Sensor Virtualization for Anomaly Detection of Turbo-Machinery Sensors—An Industrial Application
We apply a Granger causality and auto-correlation analysis to train a recurrent neural network (RNN) that acts as a virtual sensor model. These models can be used to check the status of several hundreds of sensors during turbo-machinery units’ operation. Checking the health of each sensor is a time-...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/39/1/96 |
| Summary: | We apply a Granger causality and auto-correlation analysis to train a recurrent neural network (RNN) that acts as a virtual sensor model. These models can be used to check the status of several hundreds of sensors during turbo-machinery units’ operation. Checking the health of each sensor is a time-consuming activity. Training a supervised algorithm is not feasible because we do not know all the failure modes that the sensors can undergo. We use a semi-supervised approach and train an RNN (LSTM) on non-anomalous data to build a virtual sensor using other sensors as regressors. We use the Granger causality test to identify the set of input sensors for a given target sensor. Moreover, we look at the auto-correlation function (ACF) to understand the temporal dependency in data. We then compare the predicted signal vs. the real one to raise (in case) an anomaly in real time. Results report 96% precision and 100% recall. |
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| ISSN: | 2673-4591 |