超大型海上风电吊装装备故障诊断与预报的多尺度方法研究

Nowdays, the offshore wind power generation has become one of the focuses in the field of the renewable energy resources worldwide. The offshore windpower lifting equipments are the main facilities to carry out the pipe pile foundation in construction and the offshore windpower installation tasks in the oceans. With the developing trend of large-scale and complication for the equipments, the factors affecting their normal work are increasing sharply. In such circumstance, the offshore windpower lifting equipments are frail to faults, and it is becoming more difficult for their work condition monitoring and fault diagnosis. Therefore, it is an urgent requirement in the engineering practice to investigate the fault diagnosis and prediction technology of the incipient faults for the offshore windpower lifting equipments. This project focuses on the fault diagnosis and prediction of incipient faults based on the various operating data from the typical super-large offshore windpower lifting equipments. Multi-scale based approches will be investiged in the project. The main research topics include: multi-scale modeling theory of the monitoring models for fault diagnosis and prediction; multi-scale ICA (independent component analysis) method for the fault diagnosis and prediction, multi-scale HOS (high-order statistics) analysis theory for the fault detection; integrated multi-scale fault diagnosis strategy for the incipient faults; and the testing techniques for the applications of these methods in practice. The resulting achievements will develop the reliability theory of the super-large offshore windpower lifting equipments, and advance the development of fault diagnosis and prediction technology for such equipments. It will benefit the technical improvement for the equipments manufacturing in China, and enhance its competitive power in the global market.

海上风力发电已成为世界可再生能源发展领域的焦点。海上风电吊装设备是实现海洋管桩基础施工、海上风电安装作业的关键装备。随着其日益大型化和复杂化，导致影响设备正常吊装的因素骤增、发生故障的可能性增加、状态监测和故障诊断难度加大，因此研究海上风电吊装装备的早期故障诊断和预报技术理论已成为工程实际的迫切需要。本项目立足于超大型海上风电吊装装备的大量运行数据，研究基于运行数据的早期故障诊断和故障预报的多尺度理论和方法，包括用于故障诊断与预报的多尺度数据监控建模技术理论，多尺度ICA分析方法，多尺度高阶统计分析检测理论，早期故障诊断的集成多尺度方法策略，以及上述理论方法的应用验证技术理论等内容。研究成果有望丰富超大型海上吊装装备的可靠性理论，推动超大型海上吊装装备故障诊断与预测技术的发展，对我国海上超大型吊装装备向高端和前沿转型，提高超大型海上吊装装备国际竞争力将起到促进作用。

随着海上风电起重机日益大型化和复杂化，导致影响设备正常吊装的因素骤增、发生故障的可能性增加、状态监测和故障诊断难度加大，因此研究其早期故障诊断和预报技术理论已成为工程实际的迫切需要。本课题依据项目研究计划，开展了关于海上风电起重机故障诊断与预报相关的技术研究与应用开发。项目主要完成了以下内容：1.基于运行与监测数据的数据建模技术，结合吊装设备起升系统液压系统，开展了针对非线性特征的建模理论研究；2.基于多尺度分析的故障诊断和预报方法研究，提出了利用油液检测的减速器磨损趋势预测以及基于振动信号的关键部件故障诊断方法；3.对于海上风电起重机在特殊工况下，关键部件所出现的特殊故障模式进行了实验测试与分析，并验证了相关故障诊断方法的实用性；4.结合故障诊断与预测相关成果，设计开发了基于数据处理的多尺度集成故障诊断系统。本项目的研究成果为超大型海上吊装装备的可靠性提供了理论依据，推动超大型海上吊装装备故障诊断与预测技术的发展。