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Fiber-Optic Acoustic-Based Disturbance Prediction in Pipelines Using Deep Learning
- Source :
- IEEE Sensors Letters. 1:1-4
- Publication Year :
- 2017
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2017.
-
Abstract
- The problem of detecting nonstationary disturbances in a pipeline is demonstrated using a predictive framework based on high spatial resolution fiber-optic acoustic sensors. We show that the root-mean-square (RMS) acoustic power is related to flow and density changes in the fluid. However, in practice, fluid parameters are not known at the resolution of the acoustics. In an experimental study, we trained long-short-term memory (LSTM) networks to exploit hidden patterns in an acoustic time series to predict the RMS acoustic power. We found LSTM perform efficiently and shows improvement over baseline neural network predictor, and its strength lies in discriminating sequential order from spatial input data. The system is verified on 25 m resolution fiber-optic acoustic data. Results show promise in predicting anomalous disturbances despite unknown pipe and fluid parameters..
- Subjects :
- Imagination
Optical fiber
Artificial neural network
business.industry
Computer science
Pipeline (computing)
Deep learning
media_common.quotation_subject
Acoustics
Flow (psychology)
02 engineering and technology
021001 nanoscience & nanotechnology
Sound power
01 natural sciences
law.invention
010309 optics
Pipeline transport
law
0103 physical sciences
Artificial intelligence
Electrical and Electronic Engineering
0210 nano-technology
business
Instrumentation
media_common
Subjects
Details
- ISSN :
- 24751472
- Volume :
- 1
- Database :
- OpenAIRE
- Journal :
- IEEE Sensors Letters
- Accession number :
- edsair.doi...........90e80843aaedf91b0b71a2c74cfd578c