Process Control Trend Analysis for Time-Series Monitoring Load
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Solution Overview
Problem
In process control systems, operators and maintenance personnel face a heavy load in interpreting and predicting change trends in numerous time-series data points, which can lead to incomplete monitoring and increased complexity due to the need for extensive knowledge about plant processes and environments, especially as intelligent field devices generate more data.
Innovation Solution
A system that includes field devices, a control apparatus, and a change trend calculation device which calculates slopes of linear functions to analyze time-series data, minimizing an objective function weighted by elapsed time, and a notification selection device that prioritizes channels based on change trends and patterns stored in a judgment table to reduce the load on users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If operators manually interpret and predict change trends in time-series data, then monitoring accuracy can be maintained with existing tools, but operator workload increases and comprehensive monitoring becomes difficult
Solution Approach 1:
The system enables self-service by automatically calculating change trends through slope computation of linear functions fitted to time-series data. The trend calculation device autonomously processes the data without requiring manual operator interpretation, thereby maintaining monitoring accuracy while significantly reducing operator workload.
Solution Approach 2:
The patent replaces the mechanical human interpretation process with an automated computational system. Instead of operators manually analyzing time-series data, a trend calculation device computes slopes of linear functions to determine change trends, substituting human cognitive effort with automated mathematical processing.
2Measurement precision
If intelligent field devices are deployed to generate more data, then measurement capability improves, but data complexity and monitoring difficulty increase
Solution Approach 1:
The system extracts only the essential feature from complex time-series data by calculating the slope of the linear function that best fits the data points. This extraction process transforms complex multi-dimensional data into a single meaningful parameter (the slope representing change trend), thereby simplifying data processing while preserving measurement precision.
Solution Approach 2:
The patent transforms the original time-series data parameters into a derived parameter (slope of linear function). By changing the parameter representation from raw data points to a calculated slope value, the system simplifies the data structure while maintaining the essential information about change trends.
3Measurement precision
If operators need extensive knowledge about plant processes to interpret data, then interpretation accuracy improves, but the system becomes harder to operate and requires more trained personnel
Solution Approach 1:
The system performs self-service by automatically calculating change trends without requiring operator knowledge of plant processes. The trend calculation device independently processes time-series data and generates meaningful trends, eliminating the need for operators to possess extensive domain knowledge while maintaining interpretation accuracy.
Data Source
AI summary
A process control system includes: one or a plurality of field devices configured to be placed in a plant; and a control apparatus configured to perform at least one of input and output on the field device to control the plant; and a change trend calculation device configured to calculate a change trend in time-series data including an observed value at each point in time of the field device.