Predictive Maintenance
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Quality, Maintenance & Production
Predictive Maintenance
Predict failures from machine data, and auto-raise the work order before the line stops.
Analyses sensor, PLC, maintenance-log and machine-runtime data to predict failures, then auto-raises prioritised work orders with the likely cause and required spares before a breakdown stops the line.
20–40%
less unplanned downtime
10–20%
lower maintenance cost
Hours–days
earlier failure warning
The impact
Less unplanned downtime and lower maintenance cost, by fixing machines just before they fail rather than after.
20–40%
less unplanned downtime
10–20%
lower maintenance cost
Hours–days
earlier failure warning
Who it's for
- Plants with bottleneck or high-value critical machines
- Factories stuck on run-to-failure or fixed-calendar servicing
- Maintenance teams firefighting unplanned breakdowns
- Owners with legacy machines and no modern IoT
What you get
- Machine-data ingestion (sensors, PLC/SCADA, logs)
- Per-machine failure-drift detection model
- Time-to-failure ranking by production impact
- Auto-raised work orders with cause and spares
- WhatsApp/dashboard maintenance alerts
The pipeline
How it works, end to end.
Every step is built, benchmarked, and wired into your stack. Here is exactly what happens.
Ingest machine data
It pulls vibration/temperature/current from sensors or PLC/SCADA, plus runtime hours and historical maintenance logs.
Baseline & detect
The AI learns each machine’s normal signature and detects drift that precedes failure (bearing wear, overheating, current spikes).
Predict & prioritise
It estimates time-to-failure and ranks machines by risk and production impact.
Auto work order
A maintenance work order is raised with the likely cause, recommended action and spares to keep ready.
Alert & schedule
Maintenance gets a WhatsApp/dashboard alert and the job is slotted into a low-impact window.
Human checkpoint
The maintenance lead confirms/closes the work order and logs the actual finding, which improves the model.
Ingest machine data
It pulls vibration/temperature/current from sensors or PLC/SCADA, plus runtime hours and historical maintenance logs.
Baseline & detect
The AI learns each machine’s normal signature and detects drift that precedes failure (bearing wear, overheating, current spikes).
Predict & prioritise
It estimates time-to-failure and ranks machines by risk and production impact.
Auto work order
A maintenance work order is raised with the likely cause, recommended action and spares to keep ready.
Alert & schedule
Maintenance gets a WhatsApp/dashboard alert and the job is slotted into a low-impact window.
Human checkpoint
The maintenance lead confirms/closes the work order and logs the actual finding, which improves the model.
Under the hood
The data flow, wired into your tools.
Reads
- Sensor vibration/temperature/current
- PLC/SCADA tags and machine runtime
- Historical maintenance and breakdown logs
- Spares inventory
Produces
- Failure predictions with time-to-failure
- Prioritised maintenance work orders
- Recommended spares to keep ready
- Maintenance alerts and schedule slots
Before & after
What changes once it ships.
Machines run to failure, breakdown stops a critical order
Drift caught hours–days early, fixed in a planned window
Fixed-calendar servicing wastes spend or misses failures
Service triggered by actual machine condition
Breakdown means scramble for cause and spares
Work order arrives with likely cause and spares listed
Why it matters
The business case
On a bottleneck machine, an unplanned breakdown can cost ₹2–3 lakh a shift in lost output plus rushed spares and overtime, and it always seems to happen during a critical order. Run-to-failure and fixed-calendar servicing either waste maintenance spend or miss the failure entirely. Catching the drift hours to days early and auto-raising a work order with the likely cause and spares cuts unplanned downtime 20–40% and trims maintenance cost, which on a single critical cell can pay for the whole programme in a quarter.
FAQ
Predictive Maintenance: your questions, answered.
We have no IoT sensors, can we still use this?+
Yes. We start with what you already have (PLC/SCADA tags, machine runtime, breakdown history and maintenance logs) and add a few low-cost retrofit sensors (vibration/temperature/current clamps) only where the payback is clear. No full IoT overhaul needed.
How does it connect to old machines without modern controls?+
For legacy machines we use clamp-on or magnetic-mount sensors and an edge gateway, plus structured maintenance-log analysis. It’s a non-invasive retrofit, not a re-wiring project.
Does it actually create work orders or just alert?+
Both. It raises a prioritised work order with likely cause and recommended spares in your CMMS/sheet, and alerts the maintenance team, so a prediction turns into action, not just a notification.
How accurate is the failure prediction?+
Accuracy grows as it learns each machine. Early on it’s strong at catching clear drift (overheating, rising vibration); over time, with confirmed outcomes fed back, time-to-failure estimates tighten meaningfully.
Where does the sensor data live, is it secure?+
Processing can run on an on-prem edge gateway with only insights sent out, or in an India-resident cloud with encryption and role-based access. You choose based on your IT and security policy.
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