Component Prognostic Analytics for Preemptive Replacement Scoping
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Solution Overview
Problem
Current reliability monitoring systems are inadequate for providing predictive and preemptive work-scoping information at the individual component level, particularly in military applications where operational readiness requires extending the lifetime of assets while minimizing downtime, as they rely on fleet-wide statistics that may not accurately reflect the condition of individual parts.
Innovation Solution
A system and method that utilize individual engine data to perform comparative statistical analyses and apply relative severity factors to shift the Weibull distribution, allowing for predictive and preemptive identification of part replacement candidates, thereby extending the time-on-wing of specific assets like jet engines by providing personalized prognostic analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fleet-wide statistical models are used for reliability monitoring, then data requirements are reduced and system complexity is lowered, but measurement precision and reliability prediction accuracy for individual components deteriorate
Solution Approach 1:
The patent segments the reliability monitoring system into two distinct analytical pathways: fleet-wide statistical analysis for general trends and individual component-level analysis for precise predictions. This segmentation allows the system to maintain low complexity for routine monitoring while providing high precision when individual component analysis is required, resolving the contradiction between system simplicity and prediction accuracy.
Solution Approach 2:
The patent applies local quality by transitioning from uniform fleet-wide statistical models to customized individual component analysis. Each component receives tailored prognostic analytics based on its specific operational data, usage patterns, and failure modes, thereby achieving high measurement precision for individual parts without requiring complete redesign of the entire monitoring system.
2Productivity
If fleet-based performance data is used for predictive analytics, then data collection is simplified and processing requirements are reduced, but the ability to provide accurate work-scoping information for individual assets deteriorates
Solution Approach 1:
The patent implements a dynamic analytical framework that automatically adjusts the level of analysis based on operational needs. The system can operate in fleet-wide mode for routine monitoring (maintaining high productivity) and dynamically switch to individual component mode when precise work-scoping information is required (achieving high reliability). This dynamic adaptation resolves the contradiction between processing efficiency and analytical accuracy.
3Reliability
If parts are replaced early based on fleet statistics to ensure operational readiness, then reliability is improved, but asset lifetime is reduced and resource waste increases
Solution Approach 1:
The patent applies preliminary action by providing advance prognostic analytics that predict individual component failure before it occurs. This allows maintenance to be scheduled optimally - early enough to ensure operational readiness but not so early as to waste resources. The system performs the necessary maintenance action in advance based on actual component condition rather than conservative fleet-wide estimates, thereby extending asset lifetime while maintaining reliability.
Data Source
AI summary
There are provided systems and methods for prognostic analytics of an asset. For example, there is provided a system for monitoring a reliability of a component of an asset. The system includes a processor and a memory comprising instructions that, when executed by the processor, cause it to perform certain operations. These operations may include receiving input data, which can include performance data relating to the component, configuration data relating to the component, and statistical data relating to a plurality of assets. The operations can further include providing a pre-emptive recommendation for the component based on the input data.