AI Predictive Maintenance
Predict equipment failures before they happen. AI analyses sensor data, maintenance history, and operational patterns to optimise maintenance schedules and prevent costly unplanned downtime.
The Problem
Why this matters
Unplanned equipment failures cost industrial organisations an average of £200,000 per hour in lost production, emergency repairs, and downstream disruption. Traditional time-based maintenance is wasteful — replacing components on a fixed schedule regardless of actual condition means either maintaining too early (wasting parts and labour) or too late (after failure has occurred). Reactive maintenance after a breakdown is the most expensive approach, yet it remains the default for many organisations. Asset-intensive businesses need to predict failures and intervene at the optimal moment.
The Solution
How AI solves this
AI predictive maintenance analyses real-time sensor data (vibration, temperature, pressure, current), combined with maintenance history and operating conditions, to predict when equipment components will degrade to the point of failure. Machine learning models identify subtle patterns and anomalies that precede failures, providing days or weeks of advance warning. This enables maintenance to be scheduled at the most convenient and cost-effective time, eliminating both unplanned downtime and unnecessary preventive maintenance.
Benefits
What you gain
50% Less Unplanned Downtime
Predict failures days or weeks in advance, allowing maintenance to be scheduled during planned windows rather than responding to breakdowns.
30% Lower Maintenance Costs
Eliminate unnecessary time-based maintenance activities. Service equipment based on actual condition rather than arbitrary schedules.
Extended Asset Life
Early detection and intervention prevent secondary damage from cascading failures, extending the useful life of expensive equipment.
Optimised Spare Parts
Predict which parts will be needed and when, reducing spare parts inventory while ensuring availability for planned maintenance.
Safety Improvement
Prevent catastrophic failures that endanger personnel, the environment, and surrounding equipment by detecting degradation early.
Process
How it works
Sensor Data Collection
IoT sensors capture real-time data from equipment — vibration, temperature, pressure, current, acoustic emissions — and stream it to the analytics platform.
Baseline Modelling
AI learns the normal operating signature of each asset under various conditions, establishing baselines against which anomalies are measured.
Anomaly Detection
Machine learning models continuously analyse sensor data for deviations from normal patterns that indicate developing faults or component degradation.
Failure Prediction
When degradation is detected, the system predicts the remaining useful life of the affected component and the likely failure mode.
Maintenance Scheduling
Predictive alerts are integrated with your CMMS to schedule maintenance at the optimal time, with specific work orders and parts requirements.
Industries
Who uses this
Technology
Tools we use
FAQ
Frequently asked questions
The required sensors depend on the equipment type and failure modes. Common sensors include vibration accelerometers, temperature probes, current transformers, pressure transducers, and acoustic emission sensors. Many modern machines already have built-in sensors whose data can be leveraged. We assess your equipment and recommend the most cost-effective monitoring approach.
Prediction horizons vary by failure mode. Bearing degradation can be detected weeks to months in advance, while electrical faults may provide days to weeks of warning. The system provides estimated remaining useful life with confidence intervals so you can plan maintenance accordingly.
Yes. Retrofit sensors can be installed on virtually any equipment without modification or downtime. Vibration sensors, temperature sensors, and current clamps can be added externally to older machines that lack built-in monitoring. Our team handles the sensor selection and installation planning.
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