Historian Query Summarization Using Dynamic Tag-Based Cycles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current industrial process data systems struggle to provide dynamic summarization of data in non-continuous processes, where traditional methods rely on fixed cycles and fail to adapt to changing contexts, making it difficult to correlate summary data with other MES data or equipment states.
Innovation Solution
A computer-implemented method and system that includes a historian with a GUI, capable of processing dynamic queries to retrieve time-series data based on tag-associated cycles, allowing for dynamic cycle computation and summarization of data across multiple states, such as 'Running', 'Shutdown', and 'Overheated', with the ability to slice data by other tag values, enabling flexible summarization and correlation with other MES data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed cycle summarization is used, then data retrieval is simple and consistent, but the system cannot adapt to changing contexts in non-continuous processes
Solution Approach 1:
The system transitions from fixed cycle summarization to dynamic cycle summarization where the cycle duration is determined by tag state changes. The historian now computes cycles dynamically based on when tags enter or exit specific states (Running, Shutdown, Jammed, Overheated), allowing the summarization to adapt to actual process conditions rather than following a rigid time schedule.
Solution Approach 2:
The system changes the parameter used for cycle determination from a fixed time value to a state-based parameter. Instead of summarizing at regular time intervals, the system now uses tag state transitions (entering/exiting defined states) as the trigger for cycle boundaries, fundamentally changing how summarization cycles are parameterized.
2Loss of information
If dynamic cycle computation is implemented, then data summarization adapts to process states, but query processing becomes more complex
Solution Approach 1:
The system performs preliminary actions by pre-defining the states (Running, Shutdown, Jammed, Overheated) and their associated tag conditions before query execution. When a query is made, the system can quickly retrieve summarization data aligned with these pre-defined state boundaries without performing complex real-time analysis, as the cycle structure is already established based on tag state transitions.
Solution Approach 2:
The system introduces an intermediary layer of state definitions that mediate between raw tag data and summarization queries. Instead of directly querying complex dynamic cycles, users can query summarization data aligned with state boundaries, and the system uses the state definitions as an intermediary to compute the appropriate cycles, simplifying the query interface while maintaining dynamic adaptability.
3Loss of information
If data is sliced by multiple tags, then correlation with MES data improves, but data retrieval complexity increases
Solution Approach 1:
The system segments the summarization query by allowing users to specify multiple slice tags (e.g., batch number, equipment state, operator). Each slice tag divides the data into distinct segments that can be independently analyzed. This segmentation approach enables multi-dimensional correlation with MES data while maintaining a structured retrieval process that breaks down complex queries into manageable segments.
Solution Approach 2:
The slicing mechanism is designed to be universal and applicable to any tag in the system. Rather than requiring different retrieval methods for different types of correlations, the same slice-by-tag approach can be used to correlate with any MES data or equipment state, providing a unified multi-functional interface for diverse correlation needs.
Data Source
AI summary
Some embodiments include a system and method of providing a historian that receives operational state data from a device of an industrial process of a network, where the operational state data is stored as time-series data, and processing at least one data query from at least one user coupled to the network. Based at least in part on the at least one data query and at least one dynamic cycle, at least some of the time-series data is retrieved, where the dynamic cycle is based on at least one tag associated with the time-series data. Further, based at least in part on at least some of the time-series data, at least one graphical representation of one or more tags is displayed, where each tag of the one or more tags represents an attribute of at least one process associated with the at least one device.