Wind Turbine Component Risk Forecasting for Predictive Maintenance
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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 data inputs from sensors, calculate a consolidated risk index, forecast a range of potential risk indices, and determine a remaining-useful-life distribution based on historical fleet-turbine data, allowing for proactive maintenance scheduling and idling or shutdown of wind turbines when necessary.
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 significantly due to premature component replacement
Solution Approach 1:
The system performs preliminary prognostic analysis using monitored attributes and risk index calculations to predict component failure before it occurs. This allows maintenance to be scheduled based on actual component condition and predicted remaining useful life, rather than following fixed preventive maintenance schedules that may replace components prematurely.
Solution Approach 2:
The system continuously monitors component attributes, calculates risk indices, and updates remaining useful life predictions based on actual component behavior. This feedback loop enables dynamic adjustment of maintenance schedules, allowing operators to delay maintenance when components are performing well and accelerate it when degradation is detected, optimizing both reliability and cost.
2Quantity of substance
If maintenance activities are delayed, then maintenance cost is reduced, but unexpected component failure occurs resulting in extended shutdown and increased operational cost
Solution Approach 1:
The system calculates remaining useful life predictions and forecasts potential risk indices before component failure occurs. This preliminary prognostic information allows operators to schedule maintenance activities in advance during planned downtime, avoiding unexpected failures and extended unplanned shutdowns while still delaying maintenance until it is actually needed.
Solution Approach 2:
The continuous monitoring and risk index calculation provide real-time feedback on component health status. This enables operators to make informed decisions about when to schedule maintenance, balancing the cost of maintenance against the risk of unexpected failure and operational disruption.
3Productivity
If unanticipated component failure occurs, then service action efficiency decreases due to multiple ground crane visits, but maintenance cost increases
Solution Approach 1:
By predicting component failures in advance using risk index calculations and remaining useful life forecasts, the system allows maintenance to be scheduled during planned service visits. This eliminates the need for multiple unplanned ground crane visits, improving service action efficiency and reducing maintenance costs associated with emergency repairs.
4Reliability
If excessive parts inventory is maintained to prevent unexpected failures, then component reliability is improved, but inventory cost and storage requirements increase
Solution Approach 1:
The system provides real-time feedback on component health status and predicted remaining useful life, allowing operators to optimize parts inventory based on actual component conditions across the fleet. Instead of maintaining excessive inventory for all components, operators can focus inventory resources on components showing signs of degradation, reducing overall inventory costs while maintaining reliability.
Data Source
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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.