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

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), sophistic...

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Bibliographic Details
Published in:Sustainability 2022-11, Vol.14 (21), p.14536
Main Authors: Ersöz, Olcay Özge, İnal, Ali Fırat, Aktepe, Adnan, Türker, Ahmet Kürşad, Ersöz, Süleyman
Format: Article
Language:English
Subjects:
Aeronautics
Algorithms
Artificial intelligence
Bibliometrics
Dissertations & theses
Efficiency
Internet of Things
Literature reviews
Neural networks
Predictive maintenance
Sensors
Stochasticity
Sustainability
Systematic review
Transportation industry
Transportation systems
Vibration
Vibration monitoring
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su142114536