Predictive Asset Analytics (PRiSM) is a predictive asset analytics solution that can provide early warning notification and diagnosis of equipment issues days, weeks or months before failure. Predictive Asset Analytics helps asset-intensive organizations reduce equipment downtime, increase reliability and improve performance while reducing operations and maintenance (O&M) expenditures.
Predictive Asset Analytics is based on a proprietary algorithm called OPTiCS that uses Advanced Pattern Recognition (APR) and machine learning technology. Predictive Asset Analytics learns an asset’s unique operating profile during all loading, ambient and operational process conditions. Existing machinery sensor data is input into the software’s advanced modeling process and compared to real-time operating data to determine and alert upon subtle deviations from expected equipment behavior. Once an issue has been identified, the software can assist in root cause analysis and provide fault diagnostics to help the user understand the reason and significance of the problem.
Improving reliability, performance and safety are among the top priorities of industrial plants and other asset-intensive organizations, and businesses today are focusing their efforts and resources on controlling costs and maximizing value from investments already made. Avantis Predictive Asset Analytics helps organizations gain the highest return on critical assets by supporting predictive maintenance (PdM) programs. Predictive Asset Analytics give users the ability to quickly transform raw data into actionable insights to prevent equipment failure and make smart decisions that improve operations. Equipment agnostic, Predictive Asset Analytics can be configured to monitor assets regardless of equipment type, vendor, or asset age without the need for manufacturer specific asset information.
Predictive Asset Analytics predictive asset analytics software makes reliability, performance and efficiency goals more achievable by allowing the user to address issues before they become problems that significantly impact operations. With continuous maintenance and reliability improvements, additional benefits can be achieved. Unscheduled downtime can be reduced because personnel receive early warning notifications of developing issues.
Instead of shutting down equipment immediately, the situation can be assessed for more convenient outcomes. Maintenance costs can also be reduced due to better planning; parts can be ordered and shipped without rush and equipment can continue running. Another increasingly important benefit is the capability for knowledge capture and transfer. Predictive Asset Analytics ensures that maintenance decisions and processes are repeatable even when organizations are faced with transitioning workforces.