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

被引：13

作者：

Ersoz, Olcay Ozge ^{[1
]}

Inal, Ali Firat ^{[1
]}

Aktepe, Adnan ^{[1
]}

Turker, Ahmet Kursad ^{[1
]}

Ersoz, Suleyman ^{[1
]}

机构：

[1] Kirikkale Univ, Dept Ind Engn, TR-71450 Kirikkale, Turkey

来源：

SUSTAINABILITY | 2022年 / 14卷 / 21期

关键词：

predictive maintenance; transportation systems; systematic literature review; DIGITAL TWIN; INTELLIGENT;

D O I：

10.3390/su142114536

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

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