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Time Series Anomaly Detection using Convolutional Neural Networks in the Manufacturing Process

Authors:


Publication Type:

Conference/Workshop Paper

Venue:

The 5th IEEE International Conference on Artificial Intelligence Testing (AITEST 2023)


Abstract

In the semi-automated software testing process, results are judged as "pass" or "fail" simply based on fixed thresholds derived from the various test case descriptions. Re-testing and manual analysis of the "failed" readings are labor intensive and cost the resources of the testing machine. Typically, in production, the upper and lower acceptance threshold is set based on regulator standards and an empirical value from subject matter experts. Setting too tight or too loose fixed thresholds in production is one of the major causes of test data dropping during the testing process. In this paper, we propose a solution to improve the semi-automated software testing process by detecting divergences in executed data and alerting when a deviant input falls outside a data-driven threshold. This avoids dropping test data and improves testing efficiency and accuracy. The proposed solution is based on classification using convolutional neural networks and prediction modeling using linear, Ridge, Lasso, and XGBoost. Furthermore, transfer learning in a highly correlated use case is evaluated resulting in promising results. Empirical evaluation at a leading telecom company shows the effectiveness of our approach.

Bibtex

@inproceedings{Landin6693,
author = {Cristina Landin and Jie Liu and Sahar Tahvili},
title = {Time Series Anomaly Detection using Convolutional Neural Networks in the Manufacturing Process},
month = {May},
year = {2023},
booktitle = {The 5th IEEE International Conference on Artificial Intelligence Testing (AITEST 2023) },
url = {http://www.es.mdu.se/publications/6693-}
}