AI Predictive Maintenance

Prevent failure, protect revenue
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AI Predictive Maintenance
AI Predictive Maintenance
Downtime is expensive, whether it’s a production line, a train, or a ride. Prevent failure before it happens with AI Predictive Maintenance for safer operations, longer asset life, lower maintenance cost, and minimal disruption.

What is AI Predictive Maintenance?

AI-Powered Inventory Optimisation AI Predictive Maintenance combines artificial intelligence with reliability science to predict when machines, systems, or components are likely to fail. By analysing asset data, usage patterns, environmental conditions, and historical maintenance records, it calculates the hazard rate - the probability that a failure will occur at a given point in time which is used to generate a hazard curve.

This moves organisations from time-based maintenance to condition-based reliability.

How it can help your business

Reduce downtime and lost revenue

By predicting failures before they occur, maintenance can be scheduled at the optimal time, keeping production, rides, vehicles, and systems running without interruption.

Lower maintenance costs

Fewer emergency repairs, fewer unnecessary part replacements, and better use of engineering time mean significant operational savings.

Extend asset lifespan

AI identifies the conditions that accelerate wear, allowing operators to adjust usage or environment to maximise equipment life.

Improve safety and compliance

Predicting failures before they happen reduces the risk of incidents, ensuring compliance with safety and maintenance regulations across all sites.

Build trust and customer confidence

Consistent uptime and reliable operations build brand confidence, particularly in service industries like transport, retail, and leisure.

Create smarter maintenance planning

Maintenance budgets and schedules can be aligned with real asset risk, improving resource allocation and long-term capital planning.

How it works

Reliability and hazard modelling

The AI analyses operational, sensor, and maintenance data to calculate the hazard rate - the probability that an asset will fail at a specific time or usage point. It builds hazard curves that evolve as new data is captured, providing dynamic insights into equipment health and failure likelihood.

Data-driven maintenance forecasting

Machine learning models combine historical failure data, component usage, load profiles, and environmental conditions to forecast when maintenance should be scheduled for maximum efficiency.

Failure mode identification

The AI learns the specific failure modes that most affect your assets, allowing engineers to focus on high-risk components and intervene before they degrade.

Integration with existing systems

Forecast outputs are delivered directly into your existing Reliability-Centred Maintenance (RCM) software, CMMS/MMS, ERP, or asset management environments, no new system required.

Continuous improvement

Every completed maintenance cycle refines the model, making each new forecast more accurate and aligned with real-world asset behaviour.
Trusted by leading organisations

Industry-tailored AI Predictive Maintenance

Industry Predictive Maintenance Equipment / Assets
Retail
  • HVAC & air-handling units
  • Escalators & lifts
  • Automatic doors & heavy shutters
  • Self-checkout infrastructure
  • Electrical switchgear & building power distribution
Supermarkets / Grocery Retail
  • Refrigeration rack systems
  • Bakery production equipment (ovens, mixers, proofers)
  • Walk-in freezers, chillers & cold rooms
  • HVAC & large ventilation systems
  • Electrical distribution panels & power systems
  • Backup generators
CPG
  • High-speed production lines
  • Industrial mixers, blenders, emulsifiers, reactors
  • Filling, bottling, packaging lines
  • Compressed air systems
  • Boilers, heat exchangers & steam systems
  • Robotics (pick/pack/palletising)
  • Industrial refrigeration/chillers for process cooling
  • Conveyor networks
Hospitality
  • Walk-in cold rooms & commercial refrigeration
  • HVAC & hotel-wide air-handling systems
  • Commercial boilers & hot-water systems
  • Industrial kitchen equipment
  • Laundry plant systems
  • Passenger lifts
  • Backup generators
Manufacturing
  • CNC machines & machining centres (common in industrial manufacturing)
  • Injection moulding machines & hydraulic presses
  • Furnaces, kilns, curing & heat-treatment systems
  • AGVs & automated warehouse transport systems
  • Large industrial motors, drives & PLC systems
  • Industrial chillers & cooling towers
  • Boilers & steam systems
  • Compressed air plants
  • Industrial robots & automation cells
Private Healthcare
  • MRI, CT, PET & X-ray systems
  • Surgical theatre HVAC & pressurisation systems
  • Autoclaves & sterilisation systems
  • Laboratory analysers
  • Medical-grade refrigeration
  • Operating theatre lighting & control systems
  • Backup generators & hospital electrical infrastructure

Unmatched precision in five simple steps

1

Gather

We collect and structure historical maintenance records, sensor readings, usage data, and operational context.

Enrich

External factors such as environment, temperature, humidity, or load are layered in to expose hidden patterns of degradation.
2

Optimise

Hazard rate and reliability models identify when and why failures occur, generating hazard curves that predict remaining useful life.
3

Deliver

AI predicts the optimal maintenance window for each asset to balance cost, safety, and availability.
4

Improve

Every maintenance event feeds back into the model, continually improving accuracy and reducing unplanned downtime over time.
5

Case Studies

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A partnership built on ROI

We partner with you to implement AI Predictive Maintenance that works for your business, your people, and your customers, responsibly and at scale.

Our pricing structure is simple - we ask for a 10:1 ROI. £1 invested = minimum £10 saved. We handle all the heavy lifting, so you don’t need a team of data scientists.

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FAQ

It applies hazard rate and reliability models to calculate the probability of failure over time. Machine learning identifies patterns in asset data, such as temperature, vibration, load, or run-time, to predict when maintenance will be most effective.

The hazard rate measures the likelihood that an asset will fail at a specific point in time. By tracking how this rate changes, the hazard curve gives maintenance teams foresight into when and why failures are likely to occur, enabling smarter, condition-based maintenance.

Preventive maintenance follows fixed schedules, whether maintenance is needed or not. Predictive maintenance is dynamic, driven by data that reflects actual usage, wear, and risk, meaning maintenance happens only when required.

Predicting failures before they happen eliminates the risk of sudden breakdowns that can cause safety incidents or regulatory breaches, ensuring safer, more compliant operations.

Yes. For industries like transport, logistics, manufacturing, hospitality or theme parks, fewer breakdowns mean more reliable service and fewer customer disruptions, protecting trust and brand reputation.

The model uses historical maintenance logs, sensor and IoT data, operating hours, load and usage data, and environmental conditions such as temperature or vibration. Where sensors aren’t available, operational and inspection records can still be used to build reliable models.

Yes. The AI models are designed for any environment where equipment reliability matters, from manufacturing lines and HVAC systems to refrigeration, vehicles, and rides in leisure operations.

Yes. The model learns distinct failure patterns for each component or asset type, prioritising the most critical risks for intervention.

It requires sensor data to improve accuracy and understand the forces acting on the asset. In many cases, this data is captured through existing hardwired sensors rather than additional IoT devices.

No. SolvedBy.Ai delivers advanced AI models, not a platform. Forecasts and recommendations integrate directly into your existing Reliability-Centred Maintenance (RCM) software, CMMS/MMS, ERP, or asset management systems.

No. SolvedBy.Ai handles the modelling, data preparation, and integration. Most organisations see results within the first few maintenance cycles after deployment.

No. SolvedBy.Ai’s data scientists and PHD AI experts build, maintain, and refine the models. Your maintenance teams simply use the recommendations to plan and act.

Models are retrained periodically based on new operational and maintenance data. Updates align with your maintenance or reporting cycles rather than real-time monitoring.

Every forecast includes an explainable rationale showing which variables drive the prediction, making AI-driven maintenance decisions auditable and defensible.

Yes. SolvedBy.Ai is certified to ISO 42001:2023 (AI management) and ISO 27001:2022 (information security), and complies fully with GDPR and all data protection standards.

Always. AI provides foresight and recommendations, but final maintenance decisions remain with your engineers or reliability managers.

RCM identifies what maintenance should be done; AI Predictive Maintenance determines when it should be done. The two approaches are complementary; RCM defines strategy, while AI provides timing based on real-world data.

Yes. The AI can use existing RCM or reliability databases as part of its model training, improving accuracy and alignment with established maintenance frameworks.

TL;DR

SolvedBy.Ai’s AI Predictive Maintenance uses hazard rate and reliability modelling to forecast when assets will fail, enabling proactive maintenance before downtime occurs. By combining operational data, sensor readings, and environmental factors, it helps organisations across every sector increase uptime, improve safety, and cut maintenance costs, transforming maintenance from reactive to predictive.
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