Asset Replacement Timing Using Hazard Curves and Effective Age
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Solution Overview
Problem
Current systems for managing fleets of assets, such as those in electric power systems, struggle with determining the optimal time for asset replacement due to limitations in predicting failure rates and incorporating business and operational constraints.
Innovation Solution
A computer-implemented method and system that utilize health information to determine a hazard curve for assets, which indicates their predicted failure rate, and an effective age, to inform an optimization model for determining the best time for asset replacement, thereby optimizing capital allocation and operational expenses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional asset replacement methods are used, then operational simplicity is maintained, but reliability and cost optimization deteriorate due to inability to accurately predict failure rates
Solution Approach 1:
The system performs preliminary analysis by determining hazard curves and effective ages before making replacement decisions. Health information is collected and processed in advance to predict failure rates, allowing proactive planning of asset replacement rather than reactive responses to failures.
Solution Approach 2:
The optimization model serves as an intermediary between raw health data and replacement decisions. It integrates hazard curves, effective ages, and business constraints through mathematical optimization to produce data-driven replacement recommendations, bridging the gap between complex data and actionable insights.
2Productivity
If data-driven optimization models are implemented, then capital and operational expense optimization is achieved, but system complexity and implementation difficulty increase
Solution Approach 1:
The optimization model is designed to be universally applicable across different asset types and organizational contexts. It incorporates multiple objective functions that can handle various business constraints simultaneously, making it adaptable to diverse scenarios while maintaining a unified analytical framework.
Solution Approach 2:
The system transforms raw health parameters into meaningful metrics like hazard curves and effective ages. By changing the parameter representation and incorporating business-specific constraints, the model adapts to different optimization goals while maintaining mathematical rigor and computational efficiency.
3Measurement precision
If comprehensive health information analysis is performed, then prediction accuracy improves, but information processing time and computational requirements increase
Solution Approach 1:
The system extracts only the most critical features from comprehensive health information to construct hazard curves and effective ages. By focusing on key degradation indicators rather than processing all available data, it achieves high prediction accuracy while reducing computational burden and analysis time.
Solution Approach 2:
The optimization model uses partial information (hazard curves and effective ages) rather than requiring complete asset histories. This selective approach provides sufficiently accurate predictions for replacement decision-making without the computational overhead of analyzing every available data point.
Data Source
AI summary
In some implementations, an optimization system may obtain health information identifying different measures of health of an asset. The health information identifies end of life information regarding an end of life curve of the asset and an effective age of the asset. The optimization system may determine, based on the health information, a hazard curve for the asset. The hazard curve indicates a predicted failure rate of the asset over a period of time. The optimization system may provide the hazard curve and the effective age of the asset as inputs to an optimization model. The optimization system may use the optimization model to determine a particular time for replacing the asset, wherein the particular time is determined based on the hazard curve and the effective age. The optimization system may cause the asset to be replaced at the particular time.