Edwin Lughofer
On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks.
Lughofer, Edwin; Zavoianu, Alexandru-Ciprian; Pollak, Robert; Pratama, Mahardhika; Meyer-Heye, Pauline; Z�rrer, Helmut; Eitzinger, Christian; Radauer, Thomas
Authors
Dr Ciprian Zavoianu c.zavoianu@rgu.ac.uk
Research Programme Lead
Robert Pollak
Mahardhika Pratama
Pauline Meyer-Heye
Helmut Z�rrer
Christian Eitzinger
Thomas Radauer
Abstract
Anomaly detection in todays industrial environments is an ambitious challenge to detect possible faults/problems which may turn into severe waste during production, defects, or systems components damage, at an early stage. Data-driven anomaly detection in multi-sensor networks rely on models which are extracted from multi-sensor measurements and which characterize the anomaly-free reference situation. Therefore, significant deviations to these models indicate potential anomalies. In this paper, we propose a new approach which is based on causal relation networks (CRNs) that represent the inner causes and effects between sensor channels (or sensor nodes) in form of partial sub-relations, and evaluate its functionality and performance on two distinct production phases within a micro-fluidic chip manufacturing scenario. The partial relations are modeled by non-linear (fuzzy) regression models for characterizing the (local) degree of influences of the single causes on the effects. An advanced analysis of the multi-variate residual signals, obtained from the partial relations in the CRNs, is conducted. It employs independent component analysis (ICA) to characterize hidden structures in the fused residuals through independent components (latent variables) as obtained through the demixing matrix. A significant change in the energy content of latent variables, detected through automated control limits, indicates an anomaly. Suppression of possible noise content in residuals—to decrease the likelihood of false alarms—is achieved by performing the residual analysis solely on the dominant parts of the demixing matrix. Our approach could detect anomalies in the process which caused bad quality chips (with the occurrence of malfunctions) with negligible delay based on the process data recorded by multiple sensors in two production phases: injection molding and bonding, which are independently carried out with completely different process parameter settings and on different machines (hence, can be seen as two distinct use cases). Our approach furthermore i.) produced lower false alarm rates than several related and well-known state-of-the-art methods for (unsupervised) anomaly detection, and ii.) also caused much lower parametrization efforts (in fact, none at all). Both aspects are essential for the useability of an anomaly detection approach.
Citation
LUGHOFER, E., ZAVOIANU, A.-C., POLLAK, R., PRATAMA, M., MEYER-HEYE, P., ZÖRRER, H., EITZINGER, C. and RADAUER, T. 2020. On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks. Information sciences [online], 537, 425-451. Available from: https://doi.org/10.1016/j.ins.2020.06.034
Journal Article Type | Article |
---|---|
Acceptance Date | Jun 11, 2020 |
Online Publication Date | Jun 20, 2020 |
Publication Date | Oct 31, 2020 |
Deposit Date | Jul 9, 2020 |
Publicly Available Date | Jun 21, 2021 |
Journal | Information sciences |
Print ISSN | 0020-0255 |
Electronic ISSN | 1872-6291 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 537 |
Pages | 425-451 |
DOI | https://doi.org/10.1016/j.ins.2020.06.034 |
Keywords | On-line anomaly detection; Causal relation networks; Advanced multi-variate residual analysis; Dominant parts of independent component analysis; Automated control limits; On-line production systems |
Public URL | https://rgu-repository.worktribe.com/output/943823 |
Files
LUGHOFER 2020 On-line anomaly detection with advanced
(3.8 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/
You might also like
Optimising linear regression for modelling the dynamic thermal behaviour of electrical machines using NSGA-II, NSGA-III and MOEA/D.
(2023)
Presentation / Conference Contribution
On discovering optimal trade-offs when introducing new routes in existing multi-modal public transport systems.
(2022)
Presentation / Conference Contribution
Lightweight interpolation-based surrogate modelling for multi-objective continuous optimisation.
(2022)
Presentation / Conference Contribution
Downloadable Citations
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search