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    Effects of drive side pressure angle on gear fatigue crack propagation life for spur gears with symmetric and asymmetric teeth
    (American Society of Mechanical Engineers (ASME), 2019) Karpat, F.; Dogan, O.; Yilmaz, T.; Yüce, Celalettin; Kalay, O.C.; Karpat, E.; Kopmaz, O.
    Today gears are one of the most crucial machine elements in the industry. They are used in every area of the industry. Due to the high performances of the gears, they are also used in aerospace and wind applications. In these areas due to the high torques, unstable conditions, high impact forces, etc. cracks can be seen on the gear surface. During the service life, these cracks can be propagated and gear damages can be seen due to the initial cracks. The aim of this study is to increase the fatigue crack propagation life of the spur gears by using asymmetric tooth profile. Nowadays asymmetric gears have a very important and huge usage area in the industry. In this study, the effects of drive side pressure angle on the fatigue crack propagation life are studied by using the finite element method. The initial starting points of the cracks are defined by static stress analysis. The starting angles of the cracks are defined constant at 45°. The crack propagation analyses are performed in ANSYS SMART Crack-Growth module by using Paris Law. Four different drive side pressure angles (20°-20°, 20°-25°, 20°-30° and 20°-35°) are investigated in this study. As a result of the study the fatigue crack propagation life of the gears is increased dramatically when the drive side pressure angle increase. This results show that the asymmetric tooth profile not only decrease the bending stress but also increase the fatigue crack propagation life strongly. Copyright © 2019 ASME.
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    A Novel AI-Based Method for Spur Gear Early Fault Diagnosis in Railway Gearboxes
    (Institute of Electrical and Electronics Engineers Inc., 2020) Karpat, F.; Dirik, A.E.; Dogan, O.; Kalay, O.C.; Korcuklu, B.; Yüce, Celalettin
    Artificial intelligence (AI) applications have started to take place in our lives due to increasing data collection and processing capabilities with developing technology. In this regard, AI-based early fault diagnosis technologies, which have started to gain reliability in automotive, aviation, and wind turbine fields, have begun to use for railway gearboxes in terms of defect detection and predictive maintenance. Gears are one of the most significant components of powertrain systems. The AIbased fault diagnosis has become more prominent in recent years to predict the remaining useful life of gearbox systems. The gearbox early fault diagnosis plays an important role in both security and reducing high maintenance costs. This issue is of great importance in terms of rail vehicle safety and reliability in a medium to long term perspective. This paper deals with an approach of transferability to railway gearboxes of AI-based gear early fault diagnosis methods from other industries. A vibration-based early fault diagnosis approach and test setup are proposed for railway gearboxes. Early gear crack diagnosis is performed using MATLAB with machine learning algorithms. The proposed test setup allows different degrees of tooth cracks in railway gearboxes to be detected at different operating speeds. As a result, it is observed that the proposed AI-based approach is suitable to identify railway gearbox faults and can be adaptable in rail-based transportation systems. © 2020 IEEE.