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Smart monitoring fault detection system for malfunction traffic light operation.


Citation

Che Soh, Azura and Ishak, Asnor Juraiza and Zaini, Mohd Hanif (2013) Smart monitoring fault detection system for malfunction traffic light operation. In: The 8th IEEE Conference on Industrial Electronics and Application (ICIEA 2013), 19 - 21 June 2013, Melbourne, Australia. (549-554 ).

Abstract

Normally, signboards are used to remind road users to call contractor and authority department, when malfunction of traffic light operation occurs. As a result, the action that taken for repairing the traffic light system is depend on initiative of the road users to call and report the failure of traffic light operation. The delay of report to local authority will cause the repairing work delayed. To overcome this problem, a smart monitoring fault detection system has been developed to monitor the traffic light operation in rural area or small city. The Fuzzy-Fault Identification and Fuzzy-Fault Classifications are two modules which employed as the subsystems in fault detection system. Both of the modules are used to classify the level of seriousness for traffic light failure based on two factors of failures which are electrical fault and mechanical fault. The seriousness of the traffic light failures are classified to four conditions such as normal, non-critical 1, non-critical 2 and critical and a signal will be sent to the contractor and authority department via message service for further action. Thus, it is a more effective system and has a bright prospect to be implemented in Malaysia.


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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Engineering
Notes: Full text are available at Special Collection Division Office
Keywords: Monitoring system; Fault detection system; Malfuntion of traffic light; Fuzzy-fault identification; Fuzzy-fault classification.
Depositing User: Erni Suraya Abdul Aziz
Date Deposited: 29 Mar 2014 14:04
Last Modified: 13 Jun 2014 01:41
URI: http://psasir.upm.edu.my/id/eprint/27374
Statistic Details: View Download Statistic

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