Person: DİRİK, AHMET EMİR
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DİRİK
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AHMET EMİR
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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-3733-2017Gears 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.Publication A one-dimensional convolutional neural network-based method for diagnosis of tooth root cracks in asymmetric spur gear pairs(Mdpi, 2023-04-01) Kalay, Onur Can; KALAY, ENGİN; Karpat, Esin; DİRİK, AHMET EMİR; Dirik, Ahmet Emir; Karpat, Fatih; 0000-0001-8643-6910; 0000-0001-8474-7328; KIK-4851-2024; A-5259-2018Gears are fundamental components used to transmit power and motion in modern industry. Their health condition monitoring is crucial to ensure reliable operations, prevent unscheduled shutdowns, and minimize human casualties. From this standpoint, the present study proposed a one-dimensional convolutional neural network (1-D CNN) model to diagnose tooth root cracks for standard and asymmetric involute spur gears. A 6-degrees-of-freedom dynamic model of a one-stage spur gear transmission was established to achieve this end and simulate vibration responses of healthy and cracked (25%-50%-75%-100%) standard (20 degrees/20 degrees) and asymmetric (20 degrees/25 degrees and 20 degrees/30 degrees) spur gear pairs. Three levels of signal-to-noise ratios were added to the vibration data to complicate the early fault diagnosis task. The primary consideration of the present study is to investigate the asymmetric gears' dynamic characteristics and whether tooth asymmetry would yield an advantage in detecting tooth cracks easier to add to the improvements it affords in terms of impact resistance, bending strength, and fatigue life. The findings indicated that the developed 1-D CNN model's classification accuracy could be improved by up to 12.8% by using an asymmetric (20 degrees/30 degrees) tooth profile instead of a standard (20 degrees/20 degrees) design.Publication Genetic based road selection for tracking systems(Istanbul Univ, Fac Engineering, 2005-01-01) Dirik, Ahmet Emir; DİRİK, AHMET EMİR; Yenikaya, Gökhan; YENİKAYA, GÖKHAN; KIK-4851-2024As land-vehicles almost always moves on roads, most of vehicle tracking systems, projects the vehicle positions on digital road segments. Vehicle tracking systems generally use dead reckoning and global positioning system (GPS) in order to get geographical vehicle position information. These positioning technologies have limitations either in accuracy of the absolute position, accumulated error or availability. Because of the unknown GPS noise, the estimated vehicle positions have undesirable errors. These errors not only cause position uncertainty but also road ambiguity problems in some crossroads. In this paper in order to solve this problem, a genetic map-matching method is introduced, which uses a digital road map to correct the position error.Publication Prnu based source camera attribution for image sets anonymized with patch-match algorithm(Elsevier Sci Ltd, 2019-09-01) Karaküçük, Ahmet; KARAKÜÇÜK, AHMET; Dirik, A. Emir; DİRİK, AHMET EMİR; Bursa Uludağ Üniversitesi/Mühendisliği Fakültesi/Elektrik ve Elektronik Mühendisliği Bölümü.; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Bilgisayar Mühendisliği Bölümü.; KIK-4851-2024; A-1996-2017Patch-Match is an efficient algorithm used for structural image editing and available as a tool on popular commercial photo-editing software. The tool allows users to insert or remove objects from photos using information from similar scene content. Recently, a modified version of this algorithm was proposed as a counter-measure against Photo-Response Non-Uniformity (PRNU) based Source Camera Identification (SCI). The algorithm can provide anonymity at a great rate (97%) and impede PRNU based SCI without the need of any other information, hence leaving no-known recourse for the PRNU-based SCI. In this paper, we propose a method to identify sources of the Patch-Match-applied images by using randomized subsets of images and the traditional PRNU based SCI methods. We evaluate the proposed method on two forensics scenarios in which an adversary makes use of the Patch-Match algorithm and distorts the PRNU noise pattern in the incriminating images she took with his camera. Our results show that it is possible to link sets of Patch-Match-applied images back to their source camera even in the presence of images that come from unknown cameras. To our best knowledge, the proposed method represents the very first counter-measure against the usage of Patch-Match in the digital forensics literature.Publication A deep learning- based method for early crack diagnosis in non-standard spur gear pairs(Amer Soc Mechanical Engineers, 2023-01-01) Ekwaro-Osire, Stephen; Dirik, Ahmet Emir; DİRİK, AHMET EMİR; Kalay, Onur Can; Karpat, Fatih; KARPAT, FATİH; Karpat, Esin; KARPAT, ESİN; Dirik, Ahmet Emir; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Elektrik ve Elektronik Mühendisliği Bölümü.; 0000-0001-8643-6910; 0000-0001-8474-7328; 0000-0002-9548-8648; KIK-4851-2024; A-5259-2018Gears are the key components of modern industry and have been widely employed in the automotive, wind turbine, and aviation fields. From the engineering point of view, an intelligent method that can automatically extract fault features from the vibration signals would be precious since failing in early diagnosis of root cracks may result in a tooth broken rapidly. In this regard, deep learning (DL) is increasingly popular in achieving early fault diagnostics tasks in geared systems with the wide availability of sensors and ever-increasing computation power. With this in mind, the asymmetric tooth concept offers higher load-carrying capacity, long fatigue propagation life, and the ability to lessen vibration and noise than the standard (symmetric) involute profile spur gears. From this standpoint, this study aims to determine the tooth root crack and its degree for both symmetric (20 degrees/20 degrees) and asymmetric (20 degrees/30 degrees) involute spur gears with a DL-based approach using vibration data. To this end, the single tooth stiffness values of the designed gears were obtained with ANSYS software for healthy and cracked gears (50%-100%), and then the time-varying mesh stiffness was calculated. Besides, a six-degree-of-freedom dynamic model was developed by deriving the equations of motion of a one-stage spur gear transmission. The vibration responses were collected for the healthy state, 50%, and 100% crack degrees for symmetric and asymmetric tooth profiles. Three different signalto-noise ratios were considered to complicate the early crack diagnosis task and evaluate its influence on the DL algorithm's classification performance. The obtained findings were then evaluated and interpreted in time and frequency domains. To this end, the Fourier transform was applied to the simulated timesequence acceleration data in the time domain. As a supplementary finding, the present research also benefited from three statistical indicators, namely, (1) root mean square, (2) kurtosis, and (3) crest factor, to investigate whether the configuration of tooth profiles would provide an advantage in detecting tooth- root cracks. The present study also evaluated the influence of residual signals on the proposed DL-based method's classification accuracy and further expanded the scope of research work. The findings indicated that the overall classification accuracy could be improved by 5.1% using asymmetric (20 degrees/30 degrees) gearing.Publication Source device attribution of thermal images captured with handheld ir cameras(Ieee, 2019-01-01) ; Karaküçük, Ahmet; Dirik, A. Emir; DİRİK, AHMET EMİR; KARAKÜÇÜK, AHMET; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Bilgisayar Mühendisliği Bölümü.; KIK-4851-2024; A-1996-2017Source camera attribution of digital images has been a hot research topic in digital forensics literature. However, the thermal cameras and the radiometric data they generate stood as a nascent topic, as such devices arc expensive and tailored for specific use-cases not adapted by the masses. This has changed dramatically, with the low-cost, pluggable thermal-camera add-ons to smartphones and similar low-cost pocket-size thermal cameras introduced to consumers recently, which enabled the use of thermal imaging devices for the masses. In this paper, we are going to investigate the use of an established source device attribution method on radiometric data produced with a consumer-level, low-cost handheld thermal camera. The results we represent in this paper are promising and show that it is quite possible to attribute thermal images with their source camera.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-5259-2018Gear 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.Publication The effect of inverse square law of light on enf in videos exposed by rolling shutter(IEEE, 2023-01-01) Vatansever, Saffet; Dirik, Ahmet Emir; Memon, Nasir; DİRİK, AHMET EMİR; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Bilgisayar Mühendisliği Bölümü; 0000-0002-6200-1717; KIK-4851-2024Due to a constant imbalance between demand and supply of power, ENF (Electric Network Frequency) fluctuates around a nominal value of 50 or 60 Hz. These variations in ENF cause the luminance intensity of a mains-powered light source, having no AC/DC converter inside, also to fluctuate. As a result, a video of a scene illuminated by a mains-powered light source can be used to estimate these fluctuations. As a consequence, the ENF signal within the time period when the video was captured can be estimated. This work explores the effects of frame rate harmonics that emerge when a rolling shutter based approach is used for ENF estimation from videos captured using CMOS cameras. These harmonics are a problem, especially for videos whose frame rate is a divisor of the nominal ENF because the frame rate harmonics and the ENF harmonics overlap. It is discovered that a key reason for the presence of the harmonics is the inverse square law of light that results in some repeating patterns of luminance variation across frames. This paper presents an analysis of the effect of the inverse square law of light on ENF estimation. A technique for refined ENF-related luminance signal estimation is proposed that attenuates these frame rate harmonics. This enables more accurate ENF estimates. The work also proposes an approach to estimate ENF-related luminance waveform cycles within each video frame, and a method to compute the confidence score for the estimated cycles. It provides insight into the reliability of the extracted ENF signal from a video, in the sense of its usefulness for ENF forensics, and consequently for ENF detection, which is an important precursor to ENF-based video forensics.Publication Sub-pixel counting based diameter measurement algorithm for industrial machine vision(Elsevier Sci Ltd, 2023-12-27) Poyraz, Ahmet Gokhan; Kacmaz, Mehmet; Gurkan, Hakan; Dirik, Ahmet Emir; DİRİK, AHMET EMİR; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Bilgisayar Mühendisliği Bölümü.; 0000-0002-6200-1717; KIK-4851-2024In recent years, there has been a notable surge in the utilization of industrial image processing applications across various sectors, including automotive, medical, and space industries. These applications rely on specialized camera systems and advanced image processing techniques to accurately measure working products with precise tolerances. This research presents a novel fast algorithm for measuring the diameter of a ring, employing a subpixel counting method. The algorithm classifies image pixels into two categories: full pixels and transition pixels. Full pixels reside entirely within the inner region of the workpiece, while transition pixels represent gray pixels that reside at the boundary between the workpiece and its background. To ensure accurate determination of the object area, the proposed method incorporates normalization to account for the contribution of transition pixels alongside full pixels. Subsequently, the circle area equation is employed to calculate the diameter. Moreover, a robust threshold selection method is introduced to effectively distinguish pixels with gray intensities. The experimental setup consists of an industrial camera equipped with telecentric lenses and appropriate illumination. The results demonstrate that the proposed algorithm achieves a 3-10 % improvement in accuracy compared to existing approaches. In terms of measuring sensitivity, the operational sensitivity of the proposed methodology is quantified as 1/20th of the pixel size, exhibiting an average uncertainty of 1 mu m. Furthermore, the proposed method surpasses existing works by at least 12.5 % to 35 % in terms of benchmarking computing time.