Dynamic Risk Pattern Matching for Hidden Near-Miss Detection
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Solution Overview
Problem
Existing risk management systems in manufacturing facilities fail to identify hidden near-misses and subtle trends, leading to unforeseen operational failures and costly shutdowns, due to limitations in traditional fault tree analysis and the silencing of alarms, resulting in reactive rather than proactive safety measures.
Innovation Solution
A dynamic risk analyzer (DRA) system incorporating a dynamic risk pattern match (DRPM) method that analyzes real-time and historical process data to identify hidden near-misses and predict future risk levels, using a polynomial pattern recognition module to determine operational and near-miss risks, displayed through a graphical interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fault tree analysis and alarm systems are used, then the system structure is simple and ease of operation is maintained, but hidden near-misses and subtle trends cannot be identified, leading to reactive safety measures
Solution Approach 1:
The system performs preliminary analysis by continuously monitoring process data and identifying patterns that precede adverse incidents. The polynomial pattern recognition module analyzes historical and real-time data to detect subtle trends and hidden near-misses before they escalate into full-scale failures, enabling proactive safety interventions
Solution Approach 2:
The patent introduces a polynomial pattern recognition module as an intermediary between traditional alarm systems and safety decision-making. This module processes raw process data through mathematical polynomial functions to extract meaningful patterns and generate risk predictions, bridging the gap between simple monitoring and complex safety analysis
2Measurement precision
If traditional risk management systems are used, then device complexity is low, but near-miss identification capability is insufficient, resulting in unforeseen operational failures
Solution Approach 1:
The system replaces traditional mechanical alarm threshold comparisons with polynomial pattern recognition algorithms. Instead of simple binary alarm states, the system uses mathematical polynomial functions to continuously assess risk levels and identify subtle patterns in process data, significantly improving detection precision
Solution Approach 2:
The patent transforms static alarm thresholds into dynamic polynomial risk indicators. By changing from fixed parameter comparisons to dynamic polynomial function evaluations, the system can adapt to varying operating conditions and detect near-misses that would be invisible to traditional fixed-threshold systems
3Ease of operation
If alarms are silenced to reduce false positives, then ease of operation improves, but critical near-misses are missed, leading to adverse incidents
Solution Approach 1:
The system implements continuous feedback through polynomial pattern recognition that dynamically adjusts risk assessments based on evolving process conditions. Instead of static alarms that require silencing, the system provides ongoing probabilistic risk predictions that maintain operator awareness without causing alarm fatigue
Solution Approach 2:
The patent transitions from static alarm systems to dynamic polynomial risk indicators. The risk predictions continuously evolve based on new data inputs and changing process conditions, providing operators with adaptive information that remains relevant without requiring frequent alarm silencing
4Measurement precision
If comprehensive real-time and historical data analysis is performed, then risk prediction accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary polynomial pattern recognition on historical data to establish baseline risk models before real-time analysis. By pre-processing historical data and identifying recurring patterns in advance, the system reduces the computational burden during real-time operation while maintaining high prediction accuracy
Solution Approach 2:
The patent implements periodic polynomial analysis at strategically selected time intervals rather than continuous analysis. The system evaluates risk predictions at intervals sufficient to capture meaningful trends while avoiding redundant computations, balancing accuracy with processing efficiency
Data Source
AI summary
The invention provides a dynamic risk analyzer (DRA) that periodically assesses real-time or historic process data, or both, associated with an operations site, such as a manufacturing, production, or processing facility, including a plant's operations, and identifies hidden near-misses of such operation, when in real time the process data appears otherwise normal. DRA assesses the process data in a manner that enables operating personnel including management at a facility to have a comprehensive understanding of the risk status and changes in both alarm and non-alarm based process variables. The hidden process near-miss data may be analyzed alone or in combination with other process data and/or data resulting from prior near-miss situations to permit strategic action to be taken to reduce or avert the occurrence of adverse incidents or catastrophic failure of a facility operation.