Valve Assembly Trend Graph Real-Time Data Display
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Solution Overview
Problem
Conventional tools for device diagnostics in process control systems struggle to effectively display real-time data to users, often requiring multiple interactions and relying on antiquated approaches that limit access to granular data, especially when using web-based interfaces, leading to static displays and reduced functionality.
Innovation Solution
A system and method utilizing a timing structure with FIFO data queues and timers to coordinate data transfer across multiple layers, ensuring synchronized data sampling, processing, and exchange rates, allowing for dynamic and real-time data display on web-based user interfaces, including trend graphs that reflect the operation of valve assemblies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional tools are used to display device diagnostics data, then data can be accessed and displayed, but the interface requires multiple interactions and provides static displays that do not effectively show real-time data
Solution Approach 1:
The system segments data access into multiple hierarchical levels (facility level, process level, device level) allowing users to drill down from general to specific data views. This segmentation enables efficient navigation through the data structure without requiring multiple separate interactions, as each level provides contextual information and direct access paths to detailed device-level data.
Solution Approach 2:
The system adds a temporal dimension to data display by implementing real-time data streaming and dynamic updates. Instead of static displays that require refreshes or new interactions, the interface continuously updates with new data points, creating a time-based progression that allows users to observe trends and changes without additional interactions. This transforms the display from a static snapshot to a dynamic, evolving view of device status.
2Loss of information
If multiple data layers are implemented for comprehensive diagnostics, then data completeness is improved, but system complexity increases
Solution Approach 1:
The system divides diagnostic data into distinct hierarchical layers: facility-level aggregate data, process-level intermediate data, and device-level detailed data. Each layer processes and filters data appropriate to its level, reducing the complexity burden on any single component. The segmentation allows each layer to focus on specific data types and processing requirements, making the overall complex system manageable through modular organization.
Solution Approach 2:
The system introduces intermediate data layers that act as mediators between raw device data and final user displays. These intermediate layers aggregate, filter, and pre-process data before presenting it to users or passing it to lower layers for detailed analysis. This intermediary approach simplifies the data flow by creating buffer zones that manage complexity, allowing comprehensive data collection without overwhelming any single processing component.
3Measurement precision
If real-time data synchronization is implemented across multiple layers, then data accuracy is improved, but processing overhead increases
Solution Approach 1:
The system applies different data synchronization frequencies and update rates to different hierarchical layers based on their specific requirements. Device-level data that requires high precision for diagnostic purposes receives more frequent updates and higher synchronization priority, while facility-level aggregate data uses lower update rates. This local quality approach ensures accuracy where needed while reducing processing overhead in areas where high-frequency updates are less critical.
Solution Approach 2:
The system implements periodic data collection and synchronization cycles at each hierarchical level, with varying periods optimized for each layer's needs. Instead of continuous real-time synchronization across all layers, the system uses timed intervals that balance data freshness with processing efficiency. This periodic action allows the system to maintain accurate data representation while managing processing overhead through rhythmic, predictable update patterns.
Data Source
AI summary
A system and a method to display diagnostic and operating data on a trend graph via a web browser so that the data appears to trend in real-time. In one embodiment, the system includes a server with an architecture having multiple layers. The layers include a pair of first in, first out (FIFO) data queues including a first data queue and a second data queue. The embodiments utilize a timing structure with a plurality of timers including a first timer to regulate calls from the first data queue to a data source (e.g., a valve assembly or controller on a distributed control system), a second timer to regulate calls from the second data queue to the first data queue, and a third timer to regulate calls from the web browser to the second data queue for data to display on trend graph.