Deep learning-based vehicular engine health monitoring system utilising a hybrid convolutional neural network/bidirectional gated recurrent unit

Rahim, Md. Abdur and Rahman, Md Mustafizur and Islam, Md. Shofiqul and Md. Muzahid, Abu Jafar and Rahman, Md. Arafatur and D., Ramasamy (2024) Deep learning-based vehicular engine health monitoring system utilising a hybrid convolutional neural network/bidirectional gated recurrent unit. Expert Systems with Applications, 257 (125080). pp. 1-20. ISSN 0957-4174. (Published)

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DOI/Official URL: https://doi.org/10.1016/j.eswa.2024.125080

Abstract

Vehicles play a pivotal role in the current era of Industry 4.0 by providing passengers with excellent mobility, comfort, and safety while strengthening national and international economies. Unanticipated vehicular engine issues can hinder performance and lead to costly maintenance. As analytics processes become faster, more accurate, and more reliable, intelligent fault prediction and diagnosis for vehicles, particularly engines, is becoming increasingly popular. To date, hybrid deep learning approaches to vehicle engine diagnostics have been limited, and none have used engine health monitoring and categorisation based on vulnerability assessment and vehicle structural information. This paper introduces a hybrid deep learning-based vehicular engine health monitoring system (VEHMS) decision model using Deep CNN (convolutional neural network)-BiGRU (bi-directional gated recurrent unit). This model monitors a vehicle’s engine health in real-time and classifies its status as good, critical, moderate, or minor condition. Several advanced and hybrid deep learning algorithms were applied to monitor engine health and categorise its status by integrating sensor data with evaluated vulnerability information from an infrastructure vulnerability assessment model. The Deep CNN-BiGRU-based VEHMS decision model outperformed other techniques with an accuracy of 0.8897, ensuring minimal decision losses while classifying engine conditions. This study aims to contribute to developing comprehensive vehicle health monitoring systems and advance the automotive industry by incorporating more intelligent features. The proposed approach can enhance vehicle performance, reliability, and efficiency in the transportation sector by improving engine health monitoring.

Item Type:	Article
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Engine fault diagnosis; Hybrid deep learning; Vehicle health monitoring systems; VEHMS; Vulnerability assessment
Subjects:	T Technology > TJ Mechanical engineering and machinery
Faculty/Division:	Centre of Excellence: Automotive Engineering Centre Centre of Excellence: Automotive Engineering Centre Faculty of Mechanical and Automotive Engineering Technology
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	03 Sep 2024 06:58
Last Modified:	03 Sep 2024 06:58
URI:	http://umpir.ump.edu.my/id/eprint/42480
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