摘要
通过状态监测进行轴承故障报警,能有效避免设备灾难性事故的发生。基于数据时序特征重构的故障检测法由于仅采用正常数据进行训练,能有效避免故障数据不足而导致的模型检测精度下降。然而,此类方法的故障阈值确定依赖于大量的历史数据,且对检测精度有着极大的影响。为此,提出基于深度SVDD-CVAE的轴承自适应阈值故障检测方法。针对时序信号特征增强提取构建ConvLSTM作为基础单元的CVAE特征压缩提取框架,有效提取轴承故障微弱特征;结合SVDD自适应学习特征空间超球面,实现故障检测阈值的自适应确定;最后,通过全局误差损失反向传播对深度SVDD-CVAE框架进行迭代优化。实验结果表明:所提出的方法能有效提取轴承微弱故障特征、自适应确定阈值,并在IMS轴承数据集上取得97.7%的检测准确率。
Bearings fault alarms by condition monitoring can effectively avoids catastrophic accidents.The fault detection method based on data time series feature reconstruction can avoid the degradation of model accuracy caused by insufficient fault data because only normal data is used for training.However,the fault threshold determination in such methods depends on a large amount of historical data,which has a great impact on the detection accuracy.Therefore,a bearing adaptive threshold fault detection method was proposed based on deep SVDD-CVAE.A CVAE feature compression extraction framework was constructed with ConvLSTM as the basic unit for enhancement extraction of time series signals,so as to extract the weak features of bearing faults.The SVDD was combined to adaptively learn the feature space hypersphere to realize the adaptive determination of the fault detection threshold.Finally,the deep SVDD-CVAE framework was iteratively optimized by global error loss backpropagation.The experimental results show that the proposed method can effectively extract weak bearing fault features and adaptively determine the threshold value with a detection accuracy of 97.7%on IMS bearing dataset.
作者
刘云飞
张楷
菅紫倩
郑庆
张越宏
袁昭成
焦子一
丁国富
LIU Yunfei;ZHANG Kai;JIAN Ziqian;ZHENG Qing;ZHANG Yuehong;YUAN Zhaocheng;JIAO Ziyi;DING Guofu(School of Mechanical Engineering,Southwest Jiaotong University,Chengdu Sichuan 610031,China;Tangshan Institute,Southwest Jiaotong University,Tangshan Hebei 063003,China;Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province,Southwest Jiaotong University,Chengdu Sichuan 610031,China;Chengdu Institute of Special Equipment Inspection and Testing,Chengdu Sichuan 610299,China)
出处
《机床与液压》
北大核心
2024年第6期177-183,195,共8页
Machine Tool & Hydraulics
基金
国家自然科学基金青年科学基金项目(52205130)
中央高校基本科研业务费专项资金项目(2682022CX006)。
关键词
轴承
故障检测
深度学习
自适应阈值
变分自编码
bearings
fault detection
deep learning
adaptive threshold
variational autoencoder
