Dynamic Risk Predictor Suite for Manufacturing Operations
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Solution Overview
Problem
Current risk management systems in manufacturing and processing facilities fail to effectively identify and address hidden process near-misses and lack of preventive maintenance, leading to adverse incidents and catastrophic failures, as they rely on outdated mathematical modeling and periodic inspections rather than real-time data analysis.
Innovation Solution
The Dynamic Risk Predictor Suite (DRPS) utilizes advanced data analysis methods to monitor and prioritize alarms, identify hidden process near-misses, and provide real-time alerts to operators, enabling proactive maintenance and reducing the likelihood of adverse incidents by analyzing long-term operational behavior and classifying risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time data analysis is implemented to identify hidden process near-misses, then reliability of risk management is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary risk management system that sits between the alarm system and operators. This system processes alarm data through multiple analysis modules (frequency analysis, pattern recognition, near-miss identification) to generate risk assessments and alerts, thereby mediating the complexity between raw data and user interpretation.
Solution Approach 2:
The patent replaces traditional mechanical/mathematical modeling approaches with data-driven analytical methods. Instead of using predetermined mathematical models to assess risk, the system uses real-time analysis of alarm data patterns, frequency distributions, and temporal relationships to dynamically identify hidden near-misses and predict potential failures.
2Productivity
If periodic inspections are replaced with real-time monitoring, then productivity is improved, but loss of time for data processing increases
Solution Approach 1:
The patent implements continuous real-time monitoring and analysis of alarm data, replacing periodic inspections with uninterrupted surveillance of process conditions. The system continuously evaluates alarm frequency, patterns, and temporal relationships to identify emerging risks, ensuring that risk detection is an ongoing process rather than a periodic event.
Solution Approach 2:
The patent performs preliminary analysis of alarm data to identify patterns and trends that precede actual failures. By analyzing historical alarm data and detecting subtle changes in alarm frequency or patterns, the system identifies hidden near-misses and potential failures before they manifest as catastrophic events, enabling preventive action.
3Measurement precision
If comprehensive alarm analysis is performed to identify all near-misses, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the comprehensive alarm analysis into distinct functional modules: frequency analysis module, pattern recognition module, temporal relationship analysis module, and risk assessment module. Each module focuses on a specific aspect of alarm data, processing it independently before integrating results, thereby managing complexity through functional decomposition.
Solution Approach 2:
The patent applies partial analysis to alarm data by focusing on specific dimensions (frequency, patterns, temporal relationships) rather than attempting to analyze all possible aspects simultaneously. The system selectively analyzes alarm data based on pre-defined criteria and thresholds, performing comprehensive analysis only where needed to identify hidden near-misses.
Data Source
AI summary
Provided are methodologies to properly assess and manage operational risks at operations sites, e.g., a manufacturing, production or processing facility, such as a refinery, chemical plant, fluid-catalytic-cracking units, or nuclear energy plant, or a biological or waste management facility, airport or even financial institutions, or at any facility in which operations are often accompanied by risk associated with many high-probability, low-consequence events, often resulting in near-misses. In some operations, processes are monitored by alarms, but the invention operates on either process data or alarm data. The methods are based upon measurement of one or more variables, and/or utilization and management of the concept of “hidden process near-miss(es)” to identify a change or escalation, if any, in probability of occurrence of an adverse incident. The methodologies combine a plurality of subsets (also useful independently) of dynamically calculated leading risk indicators for dynamic risk management.