基于云-边协同变分自编码神经网络的设备故障检测方法

An equipment fault detection method based on cloud-edge collaboration variational autoencoder neural network

针对机电设备故障数据整体趋势和多阈值点实际应用,提出了一种基于云-边协同的变分自编码门控循环神经网络VAE-GRU的设备故障检测方法。构建了基于云-边协同的机电设备故障检测系统架构,终端设备层、边缘节点层、云中心层,云中心和边缘节点之间通过协同的方式对机电设备进行故障检测。设计了VAE-GRU模型,通过VAE编码器对输入数据进行采样,利用GRU捕捉时序数据的长期相关性。设计了动态阈值选择算法确定故障检测阈值,针对不同数据集可自动选择最优阈值,提高故障检测精度。实验结果表明,提出的基于云-边协同VAE-GRU设备故障检测方法提高了设备故障检测准确性,降低了处理时延,能保证机电设备稳定运行。

In response to the overall trend and practical application of multi-threshold points in electromechanical equipment fault data detection,this paper proposes a cloud-edge collaborative electromechanical equipment fault detection method based on a variational autoencoder with gated recurrent unit(VAE-GRU).A cloud-edge collaborative electromechanical equipment fault detection architecture is structed,including a terminal equipment layer,an edge node layer,and a cloud center layer,in which electromechanical equipment is detected for faults through collaboration between the cloud center and edge nodes.The VAE-GRU model is design,where the input data is sampled by VAE,and GRU is used to capture the long-term correlation of the timing data.A dynamic threshold selection algorithm is used to calculate the fault detection threshold,that can automatically select the optimal threshold for different data sets to improve fault detection accuracy.Experimental results show that the proposed method improves the accuracy of electromechanical equipment fault detection while reducing latency,ensuring the normal and stable operation of electromechanical equipment.