OpreX Asset Health

What is OpreX Asset Health Insights?

OpreX Asset Health Insights is Yokogawa’s cloud-based asset performance monitoring and OT data platform for industrial operations. It collects, refines, and aggregates operational data so plant, maintenance, and reliability teams can monitor asset health, investigate anomalies, track performance, and apply predictive analytics across distributed equipment and systems.

1. Asset monitoring: OpreX AHI combines real-time and historical data in asset-centric dashboards with KPIs, benchmarking, alerts, equipment details, and configurable failure criteria. Its self-service environment allows users to build asset hierarchies and visualizations, while the core platform is organized into Data Management, Data Insight, and Data Analytic modules.
2. Predictive analytics: the Data Analytic environment supports machine-learning model training, testing, and deployment for use cases including regression and anomaly detection. Real-time anomaly analytics can generate warning or critical alerts, while an integrated AI Assistant helps users explore anomaly data and interpret patterns. Yokogawa positions these capabilities for predictive maintenance and intervention before equipment disruption or failure.
3. Industrial connectivity: preloaded drivers can connect field devices, sensors, PLCs, DCS, SCADA systems, historians, and ERP systems. New and existing sensors can contribute measurements such as pressure, flow, temperature, and vibration, with incoming data cleaned and configured in Yokogawa Cloud before monitoring.
4. Packages and deployment: additional packages include APR-APM, PUMP-APM, Asset Digital Twin, Asset Energy Management, Smart Asset Viewer, and Reliability and Process Expert for automated root-cause analysis. OpreX AHI is quote-based rather than sold through published standard plans, and Yokogawa offers demos and consultation for deployment planning.

OpreX Asset Health Insights is most relevant to asset-intensive industrial operations that need to consolidate heterogeneous OT data and move from centralized condition monitoring into anomaly detection, predictive maintenance, and asset-level operational analysis.