Turned Fragmented Sensor Data Into Real-Time Predictive Maintenance

Background

Tata Motors' production lines operate across multiple shop-floor stations — stamping, welding, and paint — each generating continuous sensor telemetry from a mix of brownfield PLCs and SCADA networks that were never designed to feed a single analytics layer.

The Challenge

Production and maintenance teams were drowning in raw, siloed sensor streams from fragmented equipment across stamping, welding, and paint shop systems. Manually compiling that data into actionable insights took days — by which time the information was stale and early warning signs had already been missed.

The result was higher defect escape rates and sudden, high-cost downtime from unplanned stoppages on heavy production lines — failures that could have been caught earlier with real-time visibility into vibration and thermal anomalies.

The Solution

BotIntelli deployed a single pane-of-glass IIoT analytics platform that ingests edge-to-cloud telemetry over MQTT and OPC UA industrial protocols, integrating directly with existing brownfield PLC and SCADA infrastructure without requiring hardware replacement.

The platform combines unsupervised AI with ISO 10816-3 vibration-monitoring standards to convert raw sensor streams into intuitive visual dashboards and auto-prioritized work orders — triggered in real time when anomalies cross defined thresholds.

Implementation & Impact

• From days to real time

  Replaced a manual, multi-day data compilation process across stamping, welding, and paint shop systems with continuous, real-time visibility into equipment health.

• Proactive instead of reactive maintenance

  Shifted from responding after failure to acting on early warning signs — reducing unplanned downtime and the high cost of sudden stoppages on heavy production lines.

• Earlier defect detection

  Caught quality issues earlier in the production process, reducing the need for costly off-line curing and rework.

• No hardware replacement required

  Delivered the capability without requiring new sensors or PLC/SCADA hardware refreshes — using existing industrial protocols to connect brownfield infrastructure.

Looking Ahead

With the sensor-to-dashboard pipeline established, Tata Motors is positioned to extend the same MQTT/OPC UA ingestion layer to additional production lines and shop-floor stations — scaling predictive maintenance without rebuilding the analytics foundation.