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dc.contributor.authorMuhammad Naufal Mansor
dc.date.accessioned2008-12-31T12:51:53Z
dc.date.available2008-12-31T12:51:53Z
dc.date.issued2008-03
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/3817
dc.descriptionAccess is limited to UniMAP community.en_US
dc.description.abstractThis paper presents an approach to the condition monitoring in vehicle. A vibration test profiles is obtained in road testing of sedan sized car. The first step is to define the performance parameters under study. The output depend variable are frequency, R.M.S, energy, and acceleration amplitude which have been considered for the measurement. Design parameter such as steering ,brake pedal, seat and engine are defined as those related directly to operational testing on specific test vehicle and which summarize the input to the vehicle in the given speed conditions. The overall objective of the research study was to analyse the vibration signature due to the mechanical problem in order to reduce the rate of the vibration. All the performance and operational characteristics of leading-edge of vibration technological were approached to monitor the vehicle systems. Key to the success the analysis of this thesis is to extract accurate vibration property data such as that provided by the LMS SCADAS Mobile analyzer. To take full advantage of the predictive properties on gathering data, it is essential that the linear and non-linear, properties of the materials should be incorporated into the research. These properties can only be analysed using LMS Test.Xpress. Hence, vibration technique is the way provided a better accuracy in vehicle monitoring and furthermore this thesis is succeeded.en_US
dc.language.isoenen_US
dc.publisherSchool of Mechatronic Engineeringen_US
dc.subjectCondition monitoringen_US
dc.subjectVehicle cabinen_US
dc.subjectVibration signalen_US
dc.subjectVibrationen_US
dc.subjectVibration testsen_US
dc.subjectMotor vehicles -- Vibrationen_US
dc.titleCondition monitoring in vehicle cabin using vibration signal characterizationen_US
dc.typeLearning Objecten_US
dc.contributor.advisorBibi Intan Suraya Murat (Advisor)en_US


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