Wind Turbine Failure Prediction Model Selection for Early Alerts
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Solution Overview
Problem
Existing systems struggle to accurately predict failures in renewable energy assets with sufficient lead time due to inconsistent model evaluation metrics and overwhelming data volumes, leading to reactive responses and substandard predictive accuracy.
Innovation Solution
A method and system using machine learning algorithms to generate and evaluate multiple failure prediction models with standardized metrics, applying curvature analysis and dimensionality reduction techniques to improve accuracy and scalability, enabling proactive failure detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple failure prediction models are generated with different observation time windows and lead time windows, then predictive accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the failure prediction task into multiple models, each trained on different observation time windows and lead time windows. This segmentation allows systematic evaluation of how different time parameters affect prediction accuracy, resolving the contradiction by organizing complexity into manageable, comparable units rather than creating a single overly complex model.
Solution Approach 2:
The patent generates multiple models with varying degrees of historical data utilization (different observation windows) and prediction horizons (different lead time windows). This partial action approach allows selecting the optimal model for specific operational needs, improving predictive accuracy without requiring all models to be equally complex.
2Reliability
If standardized evaluation metrics are applied to compare models, then model selection reliability is improved, but ease of operation decreases
Solution Approach 1:
The patent establishes standardized evaluation metrics (confusion matrix, positive prediction value) before model comparison. This preliminary standardization ensures reliable and consistent model selection while simplifying the operational process by providing a predetermined framework that eliminates ad-hoc evaluation decisions.
3Productivity
If curvature analysis and dimensionality reduction techniques are applied, then scalability is improved, but device complexity increases
Solution Approach 1:
The patent extracts essential features from large sensor datasets using dimensionality reduction techniques, separating the critical predictive information from overwhelming data volumes. This extraction improves scalability by reducing computational burden while maintaining prediction quality.
Solution Approach 2:
The patent applies curvature analysis to transform and analyze data parameters, identifying critical patterns that predict failures. By changing the parameter representation through curvature analysis, the system achieves better scalability without proportionally increasing processing complexity.
Data Source
AI summary
An example method comprises receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.