Eren, Levent

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Profile URL
https://hdl.handle.net/20.500.14365/7077
Name Variants
Levent Eren
Eren, L
Job Title
Email Address
levent.eren@ieu.edu.tr
Main Affiliation
05.06. Electrical and Electronics Engineering
Status
Current Staff
Website
https://people.ieu.edu.tr/en/leventeren
ORCID ID
0000-0002-5804-436X
Scopus Author ID
6603027663
Turkish CoHE Profile ID
DF81D5CD73F3EB26
Google Scholar ID
pWxuQQcAAAAJ
WoS Researcher ID
T-2245-2019
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Levent_Eren.png (106.46 KB)

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41

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JournalCount
2025 Symposium on Computational Intelligence on Engineering/Cyber Physical Systems-CIES -- MAR 17-20, 2025 -- Trondheim, NORWAY2
48th Conference of the Industrial Electronics Society-IECON-Annual -- Oct 17-20, 2022 -- Brussels, Belgium2
Electrical Engineering2
Electrıcal Engıneerıng2
Ieee Transactıons on Industrıal Electronıcs1
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Now showing 1 - 10 of 21
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    Patent
    Method and Apparatus for Performing Motor-Fault Detection Via Convolutional Neural Networks
    (2020) Kiranyaz, Serkan; İnce, Türker; Eren, Levent
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    Conference Object
    Citation - Scopus: 1
    Improved Detection of Broken Rotor Bars by 1-D Self-Onns
    (IEEE, 2022) Eren, Levent; Devecioglu, Ozer Can; Ince, Turker; Askar, Murat
    Recently, machine learning techniques have been increasingly applied to the detection of both mechanical and electrical faults in induction motors. Broken rotor bars are one of the most common fault types that seriously affect the efficiency and lifetime of induction motors. In this study, compact 1-D self-organized operational neural networks (Self-ONNs) are applied to improve the detection and classification of broken rotor bars in induction motors. 1-D convolutional neural networks (CNNs) are a special case of Self-ONNs and they are usually preferred to traditional fault diagnosis systems with separately designed feature extraction and classification blocks as they provide cost-effective and practical hardware implementation. The proposed system improves the detection and classification performance of 1-D CNNs while still providing similar advantages and preserving real-time computational ability.
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    Correction
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    Article
    Citation - WoS: 24
    Citation - Scopus: 33
    Early Bearing Fault Diagnosis of Rotating Machinery by 1d Self-Organized Operational Neural Networks
    (IEEE-Inst Electrical Electronics Engineers Inc, 2021) İnce, Türker; Malik, Junaid; Devecioglu, Ozer Can; Kiranyaz, Serkan; Avcı, Onur; Eren, Levent; Gabbouj, Moncef
    Preventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs. Recently many studies investigated machine learning monitoring methods especially based on Deep Learning networks focusing mostly on detecting bearing faults; however, none of them addressed bearing fault severity classification for early fault diagnosis with high enough accuracy. 1D Convolutional Neural Networks (CNNs) have indeed achieved good performance for detecting RM bearing faults from raw vibration and current signals but did not classify fault severity. Furthermore, recent studies have demonstrated the limitation in terms of learning capability of conventional CNNs attributed to the basic underlying linear neuron model. Recently, Operational Neural Networks (ONNs) were proposed to enhance the learning capability of CNN by introducing non-linear neuron models and further heterogeneity in the network configuration. In this study, we propose 1D Self-organized ONNs (Self-ONNs) with generative neurons for bearing fault severity classification and providing continuous condition monitoring. Experimental results over the benchmark NSF/IMS bearing vibration dataset using both x- and y-axis vibration signals for inner race and rolling element faults demonstrate that the proposed 1D Self-ONNs achieve significant performance gap against the state-of-the-art (1D CNNs) with similar computational complexity.
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    Article
    Citation - WoS: 53
    Citation - Scopus: 65
    Enhanced Bearing Fault Detection Using Multichannel, Multilevel 1d Cnn Classifier
    (Springer, 2022) Ozcan, Ibrahim Halil; Devecioglu, Ozer Can; İnce, Türker; Eren, Levent; Askar, Murat
    Electric motors are widely used in many industrial applications on account of stability, solidity and ease of use. Mechanical bearing faults have the highest statistical occurrence percentage among all of the motor fault types. Accurate and advance detection of the bearing faults is critical to avoid unpredicted breakdowns of electric motors. Through early detection of bearing faults, it would be possible to solve the problem at a lower cost by repairing and/or replacing relevant parts. Most of the fault detection works in the literature attempted to detect binary {healthy, faulty} motor fault case based on a single input. In this study, we propose an enhanced performance bearing fault diagnosis system based on multichannel, multilevel 1D-CNN classifier processing vibration data collected from multiple accelerometers mounted on bearings in a test bed. Effectiveness and feasibility of the proposed method are validated by applying it to the benchmark IMS bearing vibration dataset for inner race and rolling element faults and comparing the results with the conventional single-axis data-based fault detection.
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    Doctoral Thesis
    Multi-Channel, Multi-Level Framework for Bearing Fault Diagnosis in Electrical Machines
    (İzmir Ekonomi Üniversitesi, 2021) Özcan, İbrahim Halil; Eren, Levent; İnce, Türker
    Elektrik motorları kararlılık, sağlamlık ve kullanım kolaylığı avantajları ile birlikte gelmektedirler. Ayrıca kullanıcılara düşük işletme ve bakım maliyetleri sağlarlar. Bu önemli özelliklerinden dolayı çok çeşitli endüstriyel uygulamalarda yaygın olarak tercih edilmektedirler. Asenkron motorların arızalanması, endüstriyel üretim üzerindeki etkisi nedeniyle büyük bir endişe kaynağıdır. Asenkron makinelerde yaygın olarak bilyalı veya rulmanlı yataklar kullanılmaktadır ve en yaygın motor arızaları bu bileşenlerden kaynaklanmaktadır. Bu olası rulman arızalarının en erken aşamada doğru bir şekilde tespit edilmesi, kaçınılmaz tehlikelerle karşı karşıya kalmak yerine ilgili parçaları onararak ve/veya değiştirerek sorunu daha düşük maliyetle çözmek için kritik öneme sahiptir. Literatürde yer alan arıza tespiti ile ilgili pek çok araştırma, tek bir girişe dayalı ikili {sağlıklı, arızalı} motor arıza durumlarını tespit etmeye odaklanmıştır. Bu doktora tezinde, çok kanallı, çok seviyeli 1B-Evrişimli Sinir Ağ (CNN) yapısı, ham zaman alanlı titreşim sinyallerini işleyerek, rulman arızalarını daha erken seviyelerde geliştirilmiş bir performansla sınıflandırmak için tasarlanmış ve kullanılmıştır, ve dolayısıyla yapı kestirimci bakım amacıyla kullanılabilmektedir. Önerilen sistem, her biri farklı hata türleri için uzmanlaşmış kompakt 1B CNN'ler grubunu eş zamanlı olarak farklı hata türlerini ({iç bilezik arızası, dış bilezik arızası, yuvarlanan eleman arızası}, gibi) tanımlamak için kullanmakta ve tanımlanan arıza tipine ait iki seviyeli ({erken seviye arıza, gelişmiş seviye arıza}, gibi) arıza tespiti başarmaktadır. Ek olarak, gerçek zamanlı uygulamalar gerçekleştirebilmek için veri ön işleme olarak kayan pencere tekniği uygulanmaktadır. Rulman hataları için en zengin bilgi kaynağı olan titreşim sinyalleri, özellikle hataların erken tespiti için seçilmiştir ve Ulusal Havacılık ve Uzay Dairesi (NASA) ile işbirliği içindeki Cincinnati Üniversitesi, Akıllı Bakım Sistemleri Merkezi (IMS) tarafından sağlanan referans titreşim veri seti, önerilen yaklaşımın performansını doğrulamak ve sonuçları geleneksel tek eksenli veri tabanlı hata algılama yöntemleriyle karşılaştırmak için bu tezdeki deneylerde kullanılmaktadır.
  • Thumbnail Image
    Article
    Citation - WoS: 9
    Citation - Scopus: 11
    Motor Current Signature Analysis Via Four-Channel Fir Filter Banks
    (Elsevier Sci Ltd, 2016) Eren, Levent; Askar, Murat; Devaney, Michael J.
    Motor current signature analysis (MCSA) is capable of providing continuous monitoring of induction motors in a non-intrusive manner. Fourier based techniques have been used widely in processing of stator current but these techniques have a shortcoming in processing non-stationary signals such as the stator current. Recently, wavelet packet decomposition (WPD) has become popular in such applications since it gives better results in the case of non-stationary signals. The latter approach has much higher computational complexity limiting its use in motor diagnostics applications. In this study, the use of four-channel FIR filter banks is proposed to provide lower computational complexity. Four-channel filter banks employ higher level of parallel processing than currently used two-channel filter banks. FPGA implementation of the proposed algorithm would result in even further reduction in overall computation time. (C) 2016 Elsevier Ltd. All rights reserved.
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    Article
    Citation - WoS: 39
    Citation - Scopus: 48
    Neural Network Based Inspection of Voids and Karst Conduits in Hydro-Electric Power Station Tunnels Using Gpr
    (Elsevier Science Bv, 2018) Kilic, Gokhan; Eren, Levent
    This paper reports on the fundamental role played by Ground Penetrating Radar (GPR), alongside advanced processing and presentation methods, during the tunnel boring project at a Dam and Hydro -Electric Power Station. It identifies from collected GPR data such issues as incomplete grouting and the presence of karst conduits and voids and provides full details of the procedures adopted. In particular, the application of collected GPR data to the Neural Network (NN) method is discussed. (C) 2018 Elsevier B.V. All rights reserved.
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    Conference Object
    Citation - Scopus: 5
    Smartphone-Based Bearing Fault Diagnosis in Rotating Machinery Using Audio Data and 1d Convolutional Neural Networks
    (Association for Computing Machinery, 2024) Celebioglu, C.; Kilickaya, S.; Eren, Levent
    Asynchronous machines are essential components that drive critical systems across industrial, trading, and residential sectors, powering heating units, pumps, and various appliances. Yet, ensuring their reliable process is paramount to prevent costly defects and maintain productivity. Notably, failures in the rolling element bearings (REB) account for about forty percent of motor failures, underscoring the urgency of early detection to mitigate operational risks and financial losses. To address this challenge, this paper proposes an innovative smartphone-based diagnostic technique for detecting bearing faults in induction machines. Leveraging the common availability and computational capabilities of smartphones, the approach utilizes the devices' audio recording functionality to capture motor audio signals. Audio data collected from rotating machines with various fault types is used to train a 1D Convolutional Neural Network (1D CNN), and the trained model is then deployed on a smartphone for real-time fault diagnosis. Embedding this approach into a user-friendly mobile application enhances accessibility and usability, offering a cost-effective solution for fault diagnosis in induction machines. © 2024 ACM.
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    Conference Object
    Citation - Scopus: 3
    Audio-Based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description
    (IEEE, 2025) Kilickaya, Sertac; Ahishali, Mete; Celebioglu, Cansu; Sohrab, Fahad; Eren, Levent; Ince, Turker; Gabbouj, Moncef
    The frequent breakdowns and malfunctions of industrial equipment have driven increasing interest in utilizing cost-effective and easy-to-deploy sensors, such as microphones, for effective condition monitoring of machinery. Microphones offer a low-cost alternative to widely used condition monitoring sensors with their high bandwidth and capability to detect subtle anomalies that other sensors might have less sensitivity. In this study, we investigate malfunctioning industrial machines to evaluate and compare anomaly detection performance across different machine types and fault conditions. Log-Mel spectrograms of machinery sound are used as input, and the performance is evaluated using the area under the curve (AUC) score for two different methods: baseline dense autoencoder (AE) and oneclass deep Support Vector Data Description (deep SVDD) with different subspace dimensions. Our results over the MIMII sound dataset demonstrate that the deep SVDD method with a subspace dimension of 2 provides superior anomaly detection performance, achieving average AUC scores of 0.84, 0.80, and 0.69 for 6 dB, 0 dB, and -6 dB signal-to-noise ratios (SNRs), respectively, compared to 0.82, 0.72, and 0.64 for the baseline model. Moreover, deep SVDD requires 7.4 times fewer trainable parameters than the baseline dense AE, emphasizing its advantage in both effectiveness and computational efficiency.