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.

Nowadays, the massive industrial data has effectively improved the performance of the data-driven deep learning Remaining Useful Life (RUL) prediction method. However, there are still problems of assigning fixed weights to features and only coarse-grained consideration at the sequence level. This paper proposes a Transformer-based end-to-end feature-level mask self-supervised learning method for RUL prediction. First, by proposing a fine-grained feature-level mask self-supervised learning method, the data at different time points under all features in a time window is sent to two parallel learning streams with and without random masks. The model can learn more fine-grained degradation information by comparing the information extracted by the two parallel streams. Instead of assigning fixed weights to different features, the abstract information extracted through the above process is invariable correlations between features, which has a good generalization to various situations under different working conditions. Then, the extracted information is encoded and decoded again using an asymmetric structure, and a fully connected network is used to build a mapping between the extracted information and the RUL. We conduct experiments on the public C-MAPSS datasets and show that the proposed method outperforms the other methods, and its advantages are more obvious in complex multi-working conditions.

Operating condition monitoring of wind turbine (WT) key components is of significant importance to preventative maintenance and the improvement of WT reliability. To realize this industrial target, health indicator (HI) construction is a crucial and indispensable step. While most of the recently reported HIs are emphasized effective in stationary cases, they are insufficiently applicable to variable speed conditions. To address this issue, a novel HI through operating speed slicing and discrepancy compensation is proposed in this article for WT generator bearing condition monitoring. First, signal probability distributions of the collected degradation data are appropriately characterized by an optimized multiparameter regression method. Then, benchmark distributions established at the normal state are identified through operating speed slicing, and the discrepancies induced by the time-varying operating condition are subsequently calibrated with a compensation strategy. On this basis, a globally comparable metric, by quantitatively evaluating the degree to which the currently established distribution deviates from the corresponding slice-related benchmark, is accordingly constructed. Experimental tests demonstrate that the proposed HI can make a more effective health state assessment for WT generator bearing under variable speed conditions when compared with the conventional indicators.