Predict Machine Failures Before They Happen

Improve Maintenance Planning with Model Insights

For today's large scale manufacturers, unplanned machine downtime can lead to significant financial losses and operational inefficiencies. Predictive maintenance, powered by Time To Failure AI Models, allows you plan interventions, preventing sudden machine failures and downtime. With VROC's no-code AI platform, OPUS, you can effortlessly build and deploy these powerful models, ensuring maximum uptime and optimal performance for your operations.

Watch the video to see it in action

Benefits of Time to Failure AI Models

Time to Failure AI Models analyze historical and real-time sensor and operations data to predict when a machine or process is likely to fail. 

This allows for timely interventions and improved planned maintenance, reducing unexpected downtimes.

• Cost Savings: Reduce unplanned maintenance costs, minimize the need for emergency repairs and technical call out fees.
• Increased Efficiency: Ensure your machines are always operating at peak performance.
• Reduced Downtime: Plan maintenance activities around your production schedule to avoid disruptions.
• Extended Equipment Life: Prevent overuse and excessive wear and tear on your machinery.

Built for Operators and Engineers

With VROC’s OPUS platform, building and deploying Time to Failure AI Models is a seamless process that doesn’t require any coding skills. Engineers and operators alike can utilize OPUS to create sophisticated predictive models using industrial time series data.

• No-Code Platform: Intuitive model wizard allows users to build machine learning models without coding.
• For Engineers and Operators: Designed to be used by non-data professionals.
• Rapid Deployment: Quickly build, test, and implement models without lengthy development cycles.
• Model Management: OPUS's inbuilt MLOps ensures models remain accurate, continuously monitoring and analysing machine performance
• Scalable: Quickly build models for all critical machinery and processes enterprise wide

Interested to learn more, take a look at this case study which showcases how a time to fail model helps operators prevent a turbine compressor from failing.