基于MSCNNSA-BiGRU的变工况风电机组滚动轴承故障诊断研究

Rolling bearing fault diagnosis of wind turbine under variable working conditions based on MSCNNSA-BiGRU

风电机组滚动轴承运行工况复杂多变,存在故障特征区域尺寸不一致、故障难提取、难辨别的问题,为此,提出了一种基于多尺度卷积神经网络(MSCNN)、自注意力(SA)机制与双向门控循环单元(BiGRU)的变工况条件下风电机组滚动轴承故障诊断方法(MSCNNSA-BiGRU)。首先,采用MSCNN提取了轴承原始振动信号的多尺度特征信息;然后,BiGRU结构挖掘原始振动信号的历史与未来信息,更全面地提取了其数据时序特征信息,同时引入self-attention来重点关注故障特征,提高了模型的故障诊断精度;最后,将特征信息融合成了一个特征向量,输入到SoftMax层,实现了对故障的分类;并将该方法应用于实际风电机组滚动轴承故障诊断中。研究结果表明:变工况背景下轴承故障识别准确率为92.7%,与经典的MSCNN网络相比,其故障识别的平均准确率提高8.13%;该方法直接从原始振动信号自适应地提取多尺度的时序特征,并将其进行融合,实现了“端到端”的滚动轴承故障诊断,省去了人工特征提取过程,提高了模型的泛化能力和鲁棒性,对实际工程风电机组滚动轴承故障诊断研究应用具有一定价值。

Aiming at the problems of complex and changeable operation conditions,inconsistent size of feature areas,difficulty in extracting and distinguishing faults for wind turbine rolling bearings,a rolling bearing fault diagnosis method(MSCNNSA-BiGRU)was proposed based on a multi-scale convolutional neural network(MSCNN),the self-attention(SA)mechanism and bidirectional gated recurrent unit(BiGRU).Firstly,the multi-scale feature information of the original vibration signal was extracted by MSCNN.Then,the history and future information of the original vibration signal was mined by the BiGRU,and the time sequence feature information of the data was extracted more comprehensively.Meanwhile,SA was introduced to focus on the fault feature to improve the fault diagnosis accuracy of the model.Finally,the feature information was fused into the feature vector and input into the SoftMax layer to achieve fault classification.The proposed method was applied to the actual rolling bearing fault diagnosis of wind turbines.The results show that the fault identification accuracy is up to 92.7%under different working conditions,and the average accuracy is 8.13%higher than that of the classical MSCNN network.Based on the method,the multi-scale time series features from original vibration signals was directly adaptively extracted,“end-to-end”fault diagnosis was achieved,the process of artificial feature extraction was avoided,and the generalization ability and robustness was improved.It has a certain value for practical engineering research and application of rolling bearing fault diagnosis in wind turbines.