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Hierarchical spatial-temporal state machine for vehicle instrument cluster manufacturing

Maniak, Tomasz and Iqbal, Rahat and Doctor, Faiyaz (2021) 'Hierarchical spatial-temporal state machine for vehicle instrument cluster manufacturing.' IEEE Transactions on Intelligent Transportation Systems, 22 (7). pp. 4131-4140. ISSN 1524-9050

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The vehicle instrument cluster is one of the most advanced and complicated electronic embedded control systems used in modern vehicles providing a driver with an interface to control and determine the status of the vehicle. In this paper, we develop a novel hybrid approach called Hierarchical Spatial-Temporal State Machine (HSTSM). The approach addresses a problem of spatial-temporal inference in complex dynamic systems. It is based on a memory-prediction framework and Deep Neural Networks (DNN) which is used for fault detection and isolation in automatic inspection and manufacturing of vehicle instrument cluster. The technique has been compared with existing methods namely rule-based, template-based, Bayesian, restricted Boltzmann machine and hierarchical temporal memory methods. Results show that the proposed approach can successfully diagnose and locate multiple classes of faults under real-time working conditions.

Item Type: Article
Uncontrolled Keywords: Spatial-temporal inference; fault detection; fault isolation; neural networks; Restricted Boltzmann Machine; hierarchical spatial-temporal state machine; deep belief network; data analysis
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 11 Sep 2020 15:21
Last Modified: 23 Sep 2022 19:42

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