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Predictive Maintenance in the Automotive Sector: A Literature Review
With the rapid advancement of sensor and network technology, there has been a notable increase in the availability of condition-monitoring data such as vibration, temperature, pressure, voltage, and other electrical and mechanical parameters. With the introduction of big data, it is possible to prev...
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| Published in: | Mathematical and computational applications 2022-02, Vol.27 (1), p.2 |
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| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c294t-1764486f5bbcf871875f9d6c79ae0a228fa010d1294d052e3834b6dcb653b38d3 |
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| cites | cdi_FETCH-LOGICAL-c294t-1764486f5bbcf871875f9d6c79ae0a228fa010d1294d052e3834b6dcb653b38d3 |
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| container_issue | 1 |
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| container_title | Mathematical and computational applications |
| container_volume | 27 |
| creator | Arena, Fabio Collotta, Mario Luca, Liliana Ruggieri, Marianna Termine, Francesco Gaetano |
| description | With the rapid advancement of sensor and network technology, there has been a notable increase in the availability of condition-monitoring data such as vibration, temperature, pressure, voltage, and other electrical and mechanical parameters. With the introduction of big data, it is possible to prevent potential failures and estimate the remaining useful life of the equipment by developing advanced mathematical models and artificial intelligence (AI) techniques. These approaches allow taking maintenance actions quickly and appropriately. In this scenario, this paper presents a systematic literature review of statistical inference approaches, stochastic methods, and AI techniques for predictive maintenance in the automotive sector. It provides a summary on these approaches, their main results, challenges, and opportunities, and it supports new research works for vehicle predictive maintenance. |
| doi_str_mv | 10.3390/mca27010002 |
| format | article |
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| ispartof | Mathematical and computational applications, 2022-02, Vol.27 (1), p.2 |
| issn | 2297-8747 1300-686X 2297-8747 |
| language | eng |
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| source | Publicly Available Content Database; EZB Electronic Journals Library |
| subjects | Accident prevention Artificial intelligence Automobile industry Big Data Capital costs Condition monitoring Cost control data-driven methods Decision making Failure Industry 4.0 Internet of Things Literature reviews Machine learning machine learning algorithms Maintenance costs Mechanical properties Predictive maintenance Preventive maintenance Productivity Repair & maintenance Sensors Statistical inference Vibration monitoring |
| title | Predictive Maintenance in the Automotive Sector: A Literature Review |
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