Industrial Controller Workflow Triggers for Performance Degradation
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Solution Overview
Problem
Model-based industrial process controllers experience performance decline over time due to factors like inaccurate models, misconfiguration, and operator actions, making it difficult to quantify and resolve the loss of benefits, especially in multi-vendor and multi-version environments, and lacking seamless mechanisms for vendor-neutral analysis.
Innovation Solution
A framework that identifies, visualizes, and triggers workflows to reclaim lost benefits by analyzing data from model-based controllers, providing scalable and elastic solutions through analytic containers, and offering contextual visualizations and auto-suggested actions to improve controller performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If model-based controllers are used to control industrial processes, then control performance and efficiency are improved, but performance degrades over time due to model inaccuracy, misconfiguration, and operator actions
Solution Approach 1:
The system performs preliminary analysis of controller data to identify performance degradation issues before they significantly impact operation. By proactively detecting model inaccuracy, misconfiguration, and operator actions that lead to performance decline, the system can trigger workflows to correct issues before they cause major problems, thus maintaining both productivity and reliability over time
Solution Approach 2:
The system continuously monitors controller data and provides feedback about performance degradation. By analyzing historical and real-time data, the system identifies when controllers are underperforming and triggers corrective workflows, creating a closed-loop feedback mechanism that maintains controller reliability while preserving productivity gains
2Measurement precision
If comprehensive data analysis is performed to identify controller issues, then accuracy of issue identification is improved, but computational complexity and processing time increase
Solution Approach 1:
The system segments the complex task of controller performance analysis into distinct analytical components. By breaking down the analysis into specific issue types (model inaccuracy, misconfiguration, operator actions), the system can apply targeted analysis methods to each segment, improving identification accuracy while managing overall system complexity through modular processing
Solution Approach 2:
The system creates a universal analysis framework that handles multiple types of controller issues through a common platform. By developing multi-functional analysis capabilities that can detect various problem types using standardized methods, the system achieves high identification accuracy without proportionally increasing complexity, as the same infrastructure serves multiple diagnostic purposes
3Adaptability or versatility
If vendor-neutral analysis mechanisms are implemented, then adaptability to different controller systems is improved, but system complexity and integration requirements increase
Solution Approach 1:
The system implements a universal analysis platform that can work with multiple vendor controllers through standardized data interfaces. By creating multi-functional capabilities that handle different controller types and protocols through a common architecture, the system achieves vendor-neutral adaptability while managing integration complexity through standardized approaches rather than custom solutions for each vendor
Solution Approach 2:
The system introduces an intermediary analysis layer between diverse controller systems and the performance optimization workflows. This mediator layer standardizes data from different vendors and translates it into a common format for analysis, enabling vendor-neutral operation while isolating integration complexity to the intermediary layer rather than propagating it throughout the entire system
Data Source
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AI summary
A method includes obtaining (504) data associated with operation of an industrial process controller (106) and identifying (506) impacts of operational problems of the industrial process controller. The method also includes generating (510) a graphical display (400) for a user, where the graphical display presents one or more recommended actions to reduce or eliminate at least one of the impacts of at least one of the operational problems. The method further includes triggering (512) at least one of the one or more recommended actions based on input from the user. The method could also include executing one or more analytic algorithms (314) to process the obtained data and identify the operational problems of the industrial process controller. Each of the one or more analytic algorithms could be instantiated as a container (318), and multiple containers could be instantiated and executed as needed. Results of executing the one or more analytic algorithms could be transformed (608) into a standard format.