Electromechanical Component RUL Estimation Using Physics-Based Fault Models
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Solution Overview
Problem
Existing methods fail to accurately predict the remaining service life of electromechanical components due to limitations in fault detection and monitoring, leading to unplanned downtimes and maintenance.
Innovation Solution
A method that compares sensor measurement data with fault patterns using a numerical simulation on a physical model, combined with an artificial neural network for high reliability, to determine the remaining service life by simulating stress loads and material properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If fault detection is performed using sensor data alone, then fault monitoring capability is improved, but prediction accuracy of remaining service life deteriorates
Solution Approach 1:
The patent introduces a physical model as an intermediary between sensor data and RUL prediction. The physical model (comprising mathematical equations representing component behavior) transforms raw sensor measurements into meaningful degradation states, enabling accurate RUL prediction. This mediator bridges the gap between simple fault detection and precise remaining life estimation.
Solution Approach 2:
The patent replaces purely data-driven machine learning approaches with a physics-based modeling approach. Instead of relying on black-box algorithms to predict RUL from sensor data, the system uses explicit physical models (mathematical equations) that represent the actual mechanical and physical degradation processes, providing interpretable and accurate predictions.
2Reliability
If numerical simulation on physical model is used, then prediction reliability is improved, but computational complexity increases
Solution Approach 1:
The patent changes the parameters of the physical model dynamically based on sensor data. Instead of solving complex simulations continuously, the system updates key parameters (degradation state, stress loads, material properties) based on real-time sensor measurements, maintaining prediction reliability while reducing computational burden through parameter-based adaptation rather than full re-simulation.
Solution Approach 2:
The patent performs preliminary setup of the physical model offline, pre-defining the mathematical structure, material properties, and boundary conditions. This preliminary action allows the online prediction system to only update specific parameters based on sensor data rather than rebuilding the entire simulation model, significantly reducing real-time computational complexity while maintaining reliability.
Data Source
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AI summary
The invention relates to a method for estimating the remaining service life of at least one electromechanical component. To achieve high reliability in estimating the remaining service life, it is proposed that at least one system parameter is determined from sensor data of at least one sensor device (6) to which at least one electromechanical component (4) is assigned, wherein at least one failure case of the electromechanical component (4) is assigned to the system parameter, wherein a stress load on the electromechanical component (4) is simulated on a physical model using a numerical method based on the system parameter, and wherein a remaining service life is determined from the simulated stress load.