Optimization of RFID network planning for monitoring railway mechanical defects based on gradient-based Cuckoo search algorithm

• 主要作者: Talib, Nihad Hasan
• 格式: Thesis
• 語言: 英语; 英语; 英语
• 出版: 2020
• 主題: T Technology (General); TK7800-8360 Electronics
• 在線閱讀: http://eprints.uthm.edu.my/4137/

Radio Frequency Identification (RFID) is an increasingly widespread and applied technology of automatic real-time monitoring and control railway assets. For that, the present research has developed an RFID network-planning model that can improve real-time information detection based on the temperature and vibration in the gear and motor of the train bogies. The selected system was Kuala Lumpur railway system, which has been operating in the city of 243 km2 area. It involves three challenges which represent the objectives of this thesis; the first is how to deal with the large�scale area and huge number of stations based on functional features. The second is how to decide which station (or stations) is suitable to be applied with the RFID system to help in monitoring the trains effectively. Finally, the third challenge is how to find the optimal evolutionary method for railway network planning to increase the RFID system performance. The solution strategy started in its initial input and process to find effective stations that can serve the railway monitoring system well. The researcher developed a new clustering model to separate the necessary data from unnecessary data, and specified the suitable primary stations. For the second objective, the Analytic Hierarchy Process (AHP) was used to decide which stations can be used to monitor the railway system optimally. The Gradient-Based Cuckoo Search (GBCS) algorithm was used to achieve the final objective. It solved the multi-objective functions of RNP challenge. In the validation process, the results showed a superior finding compared to the firefly algorithm. It was able to detect more tags by 3%, and a reduced number of readers by 16.6%. In the large-scale area application, the GBCS algorithm achieved 100%, 93.75%, and 98.9% coverage for Maluri, Subang, and TBS stations, respectively. In conclusion, this study presented a novel hybrid evolutionary algorithm based on the combination of AHP with GBCS to specify optimal RFID reader positions and amount based on the working train station domain. The present method has proven its precise performance in RNP of large-scale area based on real-time railway monitoring tasks.