The ever-lasting challenge of detecting and mitigating failures in computer networks has
become more essential than ever; especially with the enormous number of smart devices …

传统的阈值判定方法难以准确检测输变电设备的状态异常, 该文提出一种基于时间序列分析和无
监督学习等大数据分析的异常检测方法, 从数据演化过程, 数据关联的全新角度实现异常检测 …

Detecting incident anomalies within temporal data-time series becomes useful in a variety of
applications. In this paper, anomalies in time series are divided into two categories, namely …

In this survey paper, we report the results of a comprehensive study involving the application
of dynamic self-organizing neural networks (SONNs) to the problem of novelty detection in …

Anomaly detection in spatial time series (spatiotemporal data) is a challenging problem with
numerous potential applications. A comprehensive anomaly detection approach not only …

Currently, the cleaning process for power equipment monitoring data is cumbersome and
often leads to loss of information. To address these problems, a data cleaning method based …

This paper explores a novel idea for power equipment monitoring and finds that random
matrix theory is suitable for modeling the massive data sets in this situation. Big data …

High‐quality condition monitoring data can provide vital information on power equipment
condition assessment and fault diagnosis. However, data quality is difficult to guarantee …

The failure rate of dynamic systems with random parameters is time-varying even for linear
systems excited by a stationary random input. In this paper, we propose a simulation-based …

Anomaly detection in time series has become a widespread problem in the areas such as
intrusion detection and industrial process monitoring. Major challenges in anomaly detection …