UNSUPERVISED PROBABILISTIC ANOMALY DETECTION OVER NOMINAL SUBSYSTEM EVENTS THROUGH A HIERARCHICAL VARIATIONAL AUTOENCODER
This work develops a versatile approach to discover anomalies in operational data for nominal (i.e., non-parametric) subsystem event signals using unsupervised Deep Learning techniques. Firstly, it builds a neural convolutional framework to extract both intrasubsystem and intersubsystem patterns. T...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
The Prognostics and Health Management Society
2023-01-01
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| Series: | International Journal of Prognostics and Health Management |
| Subjects: |