, forecast component degradation, and guide service engineers before a failure happened in the field.
In agricultural machinery, breakdowns during peak seasons can create high service pressure, customer dissatisfaction, and operational losses.
Predictive maintenance helps OEMs move from routine servicing to condition-based servicing, where every tractor is assessed based on real usage, sensor behaviour, component wear, and service history.
For mixed fleets, the challenge is not only building AI models. It is also creating a common data layer across older mechanical tractors and newer ECU-equipped machines.
How we delivered
Discover
Capture
Predict
Deploy
What we built
DigiWagon built an AI-powered predictive maintenance ecosystem that connects tractor telemetry, sensor retrofitting, service-station workflows, AI models, edge deployment, and service recommendations into one unified platform.
The system helps service engineers identify issues earlier, forecast component life, adjust service intervals, and support both legacy and modern tractors through a single service-bay interface.
Key features
Impact
The platform helped the OEM shift from fixed service schedules to AI-backed, condition-based maintenance across its tractor fleet.
unified predictive maintenance platform built for legacy and modern tractors.
tractor generations supported: mechanical legacy tractors and ECU-equipped modern tractors.
sensor retrofit model enabled for legacy tractors.
sensor data model supported for modern ECU-equipped tractors.
critical subsystems monitored: engine oil, coolant, fuel filter, air cleaner, and injector health.
faster component issue detection through anomaly alerts and service-bay intelligence.
reduction in unplanned downtime risk through early degradation signals.
improvement in service planning efficiency through dynamic maintenance scheduling.
In closing
The Smart Predictive Maintenance Platform helped the tractor OEM move from reactive and calendar-based servicing to AI-driven, condition-based maintenance.
By combining sensor retrofitting, ECU integration, edge deployment, anomaly detection, RUL prediction, dynamic scheduling, cooling health intelligence, trip-level analytics, and human-in-the-loop learning, DigiWagon created a unified service intelligence layer for the OEM’s active fleet.
The result is a scalable predictive maintenance ecosystem that improves service accuracy, reduces downtime risk, extends component life, and gives the OEM a deeper view of how tractors perform in real field conditions.
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