Electrical Machine Thermal Monitoring for RUL Prediction
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Solution Overview
Problem
Existing thermal models for electrical machines lack the ability to adapt effectively to changing characteristics over time, leading to inefficient prediction of temperature distribution and reduced remaining useful life due to factors like dirt buildup and operational changes.
Innovation Solution
A method that updates thermal parameter values based on temperature measurements, estimates future values using a degradation model that considers past behavior and current conditions, and determines future degradation and remaining useful life, allowing for proactive mitigating actions to slow down degradation and extend life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional thermal models are used without adaptation, then the model structure remains simple, but the prediction accuracy deteriorates over time due to changing machine characteristics
Solution Approach 1:
The thermal model transitions from a static structure with fixed parameters to a dynamic system where thermal parameters are continuously updated based on measured temperature data. The model adapts its parameters over time to reflect changing machine characteristics, maintaining prediction accuracy without requiring structural changes.
Solution Approach 2:
The invention changes the thermal parameters of the model over time based on measured temperature data. By continuously updating parameters such as thermal resistances and capacitances, the model adapts to changing machine characteristics while maintaining its original structure, resolving the contradiction between simplicity and adaptability.
2Measurement precision
If thermal parameters are continuously updated to maintain accuracy, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The system implements feedback by continuously comparing measured temperatures with model predictions and using this information to update thermal parameters. This feedback mechanism enables automatic adaptation to changing conditions without requiring complex manual intervention or reconfiguration of the model structure.
Solution Approach 2:
The thermal model performs self-updating of its parameters based on operational data. The system automatically adjusts its own parameters without external intervention, maintaining accuracy while keeping the overall system complexity manageable through autonomous adaptation.
3Productivity
If degradation is allowed to progress without monitoring, then operational continuity is maintained, but remaining useful life decreases due to undetected hotspots and defects
Solution Approach 1:
The system performs preliminary actions by predicting future thermal states and degradation trends before actual damage occurs. By estimating future values of thermal parameters and identifying potential hotspots in advance, the system enables proactive maintenance decisions that extend remaining useful life without interrupting operational continuity.
Solution Approach 2:
The thermal model acts as an intermediary between actual machine state and maintenance decisions. It translates measured temperatures into predicted future states and degradation assessments, providing early warnings that enable timely interventions to extend reliability while maintaining productivity.
Data Source
AI summary
A method of monitoring thermal parameters of a thermal model of an electrical machine, the method including: a) updating thermal parameter values of the thermal model based on temperature measurements of the electrical machine, b) estimating future values of the thermal parameters based on the updated thermal parameter values by means of a degradation model, which takes past behaviour of the thermal parameters into account, and c) determining a future degradation and/or a remaining useful life of the electrical machine based on the future values.