Open Access

ARTICLE

Stochastic Differential Equation-Based Dynamic Imperfect Maintenance Strategy for Wind Turbine Systems

Hongsheng Su, Zhensheng Teng^*, Zihan Zhou

School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China

* Corresponding Author: Zhensheng Teng. Email:

Energy Engineering 2026, 123(2), 9 https://doi.org/10.32604/ee.2025.069495

Received 24 June 2025; Accepted 19 August 2025; Issue published 27 January 2026

Abstract

Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy, replacement-based maintenance practices that deviate from actual operational conditions, and static maintenance strategies that fail to adapt to accelerated deterioration trends leading to suboptimal remaining useful life utilization, this study proposes a Time-Based Incomplete Maintenance (TBIM) strategy incorporating reliability constraints through stochastic differential equations (SDE). By quantifying stochastic interference via Brownian motion terms and characterizing nonlinear degradation features through state influence rate functions, a high-precision SDE degradation model is constructed, achieving 16% residual reduction compared to conventional ordinary differential equation (ODE) methods. The introduction of age reduction factors and failure rate growth factors establishes an incomplete maintenance mechanism that transcends traditional “as-good-as-new” assumptions, with the TBIM model demonstrating an additional 8.5% residual reduction relative to baseline SDE approaches. A dynamic maintenance interval optimization model driven by dual parameters—preventive maintenance threshold R_p and replacement threshold R_r—is designed to achieve synergistic optimization of equipment reliability and maintenance economics. Experimental validation demonstrates that the optimized TBIM extends equipment lifespan by 4.4% and reduces maintenance costs by 4.16% at R_p = 0.80, while achieving 17.2% lifespan enhancement and 14.6% cost reduction at R_p = 0.90. This methodology provides a solution for wind turbine preventive maintenance that integrates condition sensitivity with strategic foresight.

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Keywords

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