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KORCUKLU, BURAK

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KORCUKLU

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    Publication
    Vibration-based early crack diagnosis with machine learning for spur gears
    (Amer Soc Mechanical Engineers, 2020-01-01) Karpat, Fatih; KARPAT, FATİH; Dirik, Ahmet Emir; DOĞAN, OĞUZ; DİRİK, AHMET EMİR; Kalay, Onur Can; Korcuklu, Burak; KORCUKLU, BURAK; Doğan, Oğuz; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü.; 0000-0001-8474-7328; 0000-0001-8643-6910; KIK-4851-2024; A-5259-2018
    Gear mechanisms are one of the most significant components of the power transmission systems. Due to increasing emphasis on the high-speed, longer working life, high torques, etc. cracks may be observed on the gear surface. Recently, Machine Learning (ML) algorithms have started to be used frequently in fault diagnosis with developing technology. The aim of this study is to determine the gear root crack and its degree with vibration-based diagnostics approach using ML algorithms.To perform early crack detection, the single tooth stiffness and the mesh stiffness calculated via ANSYS for both healthy and faulty (25-50-75-100%) teeth. The calculated data transferred to the 6-DOF dynamic model of a one-stage gearbox, and vibration responses was collected. The data gathered for healthy and faulty cases were evaluated for the feature extraction with five statistical indicators. Besides, white Gaussian noise was added to the data obtained from the 6-DOF model, and it was aimed at early fault diagnosis and condition monitoring with ML algorithms.In this study, the gear root crack and its degree analyzed for both healthy and four different crack sizes (25%-50%-75%-100%) for the gear crack detection. Thereby, a method was presented for early fault diagnosis without the need for a big experimental dataset. The proposed vibration-based approach can eliminate the high test rig construction costs and can potentially be used for the evaluation of different working conditions and gear design parameters. Therefore, catastrophic failures can be prevented, and maintenance costs can be optimized by early crack detection.
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    Publication
    Fault diagnosis with deep learning for standard and asymmetric involute spur gears
    (Amer Soc Mechanical Engineers, 2021-01-01) Yuce, Celalettin; Dogan, Oguz; Karpat, Fatih; Dirik, Ahmet Emir; KARPAT, FATİH; DİRİK, AHMET EMİR; Kalay, Onur Can; Korcuklu, Burak; KORCUKLU, BURAK; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi; 0000-0001-8474-7328; 0000-0002-6200-1717; 0000-0001-8643-6910; 0000-0003-1387-907X; A-5259-2018; R-3733-2017
    Gears are critical power transmission elements used in various industries. However, varying working speeds and sudden load changes may cause root cracks, pitting, or missing tooth failures. The asymmetric tooth profile offers higher load-carrying capacity, long life, and the ability to lessen vibration than the standard (symmefric) profile spur gears. Gearbox faults that cannot be detected early may lead the entire system to stop or serious damage to the machine. In this regard, Deep Learning (DL) algorithms have started to be utilized for gear early fault diagnosis. This study aims to determine the root crack for both symmefric and asymmefric involute spur gears with a DL-based approach. To this end, single tooth stiffness of the gears was obtained with ANSYS software for healthy and cracked gears (50-100%), and then the time-varying mesh stiffness (TVMS) was calculated. A six-degrees-of-freedom dynamic model was developed by deriving the equations of motion of a single-stage spur gear mechanism. The vibration responses were collected for the healthy state, 50% and 100% crack degrees for both symmefric and asymmefric tooth profiles. Furthermore, the white Gaussian noise was added to the vibration data to complicate the early crack diagnosis task. The main contribution of this paper is that it adapts the DL-based approaches used for early fault diagnosis in standard profile involute spur gears to the asymmefric tooth concept for the first time. The proposed method can eliminate the need for large amounts of training data from costly physical experiments. Therefore, maintenance strategies can be improved by early crack detection.