Risk Analysis Parameter Updating Using Incident and Near-Event Data
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Solution Overview
Problem
Existing risk analysis methods, such as FMEA, rely on subjective human estimation of failure occurrence and detectability parameters, leading to inaccuracies and missed opportunities for preventive measures due to the exclusion of near events and lack of independent validation.
Innovation Solution
A computer-based method that retrieves incident reports to classify events and near events, adjusting failure occurrence and detectability parameters based on actual system behavior, allowing for iterative and accurate risk analysis updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If risk analysis parameters are estimated by human judgement before actual operation, then the analysis can be performed prospectively, but the accuracy of the parameters does not correspond to reality
Solution Approach 1:
The patent implements feedback by comparing prospective FMEA parameters with actual retrospective incident data. The system automatically updates occurrence and detectability parameters based on the difference between expected and actual failure modes, creating a closed-loop system that continuously improves parameter accuracy through real-world validation.
Solution Approach 2:
The system performs self-service by automatically retrieving incident data, comparing it with FMEA predictions, and updating parameters without requiring manual intervention. The computer system autonomously validates and corrects its own risk analysis parameters using actual operational data.
2Reliability
If only events are taken into account for updating FMEA parameters, then the validation process is simplified, but many opportunities to prevent accidents are lost
Solution Approach 1:
The patent segments incident data into distinct categories: events that reached the patient and near-events that did not. This segmentation allows the system to separately analyze and update parameters for different types of failures, providing more comprehensive risk analysis while maintaining structured data management.
Solution Approach 2:
The patent adds a new dimension to traditional FMEA by incorporating near-events alongside conventional events. This dimensional expansion transforms the analysis from a single-dimensional event-based approach to a multi-dimensional framework that captures both realized and potential failures.
3Reliability
If prospective and retrospective analysis are performed separately, then each analysis can be conducted independently, but the parameters are not corrected and accuracy is not improved
Solution Approach 1:
The patent merges prospective FMEA analysis with retrospective incident analysis by integrating both data sources into a unified parameter validation system. The system combines predicted failure modes with actual incident data to jointly update occurrence and detectability parameters, creating a hybrid approach that leverages both prospective planning and retrospective learning.
4Measurement precision
If FMEA parameters are validated against incident data, then parameter accuracy can be improved, but the validation process becomes more complex
Solution Approach 1:
The patent replaces manual validation processes with automated computer-based systems. The computer automatically retrieves incident data, compares it with FMEA predictions, calculates parameter updates, and applies corrections without human intervention, thereby maintaining high measurement precision while reducing the operational complexity of the validation process.
Data Source
AI summary
A computer-based method for updating risk analysis parameters of a proactive risk analysis of a technical system, such as of an FMEA analysis for example. The method uses incident report data obtained during posterior operation of the technical system, compares these incident report data with data of the proactive risk analysis, and updates the said risk analysis parameters in function of said comparison.