Detect early-stage equipment degradation, predict failures before downtime occurs, and improve reliability using real-time, data-driven models—without relying on static rules or thresholds.
VROC’s Predictive Maintenance & Reliability solution uses multivariate AI models trained on historic operating behaviour to identify subtle deviations that traditional condition monitoring systems miss. Engineers gain earlier insight, clearer root cause understanding, and the confidence to act before failures impact operations.
Most maintenance strategies rely on:
Fixed thresholds
Single-sensor alarms
Reactive or time-based maintenance schedules
While these methods can identify known failure modes, they often detect issues too late, after degradation has already progressed. They also struggle in complex, dynamic environments where equipment behaviour changes with load, process conditions, or operating context.
As a result, teams face:
Unplanned downtime
Excessive false alarms
Limited insight into why failures occur
Difficulty extending the life of aging assets
VROC’s predictive maintenance models are trained using historical data from periods when assets were operating in a healthy state. Instead of relying on predefined rules, the AI learns the normal multivariate behaviour of the equipment and process.
Once deployed, the model continuously monitors live data to:
Detect subtle deviations across multiple sensors
Quantify how far current behaviour has drifted from normal
Identify which components or parameters are contributing to the deviation
This enables earlier detection, clearer diagnosis, and more effective intervention.
Learn More: The Business Case for Machine Learning in Predictive Maintenance
* Time-to-Failure (TTF) forecasting
* Remaining Useful Life (RUL) estimation
* Early warnings well before alarms or shutdowns
* Multivariate Root Cause Analysis at sensor and component level
* Drill-down insights that show what is driving degradation
* Supports faster, more confident decision-making
* AI builds dynamic operating envelopes based on real behaviour
* Detects deviation even when individual sensors remain within limits
* Focus on meaningful deviations, not noise
* Prioritise issues based on impact and severity
VROC is built so engineers remain in control.
No-code model creation and deployment
No data science team required
Engineers apply their domain knowledge to interpret insights
Models are transparent, explainable, and auditable
This approach complements—not replaces—engineering expertise.
VROC’s Predictive Maintenance & Reliability solution is used across a wide range of assets and environments, including:
Rotating equipment (motors, pumps, compressors)
Critical process equipment
Fleet and mixed-asset environments
Brownfield assets with limited failure history
Remote and distributed operations
Predictive Maintenance & Reliability is delivered through OPUS, VROC’s industrial AI engine, and integrates seamlessly with:
DataHUB+ for real-time monitoring, dashboards, and KPI-to-sensor traceability
OASIS for operational control and execution
This ensures insights move from data → prediction → action without fragmentation.
VROC’s implementation approach is designed for operational environments:
Connect to existing sensors and historians quickly
Build and deploy models in weeks, not months
Iterate with engineers and operators as conditions change
Scale across assets, sites, and fleets
Client supply of historical data and set-up of real-time streaming
Data ingestion and real-time data streaming connection
Client training and model generation
Models in production. Client starts delivering business value
Discover how VROC helps reliability teams prevent failures, reduce downtime, and extend asset life using predictive, data-driven insights.
Explore Our Predictive Maintenance Case StudiesWhether you’re launching your first pilot or scaling AI across your enterprise, VROC’s end-to-end platform and expert team can help you unlock data, optimise performance, and accelerate results.
Interested in a demo of one of our data solution products?
DataHUB4.0 is our enterprise data historian solution, OPUS is our Auto AI platform and OASIS is our remote control solution for Smart Cities and Facilities.
Book your demo with our team today!
Complete the form below and we’ll connect you with the right VROC expert to discuss your project. Whether you’re launching a pilot, scaling AI across your enterprise, or integrating complex systems, we’ll help you turn your data into actionable insights—fast, efficiently, and with confidence.
The efficient deployment, continuous retraining of models with live data and monitoring of model accuracy falls under the categorisation called MLOps. As businesses have hundreds and even.
Learn more about DataHUB+, VROC's enterprise data historian and visualization platform. Complete the form to download the product sheet.
Learn how OASIS unifies your systems, streams real-time data, and gives you full control of your smart facility—remotely and efficiently. Complete the form to access the product sheet.
Discover how OPUS, VROC’s no-code Industrial AI platform, turns your operational data into actionable insights. Complete the form below to access the product sheet and learn how you can predict failures, optimise processes, and accelerate AI adoption across your facility.
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