Operator Action Detection for Skill Gaps in Process Control
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Solution Overview
Problem
Industrial process control and automation systems lack the ability to detect inadvertent operator actions, leading to sub-optimal plant operations and potential accidents due to undetected skill and knowledge gaps among personnel.
Innovation Solution
An automated system that analyzes configuration, event, and alarm data from distributed control systems to identify operator changes in process speed, direction, and selection among redundant systems, calculating the time duration of these actions to determine if they are inadvertent and impacting plant performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If operators manually control process variables without automated detection, then operational flexibility is maintained, but inadvertent errors and skill gaps go undetected leading to sub-optimal plant performance
Solution Approach 1:
An automated analysis system acts as an intermediary between operators and the DCS, analyzing operator actions indirectly through recorded data rather than direct intervention. This mediator detects inadvertent changes by comparing operator actions against expected patterns, providing error detection without adding complex real-time control mechanisms to the existing system.
Solution Approach 2:
The patent replaces manual monitoring and detection mechanisms with an automated computational analysis system. Instead of relying on human operators or complex mechanical monitoring devices, the system uses software-based analysis of DCS data to detect inadvertent operator actions, reducing mechanical complexity while improving detection reliability.
2Measurement precision
If the system monitors all operator actions in real-time, then detection accuracy improves, but processing time and system resource consumption increase
Solution Approach 1:
The system performs preliminary analysis by establishing baseline patterns of normal operator behavior and expected responses to alarm conditions before actual incidents occur. By pre-defining what constitutes inadvertent changes through pattern recognition and historical analysis, the system can quickly compare actual operator actions against these pre-established criteria, reducing real-time processing requirements while maintaining high detection precision.
3Productivity
If automated detection systems are implemented, then plant performance improves through error prevention, but implementation cost and system complexity increase
Solution Approach 1:
The automated detection system serves itself by utilizing existing DCS data infrastructure and operational patterns already present in the plant. Rather than requiring separate sensors, actuators, or specialized hardware, the system analyzes data that is already being collected and stored by the existing control system, making the implementation cost-effective while avoiding additional hardware complexity.
Data Source
AI summary
A method, electronic device and system are provided for collecting information associated with operational changes made by plant operators. Episodes of operational changes are identified that include a triggering event and the operational changes performed are reviewed and compared to standard operating data or historical data. The operational changes that differ from such standard operating data or historical data are classified as inadvertent operations based upon a set of pre-determined characteristics. Training may follow to avoid recurrence of such inadvertent operations in the future.