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.
King Ma, Henry Leung, Ehsan Jalilian, and Daniel Huang, "Deep learning on temporal-spectral data for anomaly detection," Proc. SPIE 10190, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII, 101900D (Presented at SPIE Defense + Security: April 11, 2017; Published: 4 May 2017); https://doi.org/10.1117/12.2262037.
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