Artificial Intelligence-Driven Vehicle Fault Diagnosis to Revolutionize Automotive Maintenance:A Review

作     者：Md Naeem Hossain Md Mustafizur Rahman Devarajan Ramasamy 

作者机构：Faculty of Mechanical and Automotive Engineering TechnologyUniversiti Malaysia Pahang Al-Sultan AbdullahPekan26600Malaysia Automotive Engineering CenterUniversiti Malaysia Pahang Al-Sultan AbdullahPekan26600Malaysia 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2024年第141卷第11期

页      面：951-996页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：funding provided through University Distinguished Research Grants(Project No.RDU223016) as well as financial assistance provided through the Fundamental Research Grant Scheme(No.FRGS/1/2022/TK10/UMP/02/35) 

主　　题：Artificial intelligence machine learning deep learning vehicle fault diagnosis predictive maintenance 

摘      要：Conventional fault diagnosis systems have constrained the automotive industry to damage vehicle maintenance and component longevity ***,there is a growing demand for advanced fault diagnosis technologies to mitigate the impact of these limitations on unplanned vehicular downtime caused by unanticipated vehicle *** to vehicles’increasingly complex and autonomous nature,there is a growing urgency to investigate novel diagnosis methodologies for improving safety,reliability,and *** Artificial Intelligence(AI)has provided a great opportunity in this area,a systematic review of the feasibility and application of AI for Vehicle Fault Diagnosis(VFD)systems is ***,this review brings new insights into the potential of AI in VFD methodologies and offers a broad analysis using multiple *** focus on reviewing relevant literature in the field of machine learning as well as deep learning algorithms for fault diagnosis in engines,lifting systems(suspensions and tires),gearboxes,and brakes,among other vehicular *** then delve into some examples of the use of AI in fault diagnosis and maintenance for electric vehicles and autonomous *** review elucidates the transformation of VFD systems that consequently increase accuracy,economization,and prediction in most vehicular sub-systems due to AI ***,the limited performance of systems based on only one of these AI techniques is likely to be addressed by combinations:The integration shows that a single technique or method fails its expectations,which can lead to more reliable and versatile diagnostic *** synthesizing current information and distinguishing forthcoming patterns,this work aims to accelerate advancement in smart automotive innovations,conforming with the requests of Industry 4.0 and adding to the progression of more secure,more dependable *** findings underscored the necessity for cross-disciplinary cooperation