Browsing by Author "Abebe, Teklu (Mr.)"

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    Automatic Route Setting and Dynamic Rescheduling Following Disturbance
    (Addis Ababa University, 2018-09) Mouhiyadin, Said; Abebe, Teklu (Mr.)
    Nowadays, development cannot be achieved without modern infrastructure capacity. The new railway project which links Addis Ababa to Djibouti aims to fulfill this task through safety and reliability as major goals. Railway operating near their theoretical capacity is particularly vulnerable to disruption by human behavior or engineering failures that lead to delays or to even cancellations of services. In order to prevent such incidents, the interlocking system is set to automatic or semi-automatic mode and rescheduling is planned by the system to set a new route for the remaining trains. This study aimed to analyze how the system reacts and respond to disruptive incidents in minimizing the impact of such disturbance. After reviewing relevant literatures regarding most occurring disruptive incidents in train operation and their consequences on the railway network reliability, we defined train rescheduling operation and more precisely the automatic route selection with their characteristics parameters after a disruption such as an accident on a level crossing or faults in electronics device. We introduce C++ programming language to model route setting using spanning tree configuration. A case study on rescheduling operation on a selected British railway area is carried on to evaluate delays cost after disruption. This data was processed to model how delays relevant to a specific disruption affect trains in a railway network using a neural network with a high performance.
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    Design of Passenger Information System and Smart Passenger Crowdedness Avoidance: Case of AALRT
    (Addis Ababa University, 2017-06) Yetages, Rorissa; Abebe, Teklu (Mr.)
    Addis Ababa Light Rail Transit is playing a prominent role in alleviating the city’s transportation problem. Currently, it only has an onboard passenger information system, and there is no wayside passenger information system. Consequently, passengers waiting at the station are unable to know if a train is coming, the estimated arrival time of the train, the crowdedness level on the train and whether the train is single or coupled. The other challenge is that on pick hours there exist a high passenger crowdedness on the trains which makes the service unintentionally unfair as only who can survive the high congestion on the train use the service. The primary objective of this thesis is to avoid the existing passenger information system problems of Addis Ababa Light Rail Transit by devising a reliable passenger information system and come up with a cost-effective solution for the current passenger over crowdedness problem. Hence, a passenger information system was designed by developing a website, an Android application and station displays which will all show the available space on the next train, the number of passengers expected to get off on that station, and its estimated remaining time of arrival. The simulation result showed that we achieved the stated objective as it can give detail information about the train for each passengers waiting at the stations. In addition to this, we introduced a passenger overcrowding solution. It is a passenger counting system that will put a limit on passengers getting on a train when the train load exceeds its carrying capacity. Android based point of sale (POS) ticketing device is proposed to be used as a passenger counter and ticketing at the same time. It is expected that the current manual ticketing problems encountered by Addis Ababa Light Rail Transit can be solved by this device.
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    Investigating in to Remote Monitoring Level crossing Safety using SCADA System: In Case of AALRT
    (Addis Ababa University, 2016-08) Mesfin, Abate; Abebe, Teklu (Mr.); Abi, Abate (Mr.) Co-Advisor
    Safety plays a prime importance in all aspects of life, especially in railway level crossings. Because accidents at level crossing contribute large portion on train accidents which costs human life and economical crisis. Moreover, the accident occur cause train delay and operational interruption. To tackle this problem and to assure safety by reducing accident at level crossing using various equipments used for this purpose. But existing warning device, and crossing barrier are simple train-oriented protection equipments. In this thesis, remote monitoring of level crossing to reduce those accidents to a minimum level to achieve safety at level crossing using wireless-SCADA System is proposed. SCADA system is employed to monitor level crossings. The system employed encompasses collecting of data from a number of remote terminal unit’s (RTU’s) and operator terminal ,and transferring it back to the traffic control center, carrying out any necessary analysis and control and then displaying that information on a number of operator screens or displays. The required control actions are then conveyed back to the process for taking action. In general, the SCADA system consists of two major parts, remote control center (RCC) and remote terminal unit’s (RTU’s).The RCC does the work of the a supervisor and controls the activities of a network through RTUs.
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    Performance Optimization of Train Dispatching Support System
    (Addis Ababa University, 2018-03) Nurye, Hassen; Abebe, Teklu (Mr.)
    One of the significant challenges in the daily operation of train dispatching is making the right decision upon unplanned conflict occurrence. The resolution process by itself will introduce an additional delay on the railway network unless well resolved. Thus, it is a decisive and challenging issue for train dispatchers and railway operation planners to decide which of the trains to stop or to pass from the trains involved in the conflict to bring minimum propagated delay. Such an operation with effective conflict resolution requires an intelligent decision support system that considers minimization of future dwelling time. To this end, this thesis developed a decision support system that provides an intelligent decision to the train dispatcher by detecting a conflict on a rail network with an optimal resolution of the conflict and cost. The approach addressed the minimization of overall delay due to the conflict resolution in addition to detection and resolution. A mixed integer linear programming approach has been implemented to find optimal combinations of arrival and departure events that bring minimum propagated delay. Optimization toolbox of the commercial software, MATLAB R2015a, was used to develop the solving algorithm and obtain the result. The solution procedure is also clearly illustrated using practical and hypothetical applications. The model has been applied to Ethio-Djibouti Railway enterprise railroad from DIRE-DAOUA to DAOUENLE, which consists of eight stations. The program was able to reach an optimal solution with minimum cost when compared to the manual (heuristic) approach especially for an increased number of trains and stations. The model was also tested based on various hypothetical assumptions and showed that it is a powerful tool to be used for train dispatchers for ensuring operational optimality and safety of the railway line.
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    Reliability, Availability and Maintainability Analysis of Addis Ababa Light Rail Transit Signaling System
    (Addis Ababa University, 2017-07) Alemu, Ayana; Jigsa, Tesfaye (Mr.); Abebe, Teklu (Mr.)
    The railway network is a complex and distributed system with several technologies working together to fulfil the demands on capacity, speed and mobility to transport goods and passengers. The railway transportation system in Addis Ababa, the capital of Ethiopia plays a vital role in sustaining social and economic activities of a country by providing a safe, reliable, environmentally friendly and cost efficient means of transportation for the people. Following the safe, economical and practical principles, the design of Addis Ababa Light Rail Transit (AA-LRT) chooses a reasonable communication and signal system. Quality of transportation system critically depend on the reliability of signaling systems. Despite their numerous advantages, considerable train delays, cancellation, high maintenance cost and passenger dissatisfaction are the main disadvantages of signaling systems failures. Reliability, Availability, and Maintainability (RAM) analysis is a practical technique that uses failure and repair dataset obtained over a reasonable time for dealing with proper signaling system operation, maintenance scheduling, cost control, and improving the availability and performance of signaling systems. This research study and analyse RAM of AA-LRT signaling system with a historical database of failures and repairs of signaling systems was collected in the whole line of AA-LRT over a period of six months operation. After data analysis, it was revealed that the Axle counter subsystem has the highest failure frequency with relatively low availability and the switch has the lowest failure frequency with highest availability. A failure Mode and Effect analysis (FMEA) result shows that the switch is most critical subsystem to cause risk. Similarly, estimating the availability of failed signaling subsystems and equipments indicated that all subsystems have an acceptable availability level of above 96%.
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    Use of Artificial Intelligence for Predictive Maintenance and Management of Addis Ababa Light Rail Transit
    (Addis Ababa University, 2017-12) Liban, Ali; Abebe, Teklu (Mr.)
    Condition based monitoring is gaining much importance in the industry because of the need to increase machine reliability and reduce the potential loss of production due to breakdowns caused by different defects. In this thesis, we are interested in a condition based monitoring techniques using artificial neural network approach, especially Multiple Layer Perceptron (MLP). The multiple layer perceptron networks trained with backpropagation algorithm are very frequently used to solve a classification problems. In order to keep the machine performing at its best, one of the principal tools for the diagnosis of signaling equipment problems is the acoustic analysis and also vibration analysis which can be used to extract the fault features and then identify the fault patterns. In addition, there is a demand for techniques that can make decision on the running health of the machine automatically and reliably. Artificial intelligent techniques have been successfully applied to automated detection and diagnosis of railway signaling equipment conditions. They largely increase the reliability of fault detection and diagnosis systems. Accordingly, the aim of this paper is to apply a MLP to classify a large number of faulty signals acquired from turn out in different states: crack signal and fatigue signal. The extracted parameters is the peak ratio, one of the best indicators. The main impact of this neural network is to generate answers that give the combined state of crack and fatigue simultaneously whereas most of previous neural networks have focalized mainly on gears or on bearings alone. Information about the signaling equipment obtained in the form of time signal indicators is converted into a frequency signal indicator using an algorithm designed and coded using MATLAB. The frequency data obtained using the algorithm then is used as an input for continuous learning by an artificial neural network. Based on this learning outcome, the state of signaling equipment can easily be defined and classified. We chose the renowned Multi-layer perceptron (MLP) an artificial neural network for the classification phase. From simulation, we obtained a learning rate of 98% showing our algorithm and equipment state classification as per the signal generated during operation is acceptable.