Unexpected equipment failures are costly — not just in repairs, but in downtime, lost production, and safety risks. Predictive maintenance uses AI and machine learning to detect early signs of equipment degradation, allowing teams to take action before failures occur.

At VROC, we deliver no-code, AI-powered predictive maintenance that integrates seamlessly with your existing systems. Our models analyse historical and real-time process data to identify anomalies and predict when assets will need attention. Whether you operate in oil & gas, mining, manufacturing, or utilities, our platform helps you reduce downtime, optimise asset life, and maximise return on investment.

With predictive maintenance, maintenance schedules are based on actual asset condition — not fixed intervals or guesswork — enabling a smarter, more cost-effective approach.

How Predictive Maintenance Works

Predictive maintenance combines sensor data, process historian information, and AI-driven analytics to forecast equipment health.

The process:

Data collection – IoT sensors, SCADA systems, and plant historians gather time-series data.
AI model training – Machine learning algorithms identify normal operating behaviour and flag anomalies.
Real-time monitoring – Models continuously assess incoming data, generating alerts before failures occur.
Action planning – Maintenance is scheduled only when needed, minimising disruption.

OPUS machine learning predictive maintenance equipment dashboard examples

Benefits of Predictive Maintenance

Implementing predictive maintenance delivers measurable business value:

Reduce unplanned downtime – Identify and address issues before they cause production stops.
Lower maintenance costs – Replace parts only when necessary, reducing waste.
Optimise asset lifespan – Maximise the productive life of critical equipment.
Improve safety – Minimise the risk of hazardous failures.
Boost efficiency – Plan maintenance around production schedules.

Learn More: The Business Case for Machine Learning in Predictive Maintenance

Predictive vs Preventative vs Reactive Maintenance

Reactive maintenance – Fix equipment after it fails. Simple, but can be costly in downtime and emergency repairs.

Preventive maintenance – Schedule maintenance at set intervals to reduce the chance of failure. Effective, but may waste resources replacing healthy components.

Predictive maintenance – Use AI and data to determine when maintenance is truly required. Delivers maximum efficiency and cost savings.

Explore the full comparison

Table showing the pros and cons of Reactive, Preventative and Predictive maintenance methods

How to Choose the Right Predictive Maintenance Solution

Selecting the right PdM solution is critical to long-term success. Consider:

Integration capability – Can it connect to your current data sources?
Ease of use – Can non-technical teams build models without coding?
Scalability – Will it work across multiple assets and sites?
Accuracy & explainability – Does it provide understandable insights, not just alerts?
ROI tracking – Can you measure the savings and performance improvements?

VROC's platform is built to integrate with your existing equipment and systems, providing maintenance engineers and operators with easy to explain AI insights - Discover OPUS

Implementation Roadmap

A successful PdM rollout follows these steps:

Pilot program – Start with a single asset or process to prove value.
Data integration – Connect all relevant sources into one platform.
Model building – Train and deploy AI models using historical and live data.
Implement Maintenance – Use AI alerts and predictions to plan and implement maintenance activities
Scale up – Expand to more equipment and sites (repeating steps 2-4).
Measure & refine – Track ROI and optimise models.

Predictive Maintenance FAQs

Predictive maintenance uses data and AI to predict when equipment will require service, preventing failures and reducing costs.

Preventive maintenance works on fixed schedules; predictive uses asset condition data to schedule only when necessary.

Industries with high-value assets: oil & gas, mining, manufacturing, utilities, defence and transport.

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The implementation timeline from purchase order (PO) to full deployment and operation varies by client. The initial setup, including data ingestion and integration, typically takes about three weeks. However, in some cases, clients have achieved full deployment within just two weeks from the start of engagement.

VROC does not place a limit on the number of the models the user can produce for each asset or equipment. The user can create as many models as they need to analyse the asset.

We recommend two years’ worth of historical data to produce the most accurate predictions, however we have had many successful predictions without significant data sets being available.

Alternatively, VROC AI can create digital twin models that learn from real-time data as it flows in. This approach is especially beneficial when clients lack access to historical data or when the available data has low integrity. By continuously processing and learning from incoming data, the digital twin enables VROC AI to refine its predictions over time, increasing accuracy as it becomes more familiar with the asset or process.