THE DEVELOPMENT OF FATIGUE FAILURE LOADING SPECTRUM ON TRANSPORTATION VEHICLE USING SMARTPHONE SENSORS

Fatigue failure is one of the main causes of damage to structural components of transportation vehicle, which has the potential to result in accidents or significant damage. To reduce this risk, regular component loading monitoring is essential. However, conventional monitoring technologies are ofte...

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Bibliographic Details
Main Author: Erwin R.Tambunan, Jagar
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/86936
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Institution: Institut Teknologi Bandung
Language: Indonesia
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Summary:Fatigue failure is one of the main causes of damage to structural components of transportation vehicle, which has the potential to result in accidents or significant damage. To reduce this risk, regular component loading monitoring is essential. However, conventional monitoring technologies are often expensive and difficult to implement in the field. As an alternative, the use of more accessible devices, such as smartphones, offers great potential in detecting and monitoring vehicle loading. The Phyphox application, which utilizes smartphone-based sensors such as accelerometers, offers a practical solution to collect loading data in real time. This study aims to develop and test a loading monitoring system using the Phyphox application to detect potential fatigue failure in transportation vehicle. The system is designed to provide an affordable, flexible, and easy-to-implement loading spectrum analysis in the field. The research methods include field experiments and signal analysis, where smartphones with the Phyphox application are used to collect vehicle acceleration and vibration data under various operational conditions. The collected data are then analyzed using a frequency spectrum, such as the Fast Fourier Transform (FFT), to identify vibration patterns that indicate potential fatigue failure. Based on the results obtained by analyzing rainflow counting through the exceedance curve of various smartphones, the noise generated is quite large, reaching 73% of the initial results of the measurement data before the process. The developed system offers a more affordable and flexible solution to improve the safety and operational reliability of transportation vehicle through earlier prediction of fatigue failure risks.