AVEVA Predictive Analytics

AVEVA Predictive Analytics is AI-powered predictive maintenance software for industrial organizations that uses real-time and historical asset data to detect abnormal equipment behavior, diagnose developing faults, forecast time to failure, and provide prescriptive actions before failures disrupt operations. The product was formerly known as PRiSM Predictive Asset Analytics.

1. Reliability role: AVEVA builds multivariate models from groups of equipment sensors to learn historically normal operating behavior and identify small deviations as conditions change. This can expose developing problems days, weeks, or months before failure and earlier than conventional single-sensor thresholds or alarms.
2. Diagnostic workflow: anomaly detection, configurable sensor-fault detection, fault diagnostics, advanced alerts, case management, and time-to-failure forecasting help reliability teams determine what is changing, which sensors are contributing, how urgent the problem is, and which prescriptive actions should be considered.
3. Implementation checks: buyers should assess operational-data quality, monitored asset coverage, model templates and configuration, alert and case-management workflows, and integration requirements. AVEVA PI System has native integration with Predictive Analytics, including Asset Framework support and the ability to use PI data when deploying models at scale; AVEVA also supports incorporating custom algorithms alongside its built-in analytics.
4. Commercial considerations: AVEVA does not publish fixed self-service pricing for Predictive Analytics. Deployment requirements can vary by asset count, sites, data architecture, integrations, modeling scope, and support needs, so buyers should obtain commercial terms from AVEVA and evaluate the broader licensing and implementation requirements for their environment.

AVEVA Predictive Analytics is particularly relevant to asset-intensive operators that want earlier fault detection and prescriptive maintenance guidance without requiring reliability engineers to build every predictive model in code.