Asset Lifetime Monitoring Using Root Cause RUL Prediction
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Solution Overview
Problem
Current methods for monitoring the remaining useful lifetime of assets are inefficient, often leading to premature or unnecessary maintenance, resulting in downtime and additional costs due to incorrect manual analysis and routine maintenance schedules.
Innovation Solution
A method using a model to generate remaining lifetime values based on root cause variables, combining sensor data and health indices to provide accurate predictions of asset deterioration, enabling timely maintenance and reducing unnecessary resource expenditure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If routine maintenance schedules are implemented to keep assets operational, then asset reliability is improved, but unnecessary maintenance causes downtime and additional costs
Solution Approach 1:
The system performs preliminary analysis of asset data using multiple root cause models to predict remaining useful lifetime before maintenance is scheduled. This allows maintenance to be timed precisely when needed, avoiding both premature and delayed maintenance actions.
Solution Approach 2:
The system changes the parameter of maintenance scheduling from fixed time intervals to dynamic predictions based on real-time asset condition and multiple root cause variable analysis. This transforms routine maintenance into condition-based maintenance, reducing unnecessary downtime while maintaining reliability.
2Device complexity
If manual analysis methods are used to assess asset health, then implementation complexity is reduced, but measurement precision of remaining useful lifetime deteriorates
Solution Approach 1:
The system creates virtual copies of the physical asset's condition through digital twins and modeling. Multiple root cause models generate virtual representations of asset degradation, allowing precise prediction of remaining useful lifetime without complex manual physical assessments.
Solution Approach 2:
The system replaces manual mechanical assessment methods with automated computational models. Multiple root cause variables are processed through algorithms that substitute human judgment with precise mathematical predictions, dramatically improving measurement precision while maintaining ease of use through automated reporting.
3Measurement precision
If multiple root cause models are utilized to assess different aspects of asset health, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments asset health assessment into multiple independent root cause models, each analyzing specific failure modes. This segmentation allows each model to specialize in particular aspects of asset degradation while the system integrates their predictions to provide comprehensive remaining useful lifetime assessment.
Solution Approach 2:
The system merges predictions from multiple root cause models into a unified remaining useful lifetime prediction. By combining the strengths of different models that analyze different root causes, the system achieves comprehensive asset health assessment while managing complexity through integrated output synthesis.
4Reliability
If maintenance is performed based on incorrect manual analysis, then immediate asset reliability is maintained, but long-term costs and downtime increase due to premature maintenance
Solution Approach 1:
The system implements continuous feedback loops that monitor asset condition and update remaining useful lifetime predictions in real-time. This feedback mechanism allows the system to adapt maintenance recommendations based on actual asset performance, preventing both premature and delayed maintenance while optimizing resource allocation.
Solution Approach 2:
The system performs preliminary predictive analysis to determine the optimal maintenance timing before maintenance is executed. By predicting remaining useful lifetime with high precision using multiple root cause models, the system ensures maintenance is performed only when necessary, eliminating waste of resources on premature maintenance while maintaining asset reliability.
Data Source
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AI summary
Embodiments of the present disclosure provide for asset lifetime monitoring. Estimated values for factors representing root causes of operational anomalies of an asset may be generated utilizing one or more models. Estimated remaining lifetime values for one or more root cause variables may be generated that indicate a time until the value for a root cause variable is estimated to reach a particular limit threshold corresponding to the root cause variable, and/or an estimated second remaining lifetime value for an asset health index representing a combination of one or more root cause variables. The second remaining lifetime value for the asset health index may be provided to enable processing of the second remaining lifetime value as the remaining useful lifetime of the asset based on overall root cause variables. The first remaining lifetime value for one or more individual root cause variables may be provided to enable more detailed insight into individual root causes of operational degradation of an asset.