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Deep learning on temporal-spectral data for anomaly detection
Author(s): King Ma; Henry Leung; Ehsan Jalilian; Daniel Huang
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Paper Abstract

Detecting anomalies is important for continuous monitoring of sensor systems. One significant challenge is to use sensor data and autonomously detect changes that cause different conditions to occur. Using deep learning methods, we are able to monitor and detect changes as a result of some disturbance in the system. We utilize deep neural networks for sequence analysis of time series. We use a multi-step method for anomaly detection. We train the network to learn spectral and temporal features from the acoustic time series. We test our method using fiber-optic acoustic data from a pipeline.

Paper Details

Date Published: 4 May 2017
PDF: 10 pages
Proc. SPIE 10190, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII, 101900D (4 May 2017); doi: 10.1117/12.2262037
Show Author Affiliations
King Ma, Univ. of Calgary (Canada)
Henry Leung, Univ. of Calgary (Canada)
Ehsan Jalilian, Hifi Engineering Inc. (Canada)
Daniel Huang, Hifi Engineering Inc. (Canada)


Published in SPIE Proceedings Vol. 10190:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII
Tien Pham; Michael A. Kolodny, Editor(s)

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