Systematic literature review on predictive maintenance of vehicles and diagnosis of vehicle's health using machine learning techniques

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.

Proposal of a Machine Learning Predictive Maintenance Solution Architecture

“…The increased interest of researchers in the predictive maintenance field it is demonstrated in the specialized literature both by articles that perform a systematic literature review of the studies that treat this concept in the context of Industry 4.0 (Zonta et al, 2020;Sahli, Evans and Manohar, 2021) and the articles that use the same technique of systematic literature review to identify machine learning methods and techniques used in predictive maintenance (Carvalho et al, 2019;Dalzochio et al, 2020;Jain et al, 2022).…”

Smart Grids in Industrial Paradigms: A Review of Progress, Benefits, and Maintenance Implications: Analyzing the Role of Smart Grids in Predictive Maintenance and the Integration of Renewable Energy Sources, Along With Their Overall Impact on the Industri

“…These techniques aid smart grids in making informed decisions related to power operation and planning upgrades, thereby providing efficient and reliable power services. Jain et al (2022) provide a systematic literature review on predictive maintenance of vehicles and the diagnosis of vehicle health using machine learning techniques. While the focus is on the automobile industry, the insights are relevant to the broader context of predictive maintenance in smart grids.…”

Advances in Machine‐Learning Enhanced Nanosensors: From Cloud Artificial Intelligence Toward Future Edge Computing at Chip Level

“…Machine learning has revolutionized many fields, including nanophotonics and the design of nanophotonic sensors. [61][62][63][64][65][66][67][68] Among numerous photonic sensors, nanoantenna-based sensors combined with machine-learning algorithms are promising, because the performance of nanoantenna sensors depends on their structural patterns. Nanoantenna-based sensors refer to a class of sensing devices that utilize nanoantennas to detect and analyze signals or phenomena at the nanoscale.…”

“…[ 67 ] Copyright 2021, John Wiley and Sons Ltd. Reproduced with permission. [ 68 ] Copyright 2022, Springer Nature. Reproduced with permission.…”