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Deep Learning Towards Intelligent Vehicle Fault Diagnosis

Al-Zeyadi, Mohammed and Andreu-Perez, Javier and Hagras, Hani and Royce, Chris and Smith, Darren and Rzonsowski, Piotr and Malik, Ali (2020) Deep Learning Towards Intelligent Vehicle Fault Diagnosis. In: 2020 International Joint Conference on Neural Networks (IJCNN), 2020-07-19 - 2020-07-24, Glasgow.

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Abstract

Recently, the rapid development of automotive industries has given rise to large multidimensional datasets both in the production sites and after-sale services. Fault diagnostic systems are one of the services that the automotive industries provide. As a consequence of the rapid development of cars features, traditional rule-based diagnostic systems became very limited. Therefore, more sophisticated AI approaches need to be investigated towards more efficient solutions. In this paper, we focus on utilising deep learning so as to build a diagnostic system that is able to estimate the required services in an efficient and effective way. We propose a new model, called Deep Symptoms-Based Model Deep-SBM, as an approach to predict a wide range of faults by relying on the deep learning technique. The new proposed model is validated through a set of experiments in order to demonstrate how the underlying model runs and its impact on improving the overall performance metrics. We have applied the Deep-SBM on a real historical diagnostic data provided by Cognitran Ltd. The performance of the Deep-SBM was compared against the state-of-the-art approaches and better result has been reported in terms of accuracy, precision, recall, and F-Score. Based on the obtained results, some further directions are suggested in this context. The final goal is having fault prediction data collected online relying on IoT.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2020 International Joint Conference on Neural Networks (IJCNN)
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 04 Dec 2020 20:09
Last Modified: 04 Dec 2020 21:15
URI: http://repository.essex.ac.uk/id/eprint/27533

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