Fault prediction is an important topic for the industry as, by providing effective methods for
predictive maintenance, allows companies to perform important time and cost savings. In …

Detecting the anomalies timely in the condition monitoring data, which are highly relevant to
the potential system faults, has become a research focus in many domains. Among the …

Anomaly detection tries to find out the data that disobeys the rule of majority data or
expected patterns. The traditional hierarchical clustering algorithms have been adopted to …

Time-series anomaly detection receives increasing research interest given the growing
number of data-rich application domains. Recent additions to anomaly detection methods in …

For a spacecraft, its power system is vital to its normal operation and capacity to complete
flight missions. The storage battery is an essential component of a power system. As a …

This paper addresses a multi-label predictive fault classification problem for
multidimensional time-series data. While fault (event) detection problems have been …

We present an unsupervised method to detect anomalous time series among a collection of
time series. To do so, we extend traditional Kernel Density Estimation for estimating …

This paper presents a novel, unsupervised approach to detecting anomalies at the collective
level. The method probabilistically aggregates the contribution of the individual anomalies in …

The article proposes a novel two-stage network traffic anomaly detection method for the
railway transportation critical infrastructure monitored using wireless sensor networks …

In this paper we describe how Bayesian Principal Anomaly Detection (BPAD) can be used
for detecting long and short term trends and anomalies in geographically tagged alarm data …