信息与控制2024，Vol.53Issue(2)：211-225,15.DOI:10.13976/j.cnki.xk.2023.3056
多源传感器数据下基于注意力机制与长短期记忆网络的轴承故障诊断与寿命预测
Bearing Fault Diagnosis and Life Prediction Based on Attention Mechanism and Long Short-term Memory Network under Multi-source Sensor Data
摘要
Abstract
To solve the problem of the poor diagnosis effect of rolling bearing faults in noisy environ-ments,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 atten-tion(CoordAtt)which is capable of considering the relationship between channels and position in-formation 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 re-sults of fault categories are output.We compare the diagnostic accuracy of the model under differ-ent 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 environ-ments.The validity of the CoordAtt-LSTM model in life prediction is verified by experimental re-sults on the C-MAPSS dataset.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Key words
multi-source sensor data/attention mechanism/label smoothing regulariza-tion/fault diagnosis/life prediction分类
机械制造引用本文复制引用
陈翔,刘勤明,胡家瑞..多源传感器数据下基于注意力机制与长短期记忆网络的轴承故障诊断与寿命预测[J].信息与控制,2024,53(2):211-225,15.基金项目
国家自然科学基金项目(71632008,71840003) （71632008,71840003）
上海市自然科学基金项目(19ZR1435600) （19ZR1435600）
教育部人文社会科学研究规划基金项目(20YJAZH068) （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) （215QBS01404）
上海理工大学科技发展项目(2020KJFZ038) （2020KJFZ038）
2023年上海市大学生创新创业训练计划(SH2023072) （SH2023072）
