Increase asset reliability and optimize production with OPUS
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. OPUS has been uniquely designed to continuously learn from historical and live data to predict future performance.
When using OPUS, operators benefit from the following advanced insights,
- 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 the insights to more accurately plan maintenance activities, increase equipment lifespan, asset uptime, and reduce maintenance costs and efforts.
OPUS’s unique holistic approach to analysing time-series data across the entire plant, rather than just individual pieces of equipment, means the no code AI platform can detect minute anomalies, with accurate predictions often days-to-weeks in advance. Customer teams can act on these insights, plan interventions, implement predictive maintenance and optimize operations.
Key Benefits:
- Rapid deployment
- Equipment agnostic
- Unlimited users and AI models
- Simple to use model wizard
- Competitive Pricing structure
Join us on this transformative journey as we redefine the possibilities for rotating equipment reliability and empower your facility’s success.
