machine_temperature_system_failure

Temperature sensor data of an internal component of a large, industrial mahcine.
The first anomaly is a planned shutdown of the machine. The second anomaly is
difficult to detect and directly led to the third anomaly, a catastrophic failure
of the machine.

datasets

Implements a set of online fault detectors for time-series, called: PEWMA see M. Carter
et al. (2012) <doi:10.1109/SSP.2012.6319708>, SD-EWMA and TSSD-EWMA see H. Raza et al.
(2015) <doi:10.1016/j.patcog.2014.07.028>, KNN-CAD see E. Burnaev et al. (2016)
<arXiv:1608.04585>, KNN-LDCD see V. Ishimtsev et al. (2017) <arXiv:1706.03412> and
CAD-OSE see M. Smirnov (2018) <https://github.com/smirmik/CAD>. The first three
algorithms belong to prediction-based techniques and the last three belong to
window-based techniques. In addition, the SD-EWMA and PEWMA algorithms are algorithms
designed to work in stationary environments, while the other four
are algorithms designed to work in non-stationary environments.

Alai<c3><b1>e Iturria

otsad

Online Time Series Anomaly Detectors

Jacinto Carrasco

Francisco Herrera

Santiago Charramendieta

Karmele Intxausti

machine_temperature_system_failure function

A data frame with three variables: <code>timestamp</code>, <code>value</code>,
 <code>is.real.anomaly</code>.
For further details, see <a href="https://github.com/numenta/NAB/blob/master/data/README.md">https://github.com/numenta/NAB/blob/master/data/README.md</a>

Format

machine_temperature_system_failure. — machine_temperature_system_failure

A data frame with three variables: <code>timestamp</code>, <code>value</code>,
 <code>is.real.anomaly</code>.
For further details, see <a href='https://github.com/numenta/NAB/blob/master/data/README.md'>https://github.com/numenta/NAB/blob/master/data/README.md</a>

machine_temperature_system_failure: machine_temperature_system_failure.

Description

Usage

Arguments

Format