Design and Implementation of a Misalignment Experimental Data Management Platform for Wind Power Equipment

Jianlin Cao, Qiang Fu, Pengchao Li, Bingchang Zhao, Zhichao Liu and Yanjie Guo ()
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Jianlin Cao: China Guanghe New Energy Holding Co., Ltd. Shaanxi Branch, Xi’an 710065, China
Qiang Fu: China Guanghe New Energy Holding Co., Ltd. Shaanxi Branch, Xi’an 710065, China
Pengchao Li: China Guanghe New Energy Holding Co., Ltd. Shaanxi Branch, Xi’an 710065, China
Bingchang Zhao: China Guanghe New Energy Holding Co., Ltd. Shaanxi Branch, Xi’an 710065, China
Zhichao Liu: School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Yanjie Guo: School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Energies, 2025, vol. 18, issue 19, 1-13

Abstract: Key drivetrain components in wind turbines are prone to misalignment faults due to long-term operation under fluctuating loads and harsh environments. Because misalignment develops gradually rather than occurring instantly, reliable evaluation of structural designs and surface treatments requires long-duration, multi-sensor, and multi-condition experiments that generate massive heterogeneous datasets. Traditional data management relying on manual folders and USB drives is inefficient, redundant, and lacks traceability. To address these challenges, this study presents a dedicated misalignment experimental data management platform specifically designed for wind power applications. The innovation lies in its ability to synchronize vibration, electrostatic, and laser alignment data streams in long-term tests, establish a traceable and reusable data structure linking experimental conditions with sensor outputs, and integrate laboratory results with field SCADA data. Built on Laboratory Information Management System (LIMS) principles and implemented with an MVC + Spring Boot + B/S architecture, the platform supports end-to-end functions including multi-sensor data acquisition, structured storage, automated processing, visualization, secure sharing, and cross-role collaboration. Validation on drivetrain shaft assemblies confirmed its ability to handle multi-terabyte datasets, reduce manual processing time by more than 80%, and directly integrate processed results into fault identification models. Overall, the platform establishes a scalable digital backbone for wind turbine misalignment research, supporting structural reliability evaluation, predictive maintenance, and intelligent operation and maintenance.

Keywords: misalignment; data management platform; spring boot; wind power equipment; LIMS (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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