基于深度SVDD-CVAE的轴承自适应阈值故障检测

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 Detection Based on Deep SVDD-CVAE with Adaptive Threshold

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.