Statistic Discrepancy Oriented Cyclo-Non-Stationary Indicator for Wind Turbine Condition Monitoring Under Varying Speed Conditions

作者：Guangyao Zhang, Zhongchao Liang, Tianyang Wang, Fulei Chu · 发表于：IEEE Transactions on Cybernetics · 年份：2025 · DOI：10.1109/tcyb.2025.3611840 · 被引用次数：2 · 研究领域：Advanced Measurement and Detection Methods、Engineering Diagnostics and Reliability、Sensor Technology and Measurement Systems

As typical and complex mechatronic system, health state of the wind turbine (WT) is of significant importance to the sustained and reliable service. However, it is noted that influenced by the seasonal or fitful wind, WTs unavoidably serve in the dynamically varying environment. In this event, most of the currently available indicators expose deficiency in regard of the false or missed alarms due to the coupled condition interference. To address this issue and improve the reliability of the mechatronic system, a novel statistic discrepancy oriented cyclo-non-stationary (CNS) indicator is developed in this article. First, characteristics of the recorded degradation samples are revealed by a multiparametric model, during which the consistency is verified and improved by the hypothesis test. Second, a specific speed-dependent slicing (SDS) operator is then designed, aiming to alleviate the varying-speed-induced modulation interference at the different degradation stages. With this developed SDS operator, a CNS indicator, which can well adapt to the dynamically varying environment during the operating process, is subsequently developed by incorporating the resampling-based statistic discrepancy evaluating mechanism. Experiments indicate that the proposed method can effectively characterize the health state of the transmission parts of the industrial WT under varying speed conditions.