Wind Turbine Component Failure Risk Modeling With Survival Analysis
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Solution Overview
Problem
Existing methods struggle to accurately predict the future risk of failure of wind turbine components due to heterogeneity in input data and varying failure modes, making it challenging to build personalized prognostic models that account for different assets and their operational conditions.
Innovation Solution
A system and method using machine learning and advanced survival analysis techniques to build joint and conditional survival models, integrating multiple data types such as reliability and time-series measurements, and adapting model structures based on data availability and sensitivity to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a personalized prognostic model is built for each asset, then prediction accuracy is improved, but model complexity and data processing requirements increase
Solution Approach 1:
The patent segments the prognostic modeling process into multiple specialized models (e.g., Weibull models, exponential models, power law models) that can be selectively applied to different assets based on their specific characteristics. This allows personalized prediction for each asset while avoiding the need for a single overly complex universal model.
Solution Approach 2:
The patent employs parameter estimation techniques that adapt model parameters to match specific asset behavior patterns. By changing and optimizing parameters based on observed failure data and operational conditions, the system achieves high prediction accuracy without requiring complex model structures.
2Measurement precision
If multiple data types are integrated, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent merges multiple data types (failure data, operational data, environmental data) into a unified prognostic framework. By combining these diverse data sources through standardized processing procedures and integrated models, the system achieves comprehensive prediction accuracy while managing data processing complexity through systematic integration.
3Adaptability or versatility
If fleet-wide coverage is enhanced, then model applicability is improved, but customization for individual assets becomes more difficult
Solution Approach 1:
The patent creates a universal prognostic platform that can be applied across entire fleets of assets. The system uses standardized model frameworks and data processing procedures that work across multiple assets, while incorporating asset-specific parameter estimation to maintain personalization accuracy within the fleet-wide context.
Solution Approach 2:
The patent implements dynamic model selection and parameter adjustment capabilities that adapt to individual asset characteristics within the fleet. The system can dynamically switch between different prognostic models and adjust parameters based on each asset's specific failure patterns and operational conditions, maintaining both fleet-wide coverage and individual customization.
Data Source
AI summary
A method for estimating future risk of failure of a component of an industrial asset. The method includes receiving a plurality of different types of data associated with the industrial asset or a fleet of industrial assets. The plurality of different types of data includes, at least, reliability data (such as time-to-event data). The method also includes generating a failure prediction model for the component based on the reliability data and available time-series measurements. Further, the method includes applying the failure prediction model to the different types of data based on the types of data available in the received data. The applied failure prediction model includes one of a default model, a conditional survival model, or a joint conditional survival model. Thus, the method includes estimating, via the failure prediction model, the future risk of failure of the industrial asset and implementing a control action as needed.