AVEVA Predictive Analytics 2025 (version updated in May 2025) is an AI-powered predictive maintenance software solution from AVEVA, designed to enhance asset reliability and minimize unplanned downtime in industrial environments. It leverages advanced pattern recognition, machine learning, and data mining to analyze near-real-time sensor data against historical equipment profiles, providing early warnings of deviations—days, weeks, or months before failure. Targeted at asset-intensive sectors like oil and gas, power generation, chemicals, mining, and manufacturing, the software supports predictive maintenance (PdM) programs by forecasting time-to-failure, diagnosing faults, and recommending prescriptive actions, ultimately reducing maintenance costs and improving operational efficiency.
This release emphasizes scalability for monitoring single assets or thousands across global sites, with seamless integration into the AVEVA PI System for enterprise-wide data handling. It’s deployable on-premises or in the cloud, making it adaptable for mid-sized businesses to large enterprises.
Key Features and Updates in the 2025 Release
The May 2025 update introduces enhancements focused on sensor reliability, data consistency, and user-friendly diagnostics, building on prior versions’ AI capabilities. Key new features include configurable rules for sensor fault detection and improved asset framework integration. Here’s a summary:
| Category | New/Updated Features | Benefits |
|---|---|---|
| Sensor Fault Detection | Configurable analysis rules to identify malfunctioning sensors (e.g., deviations in magnitude) and automatically exclude them from models and calculations. | Addresses ~25% of detected issues related to sensor failures, reducing repair times from weeks to proactive alerts; simplifies fault visualization with trend graphs. |
| Asset Framework Adapter | New AVEVA PI System Asset Framework Real-Time Service for tag identification based on templates or characteristics. | Ensures model consistency across similar assets, minimizing configuration errors in enterprise deployments; enhances scalability for multi-site operations. |
| Trends and Case Management Integration | Unified screen overview combining anomaly lessons learned, user activities, and comments for prescriptive guidance. | Supports labor shortages by providing quick, contextual insights; accelerates decision-making with historical context. |
| Custom Algorithm Support | Integration of user-defined algorithms (e.g., via Python) alongside built-in templates for data cleansing, alerting, fault diagnostics, and forecasting. | Allows customization for specific workflows while leveraging core AI for time-to-failure predictions and remedial actions. |
| Visualization and Reporting | Advanced tools to view raw training data, model results, asset comparisons, and alert impacts. | Reduces time spent searching for issues; enables business intelligence for performance benchmarking. |
These updates make deployment, validation, and interpretation of predictive models more efficient, enabling faster profitability gains.
