Sürdürülebilir Kalkınma Amaçları

• 4

• MakaleAçık Erişim2025Scopus SJRQ1/r/munzur/1379
  Automated fault classification of asynchronous motor using mobile phone accelerometer and Parallel Residual CNN-GRU

  Ertargin, Merve; Orhan, Ahmet; Yildirim, Ozal; Gurgenc, Turan

  In this study, a fault detection system that classifies motor faults using deep learning without requiring any sensor connections inside or on the motor setup is proposed. The focus is on the electrical and mechanical faults of asynchronous motors, which are widely used in the industry. A mobile application developed using Flutter can collect vibration data using the three-axis accelerometer sensor in smartphones. By placing a smartphone with the application installed on the motor for which fault detection is desired, vibration data on three axes (x, y, z) is collected to train deep models. Subsequently, test data is used to identify the fault status and class of the electric motor. Different conditions and scenarios were created to test the adequacy of smartphone accelerometer sensors in detecting motor faults. Fault classification with vibration data obtained under different motor operating speeds in loaded and unloaded conditions achieved accuracy rates of over 99%.

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  AtıfScopus18·OpenAlex15

  İncele
• MakaleAçık Erişim2022Scopus SJRQ1/r/munzur/2537
  A new mobile convolutional neural network-based approach for pixel-wise road surface crack detection

  Doğan, Gürkan; Ergen, Burhan

  In recent years, the worldwide road network has grown rapidly and reached an enormous size. Parallel to this size of the road network, the imperfections on the road surfaces are just as large. This has led to a significant increase in road maintenance costs for traffic management departments. Researchers have proposed methods for automatic flaw detection using deep neural networks to reduce road maintenance costs. However, these methods are insufficient in terms of performance and lightness. In this study, a convolutional neural network-based approach is proposed, which provides more successful road surface crack detection and requires less lightweight compared to other studies. Since the performance of the proposed approach is higher and more lightweight, it can be used more effectively and efficiently in mobile systems.

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  AtıfScopus60·WoS55·OpenAlex63

  İncele
• MakaleYalnız künye2019Scopus SJRQ1/r/munzur/3241
  Elimination of a measurement problem: A robust prediction model for missing eigenvector value to assess earthquake induced out-of-plane failure of infill wall

  Onat, Onur; Yön, Burak

  One of the effective methods to assess local or global behavior of structural system is to perform shake table experiment. The biggest challenge of all experiments, either shake table or others, are missing data during the experiments due to removed instruments. This problem is arose especially during the out-of-plane tests. This problem is named as “Measurement” problem. This paper is focused on detection the measurement problem and solving this problem by using Adaptive-Network Based Neuro Inference System (ANFIS). Definition of the problem is briefly missing out-of-plane eigenvector value during the implementation of a shake table experiments on a full scale one bay one storey reinforced concrete structural system. For this aim, two shake table experiments were performed with reinforced concrete frame (RCF)and infill wall at the National Civil Engineering Laboratory (LNEC)Portugal. The out-of-plane failure of infill wall was assessed under combined bidirectional seismic load. Shake table experiments were conducted on two types of specimens. One of them is Unreinforced Brick Infill Wall (URBIF)composed of single layer 22 cm thick brick. The other is single layer 22 cm thick Infill Wall with Bed Joint Reinforcement solution (IwBJR). Both specimens were made of a single-layer brick infill wall enclosure with the RCF. Bed joint reinforcement term refers horizontal bed joint reinforcement as a strengthening technique. Shake table experiments were performed on each specimen at four stages. After third level of earthquake load, the accelerometers were removed on the wall to prevent damage to them. The removal of these instruments results in missing data. The missing eigenvector values were predicted with a robust Adaptive-Network Based Neuro Inference System (ANFIS)model to present failure mode of infill wall. The estimation quality was tested with R2, Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE)and Root Mean Square Error (RMSE)among with available data. The results of the quality indicator of R2 are 0.999 for URBIF model and 0.997 for IwBJR model, respectively. Moreover, rest of the indicator results are less than %1. The proposed approach is reliable on the base of quality indicator. Moreover, this study can be used for seismic engineering to predict eigenvector values to see local behavior of infill wall and mode shape evolution to determine which mod prone for partial and total collapse of infill wall.

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  AtıfScopus12·WoS11·OpenAlex10

  İncele
• MakaleAçık Erişim2018Scopus SJRQ1/r/munzur/3744
  FPGA-based online power quality monitoring system for electrical distribution network

  Yildirim, Ozal; Eristi, Bekis; Eristi, Huseyin; Unal, Sencer; Erol, Yavuz; Demir, Yakup

  In this paper, a new generation Internet-based Power Quality Monitoring System (IPQMS) that transmits real-time power quality (PQ) data over the internet has been developed. The monitoring system has the ability to measure PQ parameters in accordance with the related standards. This monitoring system includes many hardware and software designs and presents an efficient structure. PQ parameters are determined by signal processing algorithms that are applied to the current and voltage signals continuously obtained from the electrical power network. These signal processing algorithms are performed as embedded functions in the FPGA device. The PQ data obtained from the measurement points are transmitted to a server by UDP/IP communication protocol that is implemented in the FPGA device. The monitoring, reporting and permanently storing tasks are accomplished with the real-time automation software and web applications that running on the server computer. With its innovative software and hardware designs, the proposed monitoring approach presents a very useful monitoring structure that can be used in the PQ field.

  AtıfScopus37·WoS31

  İncele
• MakaleYalnız künye2013Scopus SJRQ2/r/munzur/4740
  Fault diagnosis system for series compensated transmission line based on wavelet transform and adaptive neuro-fuzzy inference system

  Eristi, Huseyin

  This paper proposes a new fault diagnosis approach based on combined wavelet transform and adaptive neuro-fuzzy inference system for fault section identification, classification and location in a series compensated transmission line. It performs an effective feature extraction approach based on norm entropy in order to obtain the features represented main frequency, harmonic and transient characteristics of the fault signals. The proposed method uses the samples of fault voltages and currents for one cycle duration from the inception of fault. The feasibility of the proposed method has been tested on a 400 kV, 300 km series compensated transmission line for all the ten types of faults using MATLAB/Simulink for a large data set of 23,436 fault cases comprising of all the 10 types of faults. Fault signals varying with fault resistance, fault inception angle, fault distance, load angle, percentage compensation level and source impedance are applied to the proposed algorithm. The results also indicate that the proposed method is robust to wide variation in system conditions and has higher fault diagnosis accuracy with regard to the other approaches in the literature for this problem. (C) 2012 Elsevier Ltd. All rights reserved.

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  AtıfWoS82·OpenAlex101

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