A novel self-learning framework for fault identification of wind turbine drive bearings

涡轮机 计算机科学 断层(地质) 鉴定(生物学) 风力发电 人工智能 希尔伯特-黄变换 工程类 聚类分析 故障检测与隔离 机械工程 植物 电气工程 滤波器(信号处理) 地震学 执行机构 计算机视觉 生物 地质学
作者
Jing Yuan,Zeming Liang,Rongxi Wang,Yufan Li,Zhen Wang,Jianmin Gao
出处
期刊:Proceedings Of The Institution Of Mechanical Engineers, Part I: Journal Of Systems And Control Engineering [SAGE Publishing]
卷期号:237 (7): 1296-1312
标识
DOI:10.1177/09596518231153231
摘要

The drive train is one of the core structures of a wind turbine, and its working condition seriously affects the performance quality. It is important to identify the fault pattern of the drive bearings in time to ensure the safety and reliability of the wind turbine. However, in traditional methods, offline modeling and online identification are often fragmented, and such a mechanism limits the adaptive updating of the model. To realize real-time updating and self-learning of the identification model, we proposed a novel self-learning framework for the intelligent fault identification of wind turbine drive bearings. First, a complete ensemble empirical mode decomposition with adaptive noise analysis–based quantification scheme for intrinsic mode function values is proposed. Then, based on the intrinsic mode function values, we offer an attention mechanism for fault feature identification and construct an initial fault pattern database using unsupervised clustering techniques. Second, abnormal data are identified by the proposed artificial immunity–based outlier detection algorithm to determine the type of immune response. Third, we design an automatic update strategy based on incremental learning to realize adaptive creation, deletion, and modification of fault patterns. The proposed intelligent framework is applied to the fault diagnosis of a real offshore wind turbine drive train, showing its advantages in intelligent fault identification and model updating.
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