Real-time anomaly data detection method based on mixed kernel function PSO-SVR

Abstract

To avoid the influence of abnormal data of micro-plant factory sensors on the control system, an abnormal data detection method based on mixed kernel function particle swarm optimization (PSO)-support vector regression (SVR) is proposed. First, the mixed kernel function constructed by the polynomial kernel and radial basis kernel is used as the kernel function of SVR, and PSO is introduced to optimize the hyperparameters of the SVR model and establish a prediction model. Then, the model completes a step-by-step prediction based on the data in the sliding window and calculates confidence intervals. Finally, the data are judged to be abnormal based on whether the measured values exceed the confidence interval, and the abnormal values are replaced with the predicted values. The results show that the mean absolute percentage error of the model is 0.0063, the accuracy is 99.63%, the prediction accuracy and detection accuracy are better than the comparison model, and the abnormal data in the sensor data flow can be effectively detected and processed.