Methods to identify time series abnormalities and predicting issues caused by component failures

0Citations
Citations of this article
11Readers
Mendeley users who have this article in their library.

Abstract

Anomaly detection is a crucial analysis topic in the field of Industry 4.0 data mining as well as knowing what is the probability that a specific machine to go down due to a failure of a component in the next time interval. In this article, we used time series data collected from machines, from both classes - time series data which leads up to the failures of machines as well as data from healthy operational periods of the machine. We used telemetry data, error logs from still operational components, maintenance records comprising historical breakdowns and replacement component to build and compare several different models. The validation of the proposed methods was made by comparing the actual failures in the test data with the predicted component failures over the test data.

Cite

CITATION STYLE

APA

Deac, C. N., Deac, G. C., Chiscop, F., & Popa, C. L. (2019). Methods to identify time series abnormalities and predicting issues caused by component failures. In MATEC Web of Conferences (Vol. 290). EDP Sciences. https://doi.org/10.1051/matecconf/201929002002

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free