Sensor Virtualization for Anomaly Detection of Turbo-Machinery Sensors—An Industrial Application †
Abstract
:1. Introduction
1.1. Problem Statement
- Early detection is required: only a prompt action allows to avoid the high potential costs of unnecessary shutdowns.
- Up to few thousand sensors need to be checked daily.
- Recall is key: anomalies detected by the tool will be checked by operators and vice versa, where if no alert is given, the anomaly may remain undetected.
- Precision should be kept under control: too many false positives would increase the set of signals to be checked and may invalidate the benefits.
1.2. Related Works
2. The Dataset
3. The Model
3.1. Selection of Input Sensors
3.2. Selection of Lookback Window
3.3. Model Training
3.4. Inference Logic
4. Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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ME | MAPE | P90 | |
---|---|---|---|
Training set | 0.12 | 0.61 | 5.06 |
Validation set (non-anomalous samples only) | 1.45 | 1.61 | 5.97 |
Test set (non-anomalous samples only) | 1.89 | 0.65 | 6.52 |
Precision | Recall | |
---|---|---|
Full test set | 96% | 100% |
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Shetty, S.; Gori, V.; Bagni, G.; Veneri, G. Sensor Virtualization for Anomaly Detection of Turbo-Machinery Sensors—An Industrial Application. Eng. Proc. 2023, 39, 96. https://doi.org/10.3390/engproc2023039096
Shetty S, Gori V, Bagni G, Veneri G. Sensor Virtualization for Anomaly Detection of Turbo-Machinery Sensors—An Industrial Application. Engineering Proceedings. 2023; 39(1):96. https://doi.org/10.3390/engproc2023039096
Chicago/Turabian StyleShetty, Sachin, Valentina Gori, Gianni Bagni, and Giacomo Veneri. 2023. "Sensor Virtualization for Anomaly Detection of Turbo-Machinery Sensors—An Industrial Application" Engineering Proceedings 39, no. 1: 96. https://doi.org/10.3390/engproc2023039096