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A Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect

Author

Listed:
  • Olcay Özge Ersöz

    (Department of Industrial Engineering, Kirikkale University, Kirikkale 71450, Turkey)

  • Ali Fırat İnal

    (Department of Industrial Engineering, Kirikkale University, Kirikkale 71450, Turkey)

  • Adnan Aktepe

    (Department of Industrial Engineering, Kirikkale University, Kirikkale 71450, Turkey)

  • Ahmet Kürşad Türker

    (Department of Industrial Engineering, Kirikkale University, Kirikkale 71450, Turkey)

  • Süleyman Ersöz

    (Department of Industrial Engineering, Kirikkale University, Kirikkale 71450, Turkey)

Abstract

With the rapid progress of network technologies and sensors, monitoring the sensor data such as pressure, temperature, current, vibration and other electrical, mechanical and chemical variables has become much more significant. With the arrival of Big Data and artificial intelligence (AI), sophisticated solutions can be developed to prevent failures and predict the equipment’s remaining useful life (RUL). These techniques allow for taking maintenance actions with haste and precision. Accordingly, this study provides a systematic literature review (SLR) of the predictive maintenance (PdM) techniques in transportation systems. The main focus of this study is the literature covering PdM in the motor vehicles’ industry in the last 5 years. A total of 52 studies were included in the SLR and examined in detail within the scope of our research questions. We provided a summary on statistical, stochastic and AI approaches for PdM applications and their goals, methods, findings, challenges and opportunities. In addition, this study encourages future research by indicating the areas that have not yet been studied in the PdM literature.

Suggested Citation

  • Olcay Özge Ersöz & Ali Fırat İnal & Adnan Aktepe & Ahmet Kürşad Türker & Süleyman Ersöz, 2022. "A Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14536-:d:963955
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    References listed on IDEAS

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