Manufacturing & Advanced Industrials · Technology, AI & Digital Transformation

AI Predictive Maintenance

Spot failing equipment early and plan repairs before breakdowns stop production

What it is

AI Predictive Maintenance

We design and implement Predictive Maintenance (PdM) and Reliability-Centred Maintenance (RCM) programmes. By combining vibration and temperature sensors with machine learning anomaly detection, we help maintenance teams move from reactive repairs to planned, condition-based work.

Typical duration: 6 to 12 weeks for a pilot; scalable across the plant
Industry: Manufacturing & Advanced Industrials

The business problem

Why clients need this

Unplanned breakdowns cost manufacturers significant lost output, while calendar-based maintenance often services healthy machines and consumes spares that were not needed.

01
Failures of critical motors, gearboxes, pumps, and compressors stop production with little warning.
02
Spares inventory is padded with slow-moving parts to cover the risk of sudden breakdowns.
03
Maintenance teams lack tools to tell whether vibration comes from unbalance, misalignment, or bearing wear.

Our approach

How we deliver it

1
Asset Criticality Ranking & FMEA
Ranking equipment by likelihood of failure, production impact, safety consequence, and repair lead time.
2
Sensor Selection & Connectivity
Installing tri-axial vibration, temperature, and current sensors connected through industrial IoT gateways.
3
Model Development
Building anomaly detection models from frequency spectra and operating data, and adding remaining-life estimates where failure history allows.
4
CMMS & Workflow Integration
Generating work orders in your CMMS or ERP automatically when alert thresholds are crossed.

Data & AI

How technology strengthens this service

Models run on edge gateways to analyse high-frequency vibration data close to the machine, aiming to detect early-stage bearing and gear faults well before noise or temperature rise makes them obvious.

How we use AI across our work

Typical deliverables

What you receive

Asset Criticality & Failure Modes and Effects Analysis (FMEA) Matrix
Sensor & Gateway Deployment Architecture for the pilot area
Machine Health Dashboard (cloud or on-premise) with Automated Alerts
CMMS Work-Order Integration

Outcomes

What changes for you

Fewer unplanned stoppages on critical equipment, tracked against a pre-pilot baseline
Maintenance effort and spares directed by measured condition rather than the calendar
A tested pilot and business case for scaling across the plant

Who it is for

Continuous process industries (cement, steel, paper, chemicals)
Heavy manufacturing & fabrication plants
Power utilities & pumping stations
Logistics & automated material handling hubs

FAQs

Common questions

Which machines are best suited to predictive maintenance?+

Critical rotating equipment: motors, gearboxes, centrifugal pumps, large fans, air compressors, extruders, and CNC spindles.

Does this need cloud connectivity, or can it run on-premise?+

Either. We can deploy to a secure cloud or keep everything on-premise within your OT network, including air-gapped environments.

How much historical failure data do we need?+

Very little to start. Anomaly detection models learn a machine's normal behaviour over the first few weeks after installation and flag deviations from it. Failure history, where available, improves remaining-life estimates.