Introducing a new standard of Predictive Maintenance

Improve asset reliability and availability

Heavy industries are plagued by unplanned downtime, costing upwards of $100,000 per hour for large manufacturers. It's no wonder that predictive maintenance is called the 'holy grail' of maintenance engineering, and why an increasing number of process industries are adopting predictive maintenance.

Unlike rule-based PdM approaches, VROC's machine learning-based predictive maintenance platform OPUS, uses sensor data and machine learning to detect anomalies and predict failures ahead of time - without the high rate of false positives that is common with threshold-based PdM. Most anomalies experience unexpected failure patterns and so OPUS continuously learns from live data to predict future performance.

When using OPUS, no-code AI predictive maintenance software, operators benefit from advanced insights, including

• Early failure detection and prediction (often weeks in advance)
• Time to failure prediction
• Root cause analysis
• Real time condition monitoring
• Holistic analysis across entire plant for detection of contributing factors from seemingly un-connected processes

With OPUS Engineers and Operators can use predictive maintenance to more accurately plan maintenance activities, increasing equipment lifespan, asset uptime and reduced maintenance costs and efforts.

One predictive maintenance platform for all equipment

OPUS can detect failures on new equipment, as well as ageing equipment. Without relying on AI model libraries, the single platform can be used throughout an organisation to model all assets, regardless of model type or brand, so that predictive maintenance can be adopted at an enterprise level.

Condition-based maintenance is enhanced with predictive analytics which discovers connections in data earlier, which if analysed manually would take months to uncover. These AI insights can be rapidly produced by asset reliability engineers and maintenance engineers without any programming or coding knowledge. The existing team then reviews the insights and makes informed business decisions based on their operational knowledge.

In this approach the platform doesn’t replace these highly skilled individuals but enhances their decision-making ability, resulting in increased operational effectiveness.