Wind Turbine Prognostics for Remaining Useful Life Forecasting
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Solution Overview
Problem
Current wind turbine maintenance relies on preventative measures based on past observations rather than quantifiable prognostics, leading to increased costs due to either premature or unexpected component failures, resulting in inefficient service actions and excessive inventory requirements.
Innovation Solution
A method and system that utilize a controller to receive and analyze data inputs from sensors to determine a consolidated risk index, forecast a range of potential risk indices, and calculate a remaining-useful-life distribution, allowing for proactive maintenance scheduling and idling or shutdown of wind turbines when necessary, thereby reducing unplanned maintenance and optimizing component usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If preventative maintenance is scheduled at or in advance of expected lifespan, then component reliability is improved, but maintenance cost increases due to premature component replacement
Solution Approach 1:
The system performs preliminary prognostic analysis to predict component remaining useful life before failure occurs. By forecasting component degradation trends and predicting failure timing, the system enables maintenance to be scheduled precisely when needed, avoiding both premature replacement and unexpected failures. This preliminary prediction capability resolves the contradiction by replacing components based on actual condition rather than fixed time intervals.
2Quantity of substance
If maintenance activities are delayed, then maintenance cost decreases, but unexpected component failure occurs resulting in extended shutdown
Solution Approach 1:
The system continuously monitors component condition parameters and feeds this information back to update the prognostic model. This feedback loop tracks actual component degradation against predicted trends, allowing the system to adjust maintenance scheduling based on real-time condition assessment. The feedback mechanism enables delayed maintenance when components are healthy while triggering timely intervention when degradation accelerates, thus avoiding unexpected failures without premature maintenance.
3Measurement precision
If component monitoring systems are used to detect anomalies, then component failure detection is improved, but service action efficiency decreases due to multiple ground crane visits
Solution Approach 1:
The system performs preliminary prognostic assessment to identify components that will fail within a specific time horizon. By predicting which components require maintenance and when, the system enables advance scheduling of service actions, allowing consolidation of multiple maintenance tasks into single site visits. This eliminates the need for multiple ground crane visits by planning all necessary service actions in advance based on predicted failures.
4Reliability
If excessive parts are maintained in inventory to prevent unexpected failure, then component reliability is improved, but inventory cost increases
Solution Approach 1:
The system changes the parameter basis for inventory management from fixed time-based replacement schedules to condition-based prognostic predictions. By predicting actual component failure timing based on monitored degradation, the system optimizes inventory levels to match predicted maintenance needs. This reduces inventory costs by eliminating excess parts while maintaining reliability through accurate prediction of when components will require replacement.
Data Source
AI summary
A system and method are provided for operating and maintaining a wind turbine. Accordingly, a plurality of data inputs are received. The plurality of data inputs represent a plurality of monitored attributes of a component of the wind turbine. A consolidated risk index for the component is determined using the plurality of monitored attributes, and a range of potential risk indices is forecasted. A remaining-useful-life distribution is determined based on the damage potential and an end-of-life damage threshold. The wind turbine is shut down or idled if the remaining-useful-life distribution is below a shutdown threshold.