Edge Interlock Recommendations Using Online Process Stream Analysis
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Solution Overview
Problem
Current safety interlock systems operate reactively, leading to workflow disruptions, potential losses, and missed opportunities for continuous learning and improvement due to their reliance on manual efforts and limited proactive management of industrial processes.
Innovation Solution
A safety interlock recommendation system that utilizes an edge device with an operational technology edge application unit and a stream analysis unit, incorporating online machine learning to provide real-time and long-term recommendations based on process and operational technology stream data, enabling proactive management and continuous learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reactive safety interlock management is used, then system simplicity is maintained, but productivity is reduced due to workflow disruptions and unplanned stops
Solution Approach 1:
The system performs preliminary analysis of process data to predict potential safety interlock events before they occur. By analyzing historical and real-time process data, the system identifies patterns and trends that indicate upcoming safety issues, allowing operators to take preventive actions before actual interlocks trigger, thus avoiding workflow disruptions and maintaining productivity
Solution Approach 2:
The system implements continuous feedback loops by monitoring process data in real-time and comparing it against learned patterns. The machine learning models continuously learn from new data and adjust their predictions, providing ongoing feedback to operators about potential safety issues. This creates a closed-loop system that continuously improves while maintaining productivity
2Reliability
If manual analysis of all alternative factors is performed, then comprehensive safety coverage is achieved, but device complexity increases significantly
Solution Approach 1:
The system replaces manual mechanical analysis methods with automated machine learning models. Instead of relying on human operators to manually analyze process data and identify safety patterns, the system uses algorithms that automatically learn from historical data and real-time measurements, providing comprehensive safety coverage without increasing operational complexity
Solution Approach 2:
The machine learning models perform self-learning and self-improvement by automatically analyzing process data and updating their internal parameters. The system autonomously identifies patterns, correlates variables, and refines its predictions without requiring manual reconfiguration or expert intervention, thereby achieving comprehensive safety coverage while keeping the system manageable
3Loss of information
If expert knowledge is not documented, then system operation is simple, but loss of information occurs when experts leave or retire
Solution Approach 1:
The system creates digital copies of expert knowledge by training machine learning models on historical process data and expert decisions. The models learn to replicate the decision-making patterns and safety judgment of experienced operators, effectively capturing and preserving their knowledge in a reusable digital format that doesn't depend on individual experts being present
Solution Approach 2:
The system transforms qualitative expert knowledge into quantitative parameters that can be stored and processed digitally. By converting expert judgment into measurable patterns in the data, the system preserves knowledge in a form that can be automatically applied and consistently reused, preventing knowledge loss while maintaining operational simplicity
4Reliability
If proactive safety management is implemented, then interlock events are prevented, but system complexity increases due to additional monitoring requirements
Solution Approach 1:
The system achieves multiple functions using a single integrated platform. The same machine learning infrastructure that performs predictive analytics also provides real-time monitoring, historical analysis, and pattern recognition. This multi-functional approach enables proactive safety management without requiring separate complex systems for each function, thereby improving safety performance while limiting complexity growth
Data Source
AI summary
A safety interlock recommendation system includes at least one process data source, an edge device, wherein the process data source is configured for providing IOS device stream data to the edge device; wherein the edge device comprises an operational technology edge application unit, OT edge application unit, and a stream analysis unit; wherein the OT edge application unit is configured for providing operation technology stream data, OT stream data; wherein the stream analysis unit comprises an online machine learning model, being configured for determining online analysis data using the provided process stream data and the provided OT stream data; wherein the OT edge application unit is configured for determining a short-term recommendation using the online analysis data.