Systematic literature review on predictive maintenance of vehicles and diagnosis of vehicle's health using machine learning techniques. (25th October 2022)

• Record Type: Journal Article
• Authors: Jain, Muskan; Vasdev, Dipit; Pal, Kunal; Sharma, Vishal
• Is Part Of: Computational intelligence. Volume 38:Number 6(2022)
• Page Start: 1990
• Page End: 2008
• Publication Date: 2022-10-25
• Subjects: automotive -- lifetime prediction -- machine learning -- on board diagnosis -- predictive maintenance -- remaining useful life; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12553
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Many industries, inclusive of the automobile industry, have shifted their attention toward predictive maintenance. The automobile industry finds predictive maintenance a key player in improving the servicing and vehicles they deliver. It is also vital for the vehicle owners to diagnose the vehicles to prevent risks the vehicle may face by timely servicing. Even though, with so many benefits of predictive maintenance, it is challenging to detect a breakdown in advance in the automobile sector. This is due to the restricted accessibility to sensors and the unavailability of some design applications. However, with the continuous advances in technology, machine learning (ML) methods have come forward as a viable solution to analyze data and develop solutions even when the data is scarce. This article intends to provide the literature review of ML techniques used for predictive maintenance of automobiles and diagnosis of the vehicle's health using ML. This review focuses on machine learning techniques in practice, extraction of data from the onboard diagnosis system and difficulties that models face. The article also explores the possibility and scope of positive work efforts in this area.