example_refs.bib

@Article{Bagavathiappan2013,
  author       = {S. Bagavathiappan and B. Lahiri and T. Saravanan and J. Philip and T. Jayakumar},
  date         = {2013-03-24},
  journaltitle = {Infrared Physics {\&} Technology},
  title        = {Infrared thermography for condition monitoring {\textendash} A review},
  doi          = {10.1016/j.infrared.2013.03.006},
  pages        = {35--55},
  volume       = {60},
  abstract     = {Infrared thermography (IRT) is a non-contact condition monitoring (CM) tool. We review the advances of IRT for CM of machineries, equipment and processes. Applications in various industries are covered in this critical review. Basics of IRT, experimental procedures and data analysis techniques are reviewed. Sufficient background information for the beginners and non-experts are provided.},
  day          = {24},
  file         = {:C\:/Users/yulel/OneDrive - University of Southampton/PCBs/1-s2.0-S1350449513000327-main.pdf:PDF},
  keywords     = {Infrared thermography Condition monitoring Preventive maintenance Deformation monitoring Thermal anomaly Quality assurance Head, Smart Materials Section, Radiography & Thermography Section, Non-Destructive Evaluation Division, Metallurgy and Materials},
  month        = {sep},
  publisher    = {Elsevier {BV}},
  year         = {2013},
}

@Article{Liu2022,
  author       = {He Liu and Min Xia and Darren Williams and Jianzhong Sun and Hongsheng Yan},
  date         = {2022-07-30},
  journaltitle = {Journal of Sensors},
  title        = {Digital Twin-Driven Machine Condition Monitoring: A Literature Review},
  doi          = {10.1155/2022/6129995},
  editor       = {Xueliang Xiao},
  pages        = {1--13},
  volume       = {2022},
  abstract     = {Digital twin (DT), aiming to characterise behaviors of physical entities by leveraging the virtual replica in real time, is an emerging technology and paradigm at the forefront of the Industry 4.0 revolution. The implementation of DT in predictive maintenance has facilitated its growth. As a major component of predictive maintenance, condition monitoring (CM) has great potential to combine with DT. To describe the state-of-the-art of DT-driven CM, this paper delivers a systematic review on the theoretical and practical development of DT in advancing CM. The evolution of concepts, main research areas, applied domains, and related key technologies are summarised. The driver of DT for CM is detailed in three aspects: data support, capability enhancement, and maintenance mode shift. The implementation process of DT-driven CM is introduced from the classification of DT modelling and the extension of monitoring algorithms. Finally, current challenges and opportunities for future research are discussed especially concerning the barriers and gaps in data management, high-fidelity modelling, behavior characterisation, framework standardisation, and uncertainty quantification.},
  file         = {:C\:/Users/yulel/OneDrive - University of Southampton/PCBs/Digital_Twin-Driven_Machine_Condition_Monitoring_A.pdf:PDF},
  publisher    = {Hindawi Limited},
  year         = {2022},
}

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