基于IMHSA-MSCNN-BiLSTM的风机轴承故障诊断

由于风力发电机组的非平稳运行条件和周围恶劣的工作环境，风机轴承故障振动脉冲特征易被随机噪声干扰所淹没，这给准确检测滚动轴承故障造成了挑战。为了降低随机干扰对后续特征提取的影响和算法复杂度，提出了一种改进多头自注意力机制（IMHSA）-多尺度卷积网络（MSCNN）-双向长短期记忆网络（BiLSTM）的风机轴承故障诊断方法。首先，由周期空洞自注意力和局部自注意力组成的IMHSA对特征进行增强，以减少随机干扰影响及特征增强过程中的时间消耗；然后，利用MSCNN-BiLSTM网络提取故障信号中的空间特征与长期依赖特征；最后，经全连接层和Softmax层输出风机轴承故障诊断结果，并采用实验台滚动轴承实际运行数据进行算例分析，通过与领域内其他同类方法的对比，验证了所提方法的有效性和优越性。

Fault diagnosis of fan bearings based on IMHSA-MSCNN-BiLSTM

Due to the non-stationary operating conditions and harsh working environment of wind turbines, the vibration pulse characteristics of wind turbine bearing faults are easily overwhelmed by random noise interference, which poses a challenge to accurately detect rolling bearing faults. In order to reduce the impact of random interference on subsequent feature extraction and algorithm complexity, an improved multi head self attention mechanism (IMHSA)-multi-scale convolutional network (MSCNN)-bidirectional long short-term memory network (BiLSTM) wind turbine bearing fault diagnosis method is proposed. Firstly, the IMHSA composed of periodic cavity self attention and local self attention enhances the features to reduce the impact of random interference and the time consumption during the feature enhancement process; Then, the MSCNN-BiLSTM network is used to extract spatial features and long-term dependency features from the fault signal; Finally, the fault diagnosis results of the fan bearings were output through the fully connected layer and Softmax layer, and the actual operating data of the rolling bearings on the experimental platform was used for numerical analysis. The effectiveness and superiority of the proposed method were verified by comparing it with other similar methods in the field.