Introducing a new standard of Predictive Maintenance

Improve maintenance planning and equipment reliability

Process industries are plagued by unplanned downtime, at a cost 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 businesses are adopting it.

Unlike rule-based predictive maintenance, 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 false positives common with threshold-based predictive maintenance. As most anomalies occur from unexpected failure patterns, OPUS has been uniquely designed to continuously learn from historical and live data to predict future performance.

With OPUS, operators benefit from the following advanced insights,

• Early deviation 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, increase equipment lifespan, asset uptime, and reduce maintenance costs and efforts.

One solution for ALL equipment

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

Condition-based maintenance is enhanced with AI which discovers machinery and equipment issues earlier. 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.