Cumulative Maintenance Cost Modeling From Asset Distress Prediction
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Solution Overview
Problem
Maintenance and repair facilities lack visibility into estimated costs and material consumption for assets, often being surprised by the conditions of incoming components, leading to inefficiencies in maintenance planning and inventory management.
Innovation Solution
A system utilizing artificial intelligence and physical domain expertise to build cumulative cost and material models, predicting maintenance costs and material demand by analyzing distress levels of assets, incorporating data from operational, environmental, and historical data to generate predictive models for maintenance planning and inventory optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distress models are used to predict asset condition, then maintenance timing can be optimized, but visibility into estimated costs and material consumption remains insufficient
Solution Approach 1:
The patent combines distress prediction models with cost modeling components to create an integrated system. The distress model outputs (distress levels, remaining useful life predictions) are merged with cost parameters (labor rates, material costs, overhead) to generate comprehensive maintenance cost predictions. This merging resolves the contradiction by maintaining accurate distress prediction while adding cost visibility through the integrated cost modeling component.
Solution Approach 2:
The patent introduces intermediate cost modeling components that act as mediators between the distress prediction model and the maintenance decision-making process. These intermediate components translate distress predictions into cost estimates by applying labor rates, material costs, and facility overhead. This intermediary layer resolves the information gap by converting technical distress data into financial terms without compromising the accuracy of the original distress predictions.
2Adaptability or versatility
If repair and overhaul facilities process components without prior cost visibility, then operational flexibility is maintained, but inefficiencies occur in maintenance planning and inventory management
Solution Approach 1:
The patent implements preliminary cost prediction that occurs before assets arrive at repair facilities. By predicting maintenance costs, material requirements, and labor needs in advance using distress models and cost parameters, the system enables proactive maintenance planning. This preliminary action resolves the contradiction by providing cost visibility ahead of time, allowing facilities to optimize inventory levels and schedule maintenance activities efficiently while maintaining operational flexibility.
Solution Approach 2:
The patent establishes feedback loops where actual maintenance costs and outcomes are compared with predicted costs. This feedback information is used to refine and update cost parameters, labor rates, and material cost estimates in the cost modeling component. The feedback mechanism resolves the contradiction by continuously improving prediction accuracy, enabling better advance planning while preserving the ability to adapt to actual conditions when assets arrive at facilities.
3Device complexity
If traditional maintenance approaches are used without predictive modeling, then system complexity is low, but unplanned downtime and excess inventory occur
Solution Approach 1:
The patent changes key parameters from reactive to predictive by introducing distress levels, remaining useful life predictions, and forecasted maintenance costs. Instead of maintaining assets based on fixed schedules or waiting for failure, the system continuously monitors and predicts asset condition parameters. This parameter transformation resolves the contradiction by accepting increased system complexity in exchange for dramatically reduced unplanned downtime through proactive maintenance scheduling.
Data Source
AI summary
Distress models can be generated to model current or future deterioration of components. By correlating distress models with maintenance costs and material consumption, cumulative cost models and cumulative material models can be developed to optimize engine removal timing in order to maximize asset and portfolio value.