Detecting anomalies in production quality data using a method based on The Chi-Square Test Statistic

M. Mayr, J. Himmelbauer. Detecting anomalies in production quality data using a method based on The Chi-Square Test Statistic. pages 348-363, DOI https://doi.org/10.1007/978-3-030-59065-9_27, 9, 2020.

Autoren
  • Michael Mayr
  • Johannes Himmelbauer
Editoren
  • Min Song
  • Il-Yeol Song
  • Gabriele Kotsis
  • A Min Tjoa
  • Ismail Khalil
BuchDaWaK 2020: Big Data Analytics and Knowledge Discovery
TypIn Konferenzband
VerlagSpringer
SerieLecture Notes in Computer Science
DOIhttps://doi.org/10.1007/978-3-030-59065-9_27
ISBN978-3-030-59064-2
Monat9
Jahr2020
Seiten348-363
Abstract

This paper describes the capability of the Chi-Square test statistic at detecting outliers in production-quality data. The goal is automated detection and evaluation of statistical anomalies for a large number of time series in the production-quality context. The investigated time series are the temporal course of sensor failure rates in relation to particular aspects (e.g. type of failure, information about products, the production process, or measuring sites). By means of an industrial use case, we show why in this setting our chosen approach is superior to standard methods for statistical outlier detection.