Abstract: The increasingly large server clusters of state grid corporation generate a large amount of production operation data, and real-time analysis of the massive monitoring data generated by various devices and systems has become a new challenge in power IT operation and maintenance work. As a key technology of intelligent grid information operation and maintenance work, anomaly detection technology can effectively detect operation and maintenance faults and provide timely alarms to avoid damage to sensitive equipment. Currently, some traditional anomaly detection methods have few types of anomalies and low precision, resulting in delayed fault detection. To address this challenge, this article proposes a multi-dimensional time series anomaly detection method based on capsule networks, NNCapsNet. Firstly, the unsupervised algorithm is applied in combination with expert knowledge to preprocess and label the performance monitoring data of grid marketing business application servers. Secondly, the capsule network is introduced for classification and anomaly detection. Experimental results obtained through five-fold cross-validation show that NNCapsNet achieves an average classification accuracy of 91.21% on a dataset containing 15 types of anomalies. At the same time, compared with four benchmark models on the  dataset containing 20 000 monitoring data, NNCapsNet achieves good results in key evaluation indicators. 

Key words: monitoring data, power IT operation and maintenance, abnormal detection, capsule network, multi-dimensional time series analysis, unsupervised algorithm