Condition-based opportunistic maintenance strategy for multi-component wind turbines by using stochastic differential...

Area of Science:

• Engineering
• Reliability Engineering
• Maintenance Strategy

Background:

• Wind turbines operate in harsh environments, leading to high failure rates and maintenance costs.
• Complex component structures exacerbate reliability challenges in wind energy systems.
• Existing maintenance strategies often fail to adequately address the dynamic nature of wind turbine degradation.

Purpose of the Study:

• To develop a novel Condition-Based Opportunistic Maintenance strategy for wind turbines.
• To address the high failure rates and associated maintenance costs in wind turbine operations.
• To integrate operational time and equipment state for accurate failure rate prediction.

Main Methods:

• A stochastic differential equation model was developed to simulate harsh operating conditions and random disturbances using Brownian motion.
• A new failure rate model was established based on Weibull distribution, incorporating operating time and equipment state.
• Higher-Order Moment and Bayesian methods were employed for parameter estimation from monitoring data.
• Analysis of Time-Based Maintenance and Condition-Based Maintenance yielded opportunity and preventive maintenance thresholds.

Main Results:

• The study successfully derived opportunity and preventive maintenance threshold curves.
• A comprehensive Condition-Based Opportunistic Maintenance strategy was formulated.
• The proposed strategy demonstrated effectiveness in improving wind turbine maintenance through validated arithmetic examples.

Conclusions:

• The developed Condition-Based Opportunistic Maintenance strategy effectively addresses the challenges of high failure rates and costs in wind turbines.
• Integrating operational data and probabilistic models enhances maintenance planning and execution.
• The findings provide a robust framework for optimizing wind turbine reliability and operational efficiency.