Research on Detection Method of Sensor Anomaly Data Based on Improved VAE
Gas sensors often produce abnormal time series data due to complex industrial environment during sampling.The traditional a-nomaly detection of time series mainly adopts the model prediction method,but does not consider the imbalance of time series data.Therefore,a detection method based on improved VAE model is proposed.Firstly,a large amount of normal time series data is combined with a small amount of abnormal time series data which is difficult to label to build an unbalanced data set.Secondly,based on the traditional VAE model,unsupervised learning is adopted to enhance the adaptive anomaly detection ability of the network model by intro-ducing dynamic threshold method in anomaly detection classification.Finally,a combined loss function for timing anomaly detection is proposed to further improve the performance of network parameter optimization by integrating cross-entropy loss function and KL divergence.The experimental results show that the proposed method is better than the original method in terms of the accuracy rate,recall rate and F1 value.It has a good application in sensor anomaly data detection.
万方数据
维普
