PS-Trend is a powerful data analytics solution designed to transform complex raw data sets into actionable insights. With an intuitive interface and powerful toolset, PS-Trend enables users from managers to engineers to easily discover, visualize, and forecast important trends, helping to make strategic decisions and optimize operations.
Integrating AI/ML models for predictive analytics, combined with Anomaly Detection, automatically alerts when there are deviations or potential problems in the system.
Multidimensional data visualization: Provides a diverse chart library (Line, Bar, Pie, Heatmap...) allowing customization and building dynamic analysis dashboards.
Provides in-depth filters and State Analysis tools to calculate uptime, downtime, or other operating states.
| Category | Specification / Description |
|---|---|
| Platform Type | Industrial IoT analytics and time-series visualization platform |
| Use Cases | Advanced analytics for telemetry, predictive maintenance, energy and anomaly analysis |
| Data Analysis | Real-time time series analytics, anomaly detection, forecasting, dashboard widgets |
| Data Sources | Direct integration with PS-Board, supports external API/barriers |
| Database Support | PostgreSQL or TimescaleDB for large-scale time-series data |
| Query Engine | Custom query builder, dynamic filtering, group by device/type, multiple aggregations |
| Visualization | Interactive dashboard, charts, maps, reports (PDF/Excel export) |
| AI / ML Integration | Supports anomaly detection, OEE calculations, predictive analytics |
| Reporting & Automation | Automated scheduled reports, customizable by device/group/process |
| Security | Role-based access control, secure API and data storage |
| API Access | REST API, WebSocket for real-time dashboard events |
| Minimum Hardware | RAM ≥ 4GB (recommended 8GB+), CPU ≥ 2-core, Linux or Docker host |
| Deployment Model | Standalone or embedded with PS-Board, on-premise or cloud |
| Supported OS / Platform | Linux, Docker Compose, Virtual Machine support |
| Sample Use Cases | Equipment OEE analysis, predictive maintenance, smart building analytics |
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