摘要
为了能够准确预测设备未来可能发生的具体故障模式,提出一种基于双深度学习模型的设备故障趋势预测方法。采用小波包变换(Wavelet Packet Transform,WPT)提取震动传感器信号的时频域特征;通过构建的GAM-BiLSTM-RF模型对设备未来的运行趋势进行预测,并获取设备未来运行趋势所对应的时间序列数据;通过构建的GAM-BiLSTM-ET模型对设备未来的运行趋势数据进行深层特征提取,并依据提取的特征判断设备故障的类型和严重程度。在公开的设备故障数据集(IMS和XJTU-SY)上的实验结果表明,提出的两步预测法可以精确预测设备未来的运行趋势,并有效提高了设备故障预测的准确率。同时,与一些基线模型进行了性能比较,进一步验证了提出的设备故障预测方法是有效且稳定的。
To accurately predict the specific failure modes that may occur in the future of equipment,a device failure trend prediction method based on double deep learning model is proposed.Wavelet packet transform(WPT)was used to extract the time-frequency domain features of the vibration sensor signal.The designed GAM-BiLSTM-RF model was used to predict the future operation trend of the equipment,and obtain the time series data corresponding to the future operation trend.The designed GAM-BiLSTM-ET model was used to extract the deep features of the future operation trend data of equipment,and judge the type and severity of equipment faults according to the extracted features.The experimental results on the open equipment fault datasets(IMS and XJTU-SY)show that the proposed two-step prediction method can accurately predict the future operation trend of equipment,and effectively improve the accuracy of equipment fault prediction.In addition,the performance comparison with some baseline models further verifies that the proposed method is effective and stable.
作者
王欢欢
智珊珊
李代祎
钱慎一
Wang Huanhuan;Zhi Shanshan;Li Daiyi;Qian Shenyi(Henan Mechanical and Electrical Vocational College,Zhengzhou 450000,Henan,China;College of Computer and Communication Engineering,Zhengzhou University of Light Industry,Zhengzhou 450000,Henan,China)
出处
《计算机应用与软件》
北大核心
2026年第2期214-222,共9页
Computer Applications and Software
基金
河南省重大科技专项项目(201300210020)
基于人工智能等新技术的职业院校教师评价与培养培训体系的构建与实践(豫教[2021]57929)。
关键词
故障预测
深度学习
小波包变换
时间序列
特征提取
Fault prediction
Deep learning
Wavelet packet transform
Time series
Extraction features
