Engineering

Materials science

Optical time-domain reflectometer

Optical fiber

Telecommunications

Computer science

Fiber optic sensor

Acoustics

Mechanical engineering

Physics

Supervised learning

Pipeline (software)

Optics

Image (mathematics)

Rayleigh scattering

Artificial intelligence

Piping

Time domain

Programming language

Polarization-maintaining optical fiber

Distributed acoustic sensing

Artificial neural network

Reflectometry

Noise (video)

Computer vision

Optics Express

This paper presents an integrated technical framework to protect pipelines against both malicious intrusions and piping degradation using a distributed fiber sensing technology and artificial intelligence. A distributed acoustic sensing (DAS) system based on phase-sensitive optical time-domain reflectometry (φ-OTDR) was used to detect acoustic wave propagation and scattering along pipeline structures consisting of straight piping and sharp bend elbow. Signal to noise ratio of the DAS system was enhanced by femtosecond induced artificial Rayleigh scattering centers. Data harnessed by the DAS system were analyzed by neural network-based machine learning algorithms. The system identified with over 85% accuracy in various external impact events, and over 94% accuracy for defect identification through supervised learning and 71% accuracy through unsupervised learning.

Distributed fiber sensor and machine learning data analytics for pipeline protection against extrinsic intrusions and intrinsic corrosions