| Title: |
Stochastic Differential Equation-Based Dynamic Imperfect Maintenance Strategy for Wind Turbine Systems |
| Authors: |
Hongsheng Su; Zhensheng Teng; Zihan Zhou |
| Source: |
Energy Engineering ; ISSN: 0199-8595 (Print) ; ISSN: 1546-0118 (Online) ; Volume 123 ; Issue 2 |
| Publisher Information: |
Tech Science Press |
| Publication Year: |
2026 |
| Subject Terms: |
Stochastic differential equations (SDE); imperfect maintenance; condition-based maintenance (CBM); time-based maintenance (TBM); reliability constraint; wind turbine |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
https://doi.org/10.32604/ee.2025.069495 |
| DOI: |
10.32604/ee.2025.069495 |
| Availability: |
https://doi.org/10.32604/ee.2025.069495 |
| Rights: |
info:eu-repo/semantics/openAccess ; https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.8D4630F |
| Database: |
BASE |