Optimizing Assembly Error Reduction in Wind Turbine Gearboxes Using Parallel Assembly Sequence Planning and Hybrid Particle Swarm-Bacteria Foraging Optimization Algorithm

Sydney Mutale (), Yong Wang () and Tian De
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Sydney Mutale: School of New Energy, North China Electric Power University, Beijing 102206, China
Yong Wang: School of New Energy, North China Electric Power University, Beijing 102206, China
Tian De: School of New Energy, North China Electric Power University, Beijing 102206, China

Energies, 2025, vol. 18, issue 15, 1-31

Abstract: This study introduces a novel approach for minimizing assembly errors in wind turbine gearboxes using a hybrid optimization algorithm, Particle Swarm-Bacteria Foraging Optimization (PSBFO). By integrating error-driven task sequencing and real-time error feedback with the PSBFO algorithm, we developed a comprehensive framework tailored to the unique challenges of gearbox assembly. The PSBFO algorithm combines the global search capabilities of PSO with the local refinement of BFO, creating a unified framework that efficiently explores task sequencing, minimizing misalignment and torque misapplication assembly errors. The methodology results in a 38% reduction in total assembly errors, improving both process accuracy and efficiency. Specifically, the PSBFO algorithm reduced errors from an initial value of 50 to a final value of 5 across 20 iterations, with components such as the low-speed shaft and planetary gear system showing the most substantial reductions. The 50 to 5 error reduction represents a significant decrease in assembly errors from an unoptimized (50) to an optimized (5) sequence, achieved through the PSBFO algorithm, by minimizing dimensional deviations, torque mismatches, and alignment errors across 26 critical gearbox components. While the primary focus is on wind turbine gearbox applications, this approach has the potential for broader applicability in error-prone assembly processes in industries such as automotive and aerospace, warranting further validation in future studies.

Keywords: assembly errors; wind turbine gearboxes; particle swarm-bacteria foraging optimization (PSBFO); hybrid optimization; task sequencing; real-time error feedback (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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