文章基本信息

标题：Methods to identify time series abnormalities and predicting issues caused by component failures
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作者：Crina Narcisa Deac ; Gicu Calin Deac ; Florina Chiscop 等
期刊名称：MATEC Web of Conferences
电子版ISSN：2261-236X
出版年度：2019
卷号：290
页码：1-10
DOI：10.1051/matecconf/201929002002
语种：English
出版社：EDP Sciences
摘要：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.