Recent Advances in Machine Learning for Fiber Optic Sensor Applications

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  • Abhishek Venketeswaran
    Research & Innovation Center National Energy Technology Laboratory 626 Cochrans Mill Road Pittsburgh PA 15236 USA
  • Nageswara Lalam
    Research & Innovation Center National Energy Technology Laboratory 626 Cochrans Mill Road Pittsburgh PA 15236 USA
  • Jeffrey Wuenschell
    Research & Innovation Center National Energy Technology Laboratory 626 Cochrans Mill Road Pittsburgh PA 15236 USA
  • P. R. Ohodnicki
    Department of Mechanical Engineering and Materials Science University of Pittsburgh 808 Benedum Hall, 3700 O’Hara Street Pittsburgh PA 15261 USA
  • Mudabbir Badar
    Research & Innovation Center National Energy Technology Laboratory 626 Cochrans Mill Road Pittsburgh PA 15236 USA
  • Kevin P. Chen
    Department of Electrical and Computer Engineering University of Pittsburgh 1136 Benedum Hall, 3700 O’Hara Street Pittsburgh PA 15261 USA
  • Ping Lu
    Research & Innovation Center National Energy Technology Laboratory 626 Cochrans Mill Road Pittsburgh PA 15236 USA
  • Yuhua Duan
    Research & Innovation Center National Energy Technology Laboratory 626 Cochrans Mill Road Pittsburgh PA 15236 USA
  • Benjamin Chorpening
    Research & Innovation Center National Energy Technology Laboratory 3610 Collins Ferry Road Morgantown WV 26505 USA
  • Michael Buric
    Research & Innovation Center National Energy Technology Laboratory 3610 Collins Ferry Road Morgantown WV 26505 USA

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<jats:sec><jats:label/><jats:p>Over the last three decades, fiber optic sensors (FOS) have gained a lot of attention for their wide range of monitoring applications across many industries, including aerospace, defense, security, civil engineering, and energy. FOS technologies hold great promise to form the backbone for next‐generation intelligent sensing platforms that offer long‐distance, high‐accuracy, distributed measurement capabilities and multiparametric monitoring with resilience to harsh environmental conditions. The major limitations posed by FOS are 1) cross‐sensitivity, 2) enormous volume and large data generation, 3) low data processing speed, 4) degradation of signal‐to‐noise ratio over the fiber length, and 5) overall cost of sensor and interrogator systems. These challenges can be overcome by building advanced data analytics engines enabled by recent breakthroughs in machine learning (ML) and artificial intelligence (AI). This article presents a comprehensive review of recent studies that integrate ML and AI algorithms with FOS technologies. This review also highlights several FOS technology development directions that promise a significant impact on widespread use for several industrial applications, with an emphasis on energy systems monitoring. A perspective on future directions for further research development is also provided.</jats:p></jats:sec>

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