A Hidden Anomaly Detection Method for Industrial Internet of Things Based on Multiscale Features
In order to improve the detection effect of hidden anomalies in the industrial inter-net of things(IoT),a multi-scale feature based detection method for hidden anomalies in the in-dustrial IoT was designed.Based on the characteristics of the industrial IoT,multiple data fea-ture selection criteria are established and the final feature selection results are calculated.On this basis,multi-scale features are extracted and the weight values of different features are calculated.Furthermore,the reconstruction error and classification results of the features are calculated,and the design of an industrial IoT data classifier is completed.The detection of hidden anomalies in the industrial IoT is achieved by calculating the detection results and weight values at different scales.Experimental results have shown that the designed method has a low missed detection rate and good detection performance in practical applications.
multi-scale featureindustrial internet of thingshidden anomaliesanomaly de-tection method
NETL
NSTL
万方数据
