基于多源域迁移学习的带式输送机剩余寿命预测方法OA北大核心CSTPCD
Residual Life Prediction Method of Belt Conveyors Based on MDT Learning
煤矿开采过程中,带式输送机运行环境恶劣、工况复杂,致使获得的传感监测数据量有限且存在大量噪声干扰,严重限制了其剩余寿命预测的准确度.针对该问题,提出了一种多源域迁移学习剩余寿命预测方法,充分利用煤矿运输过程中积累的带式输送机多工况数据,以达到准确预测其关键零部件托辊轴承剩余寿命的目的.首先构建集成多尺度卷积神经网络和双向门控循环单元(MCNN-BiGRU)的设备退化特征提取模型,对单工况数据进行特征提取挖掘,并使用PSO算法确定模型超参数.在此基…查看全部>>
In the coal mining processes,the operating environments of the belt conveyors were harsh and the working conditions were complex.These resulted in a limited amount of sensor monito-ring data and a large amount of noise interference,which seriously limited the accuracy of the residual life prediction.Aiming at this problem,a MDT learning residual life prediction method was proposed.To predict the residual life of key component roller bearings accurately,multi…查看全部>>
高新勤;杨学琦;郑海洋
西安理工大学机械与精密仪器工程学院,西安,710048西安理工大学机械与精密仪器工程学院,西安,710048西安理工大学机械与精密仪器工程学院,西安,710048
矿山工程
带式输送机剩余寿命预测多工况特征提取多源域迁移学习
belt conveyorresidual life predictionmultiple working conditionfeature extrac-tionmulti-source domain transfer(MDT)learning
《中国机械工程》 2024 (8)
1435-1448,14
国家自然科学基金(51575443)陕西省教育厅重点科学研究计划(20JY047)
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