JACoW logo

Journals of Accelerator Conferences Website (JACoW)

JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.


RIS citation export for TU1BCO02: Integrating System Knowledge in Unsupervised Anomaly Detection Algorithms for Simulation-Based Failure Prediction of Electronic Circuits

TY  - CONF
AU  - Waldhauser, F.
AU  - Boukabache, H.
AU  - Dazer, M.
AU  - Perrin, D.
AU  - Roesler, S.
ED  - Schaa, Volker RW
ED  - Götz, Andy
ED  - Venter, Johan
ED  - White, Karen
ED  - Robichon, Marie
ED  - Rowland, Vivienne
TI  - Integrating System Knowledge in Unsupervised Anomaly Detection Algorithms for Simulation-Based Failure Prediction of Electronic Circuits
J2  - Proc. of ICALEPCS2023, Cape Town, South Africa, 09-13 October 2023
CY  - Cape Town, South Africa
T2  - International Conference on Accelerator and Large Experimental Physics Control Systems
T3  - 19
LA  - english
AB  - Machine learning algorithms enable failure prediction of large-scale, distributed systems using historical time-series datasets. Although unsupervised learning algorithms represent a possibility to detect an evolving variety of anomalies, they do not provide links between detected data events and system failures. Additional system knowledge is required for machine learning algorithms to determine the nature of detected anomalies, which may represent either healthy system behavior or failure precursors. However, knowledge on failure behavior is expensive to obtain and might only be available upon pre-selection of anomalous system states using unsupervised algorithms. Moreover, system knowledge obtained from evaluation of system states needs to be appropriately provided to the algorithms to enable performance improvements. In this paper, we will present an approach to efficiently configure the integration of system knowledge into unsupervised anomaly detection algorithms for failure prediction. The methodology is based on simulations of failure modes of electronic circuits. Triggering system failures based on synthetically generated failure behaviors enables analysis of the detectability of failures and generation of different types of datasets containing system knowledge. In this way, the requirements for type and extend of system knowledge from different sources can be determined, and suitable algorithms allowing the integration of additional data can be identified. 
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 249
EP  - 256
KW  - simulation
KW  - ISOL
KW  - electron
KW  - monitoring
KW  - radiation
DA  - 2024/02
PY  - 2024
SN  - 2226-0358
SN  - 978-3-95450-238-7
DO  - doi:10.18429/JACoW-ICALEPCS2023-TU1BCO02
UR  - https://jacow.org/icalepcs2023/papers/tu1bco02.pdf
ER  -