摘要
针对滚动轴承故障在噪声环境下诊断效果不佳的问题,提出了一种多源传感器数据下基于注意力机制与长短期记忆(long short-term memory,LSTM)网络的轴承故障诊断新方法。首先,将多源传感器采集的1维数据进行归一化处理,通过构建带AdaBN(Adaptive Batch Normalization)技术的双通道孪生卷积网络提取有效特征并进行数据融合。其次,将融合数据输入具有同时考虑通道间关系和位置信息功能的改进1维CoordAtt(Coordinate Attention)中。再次,通过LSTM层提取时间特征,通过标签平滑正则化改进后的损失函数来评估诊断效果,使用新型优化器Adan进行优化。最后,将得到的诊断模型应用于测试集,输出故障类别诊断结果。将模型在不同测试集比例下进行诊断精度对比实验,判断出最佳比例为0.3,并在噪声环境下进行测试。实验结果表明,所提方法能更好地对抗噪声环境的影响。在C-MAPSS数据集上的实验结果验证了CoordAtt-LSTM模型在寿命预测中的有效性。
To solve the problem of the poor diagnosis effect of rolling bearing faults in noisy environments,we propose a new bearing fault diagnosis method based on an attention mechanism and a long short-term memory(LSTM)network using multisource sensor data.First,we normalize the one-dimensional(1D)data collected by multisource sensors and then construct a double-channel twin convolutional network with adaptive batch normalization technology to extract effective features and perform data fusion.Second,the fusion data are input into the improved 1D coordinate attention(CoordAtt)which is capable of considering the relationship between channels and position information simultaneously.Third,we use the LSTM layer to extract time features and evaluate the diagnosis effect by the loss function after label smoothing regularization.We use the new optimizer Adan for optimization.Finally,the diagnosis model is applied to a test set,and the diagnosis results of fault categories are output.We compare the diagnostic accuracy of the model under different test set ratios,determine that the optimal ratio is 0.3,and test it in a noisy environment.The experimental results show that the proposed method can better resist the influence of noisy environments.The validity of the CoordAtt-LSTM model in life prediction is verified by experimental results on the C-MAPSS dataset.
作者
陈翔
刘勤明
胡家瑞
CHEN Xiang;LIU Qinming;HU Jiarui(Business School,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《信息与控制》
CSCD
北大核心
2024年第2期211-225,共15页
Information and Control
基金
国家自然科学基金项目(71632008,71840003)
上海市自然科学基金项目(19ZR1435600)
教育部人文社会科学研究规划基金项目(20YJAZH068)
上海市2021年度“科技创新行动计划”宝山转型发展科技专项(215QBS01404)
上海理工大学科技发展项目(2020KJFZ038)
2023年上海市大学生创新创业训练计划(SH2023072)
关键词
多源传感器数据
注意力机制
标签平滑正则化
故障诊断
寿命预测
multi-source sensor data
attention mechanism
label smoothing regularization
fault diagnosis
life prediction
