Wind Turbine Component Health Monitoring via Residual Analysis
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Solution Overview
Problem
Current wind turbine monitoring systems require manual interpretation of threshold value exceedances, leading to inefficiencies in interpreting large-scale monitoring data and varying interpretations across different components, which can result in suboptimal maintenance planning and increased costs.
Innovation Solution
A system with an independent data processing environment that receives and processes data from wind turbines using component-specific monitoring algorithms to automatically establish a health value for components, incorporating both direct and indirect monitoring data, as well as failure rate inputs, to provide a uniform and actionable health status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual interpretation of threshold value exceedances is used, then system complexity is reduced, but productivity and consistency of maintenance planning deteriorate
Solution Approach 1:
The monitoring system automatically calculates health values and generates maintenance recommendations without requiring manual interpretation. The system serves itself by processing monitoring data through algorithms that assess component health status and generate actionable insights, eliminating the need for human analysts to manually interpret threshold exceedances
Solution Approach 2:
The system transforms raw monitoring data into meaningful health values by applying mathematical models and algorithms. Instead of simply monitoring threshold exceedances, the system calculates composite health parameters that provide a unified assessment of component status, enabling automated decision-making
2Device complexity
If manual interpretation of monitoring data is used, then device complexity is reduced, but measurement precision and consistency of health assessment deteriorate
Solution Approach 1:
The system automatically and consistently applies the same health assessment algorithms to all components, eliminating variability introduced by different human interpreters. The standardized automated process ensures that identical monitoring data always produces identical health assessments, improving measurement precision and consistency
Solution Approach 2:
The system transforms diverse monitoring parameters into a unified health value scale, enabling consistent comparison across different component types. By converting various sensor readings into standardized health assessments, the system achieves precision and uniformity in evaluating component condition
3Reliability
If component-specific monitoring algorithms are implemented, then reliability of health assessment is improved, but device complexity increases
Solution Approach 1:
The monitoring system is divided into component-specific modules, each with dedicated algorithms tailored to the unique characteristics of individual component types. This segmentation allows each algorithm to be optimized for its specific component while maintaining overall system manageability through modular architecture
Solution Approach 2:
Despite being component-specific, the algorithms follow a universal framework and methodology for health assessment. The standardized approach to calculating health values across different components provides reliability and consistency, while the modular design keeps complexity manageable through reuse of common computational patterns
Data Source
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AI summary
The invention relates to a system for monitoring of wind turbine components comprising an independent data processing environment adapted to: receive a first category of data input related to operation of the wind turbine, process the received data input by one or more component specific monitoring algorithms adapted to establish an estimated component value related to a component to be monitored based on received first category data input having at least indirectly impact on the component, wherein the component specific monitoring algorithm is adapted to establish a component residual as the difference between the estimated component value and received first category of data input of the component to be monitored, and wherein the component specific monitoring algorithm furthermore is adapted to establish a component specific health value of the component to be monitored based on the established residual and put the health value at disposal for data processors outside the environment.