Industrial Sensor Data Visualization at Elco
Note: This article was written retrospectively, years after the project took place in 2018. While it captures my experiences and challenges from that time, it's enriched with insights and understanding I've gained since then.
TL;DR
- Technologies: Angular, TypeScript, widget-based architecture, configurable dashboards, sensor data modeling
- Role: Frontend developer building a component library and visualization system for industrial sensor data
- Key learning: Creating modular, reusable visualization components that balance flexibility with performance in data-intensive applications
Working with Elco Industry Automation presented a unique challenge: building a sophisticated sensor data visualization system that could integrate seamlessly with their existing product ecosystem. The project required creating something Grafana-like in its capabilities but tailored specifically for industrial automation contexts and designed as an extension rather than a standalone solution.
Building on a sensor data foundation
The project began with a well-defined sensor data model that served as the backbone for everything we built. This wasn't just about displaying numbers on a screen; it was about representing complex industrial processes in ways that operators could understand and act upon instantly. The data model needed to accommodate various sensor types, sampling rates, and aggregation patterns while maintaining real-time performance.
Working with this structured foundation made it clear how critical good data modeling is for visualization systems. Every design decision, from component architecture to rendering optimizations, traced back to how we understood and structured the underlying sensor data.
A component library for industrial visualization
The heart of the project was creating a comprehensive Angular component library specifically designed for industrial data visualization. This wasn't just about charts and graphs; it was about building a visual language for industrial automation. Each component needed to be flexible enough to handle different data types and visualization requirements while maintaining consistency across the entire system.
The widget-based architecture allowed users to compose dashboards from individual visualization components, each configured for specific sensor types or data patterns. A temperature widget might display differently than a pressure sensor widget, but they all shared common interaction patterns and visual consistency. This modularity meant that extending the system with new visualization types became straightforward, following established patterns and interfaces.
Configurability meets usability
One of the most challenging aspects was balancing configurability with usability. Power users needed the ability to create complex, multi-layered dashboards with precise control over every aspect of data display. At the same time, operators on the factory floor needed simple, clear interfaces they could understand at a glance during critical moments.
The solution involved creating multiple configuration layers. Simple wizards guided basic dashboard creation, while advanced modes exposed the full power of the system. Widget configurations could be saved as templates, allowing best practices to be shared across teams. This tiered approach meant the system could serve both the engineer designing monitoring solutions and the operator using them daily.
Frontend-focused challenges
With the project's focus squarely on frontend development, I encountered challenges unique to data-intensive visualization applications. Performance optimization became crucial when displaying real-time updates from hundreds of sensors simultaneously. We implemented intelligent data decimation, viewport-based rendering, and aggressive caching strategies to maintain smooth interactions even with massive datasets.
The Angular framework proved well-suited for this challenge, particularly its change detection strategies and component lifecycle management. By carefully controlling when and how components updated, we could display live sensor data without overwhelming the browser's rendering engine.
Extending existing products
Perhaps the most valuable lesson came from designing this as an extension to an existing product rather than a replacement. This constraint forced us to think carefully about integration points, data contracts, and migration paths. We couldn't simply impose new patterns; we had to work within established conventions while gradually introducing modern visualization capabilities.
This approach taught me the importance of evolutionary architecture in enterprise systems. Revolutionary changes might seem cleaner from a technical perspective, but evolutionary improvements that respect existing investments and workflows often deliver more value.
Impact on industrial operations
The visualization system transformed how Elco's customers interacted with their sensor data. Complex industrial processes became comprehensible through carefully designed visualizations. Anomalies that might have been buried in spreadsheets or logs became immediately apparent through visual patterns. Operators could spot trends, identify issues, and make decisions faster than ever before.
The component library we built became the foundation for multiple products within Elco's portfolio. The investment in creating a robust, flexible visualization framework paid dividends as new use cases emerged and the system evolved to meet them. What started as a single project became a platform for industrial data visualization across the organization.