Lifecycle Risk Traceability for Failure Mode Causality
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Solution Overview
Problem
Conventional risk management technologies fail to accurately reflect actual risks by disregarding failure mode relationships and misidentify risk profiles, and are cumbersome and costly for tracking changes across different versions and iterations in manufacturing and product development environments.
Innovation Solution
A risk based lifecycle management system that utilizes a machine learning model to determine failure modes and their causality, enabling effective tracking and mitigation of multi-variant causes of adverse events through a tracing matrix and knowledge retention data store, facilitating continuous improvement and regulatory compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional FMEA is used to identify failure modes, then risk priority numbers can be calculated, but the actual risk relationships between failure modes are disregarded leading to inaccurate risk assessment
Solution Approach 1:
The patent segments the risk assessment process into distinct components: failure mode identification, relationship mapping, and risk calculation. By separating the analysis of individual failure modes from the analysis of their relationships, the system can accurately capture both isolated failures and interconnected failure chains, resolving the contradiction between comprehensive risk assessment and information loss.
Solution Approach 2:
The patent introduces an intermediary layer (the relationship mapping component) that connects failure modes and captures their interrelationships. This intermediary structure preserves the relationship information that would otherwise be lost in traditional FMEA, enabling accurate risk assessment while maintaining the established FMEA framework.
2Reliability
If traditional tracking methods are used to monitor changes across FMEA versions, then regulatory requirements can be met, but the process becomes cumbersome and costly
Solution Approach 1:
The patent creates a digital copy or model of the FMEA tracking system that automatically monitors changes across versions. Instead of manual tracking processes, the system uses computational models to replicate and analyze version differences, maintaining regulatory compliance while eliminating the cumbersome manual procedures.
Solution Approach 2:
The patent transforms the tracking system from a static, manual process to a dynamic, automated system by changing key parameters: from manual review to algorithmic analysis, from version-by-version examination to continuous monitoring. This parameter change reduces complexity while maintaining compliance reliability.
3Productivity
If RPN numbers are used to prioritize failures, then a quantitative risk metric is available, but RPNs of the same value may not reflect the same actual risk level
Solution Approach 1:
The patent combines multiple risk assessment factors into a composite risk evaluation framework. Instead of relying solely on the traditional RPN metric, the system integrates relationship-based risk factors with conventional RPN values, creating a composite assessment that provides both quantitative efficiency and precise risk differentiation.
Data Source
AI summary
Risk based lifecycle management systems are presented herein. A system determines a process ontology of a process including process steps of the process and objects including attributes corresponding to performances of the process steps; associates a process step with respective objects using a tracing matrix; detects an event corresponding to a performance of the process step; and associates the event with the process step and the respective objects using the tracing matrix. In response to determining, utilizing a machine learning model, that the event corresponds to a defined risk profile including defined failure modes, the system selects a group of defined failure modes as candidates of causality of the event representing potential multi-variant causes of the event, and sends the candidates of causality of the event directed to a user identity to facilitate mitigation of effects of the potential multi-variant causes of the event on the process.