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Wen T, Li J, Fei R, Hei X, Chen Z, Wang Z. Dual-input robust diagnostics for railway point machines via audio signals. NETWORK (BRISTOL, ENGLAND) 2024:1-22. [PMID: 38860469 DOI: 10.1080/0954898x.2024.2358955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 05/18/2024] [Indexed: 06/12/2024]
Abstract
Railway Point Machine (RPM) is a fundamental component of railway infrastructure and plays a crucial role in ensuring the safe operation of trains. Its primary function is to divert trains from one track to another, enabling connections between different lines and facilitating route selection. By judiciously deploying turnouts, railway systems can provide efficient transportation services while ensuring the safety of passengers and cargo. As signal processing technologies develop rapidly, taking the easy acquisition advantages of audio signals, a fault diagnosis method for RPMs is proposed by considering noise and multi-channel signals. The proposed method consists of several stages. Initially, the signal is subjected to pre-processing steps, including cropping and channel separation. Subsequently, the signal undergoes noise addition using the Random Length and Dynamic Position Noises Superposition (RDS) module, followed by conversion to a greyscale image. To enhance the data, Synthetic Minority Oversampling Technique (SMOTE) module is applied. Finally, the training data is fed into a Dual-input Attention Convolutional Neural Network (DIACNN). By employing various experimental techniques and designing diverse datasets, our proposed method demonstrates excellent robustness and achieves an outstanding classification accuracy of 99.73%.
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Affiliation(s)
- Tao Wen
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, P.R. China
| | - Jinke Li
- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi, P.R. China
| | - Rong Fei
- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi, P.R. China
| | - Xinhong Hei
- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi, P.R. China
| | - Zhiming Chen
- XINGYITONG Aerospace Technology (Nanjing) Co. Ltd., Nanjing, China
| | - Zhurong Wang
- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi, P.R. China
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Oulefki A, Himeur Y, Trongtirakul T, Amara K, Agaian S, Benbelkacem S, Guerroudji MA, Zemmouri M, Ferhat S, Zenati N, Atalla S, Mansoor W. Detection and analysis of deteriorated areas in solar PV modules using unsupervised sensing algorithms and 3D augmented reality. Heliyon 2024; 10:e27973. [PMID: 38532999 PMCID: PMC10963330 DOI: 10.1016/j.heliyon.2024.e27973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 03/04/2024] [Accepted: 03/08/2024] [Indexed: 03/28/2024] Open
Abstract
Solar Photovoltaic (PV) systems are increasingly vital for enhancing energy security worldwide. However, their efficiency and power output can be significantly reduced by hotspots and snail trails, predominantly caused by cracks in PV modules. This article introduces a novel methodology for the automatic segmentation and analysis of such anomalies, utilizing unsupervised sensing algorithms coupled with 3D Augmented Reality (AR) for enhanced visualization. The methodology outperforms existing segmentation techniques, including Weka and the Meta Segment Anything Model (SAM), as demonstrated through computer simulations. These simulations were conducted using the Cali-Thermal Solar Panels and Solar Panel Infrared Image Datasets, with evaluation metrics such as the Jaccard Index, Dice Coefficient, Precision, and Recall, achieving scores of 0.76, 0.82, 0.90, 0.99, and 0.76, respectively. By integrating drone technology, the proposed approach aims to revolutionize PV maintenance by facilitating real-time, automated solar panel detection. This advancement promises substantial cost reductions, heightened energy production, and improved performance of solar PV installations. Furthermore, the innovative integration of unsupervised sensing algorithms with 3D AR visualization opens new avenues for future research and development in the field of solar PV maintenance.
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Affiliation(s)
- Adel Oulefki
- University of Sharjah, Sharjah, United Arab Emirates
- Centre de Développement des Technologies Avancées (CDTA), Algiers, 16018, Algeria
| | - Yassine Himeur
- College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates
| | - Thaweesak Trongtirakul
- Faculty of Industrial Education Rajamangala University of Technology Phra Nakhon, Vachira Phayaban Dusit Bangkok 10300, Bangkok, 10300, Bangkok, Thailand
| | - Kahina Amara
- Centre de Développement des Technologies Avancées (CDTA), Algiers, 16018, Algeria
| | - Sos Agaian
- Dept. of Computer Science, College of Staten Island, 2800 Victory Blvd Staten Island, New York, 10314, NY, USA
| | - Samir Benbelkacem
- Centre de Développement des Technologies Avancées (CDTA), Algiers, 16018, Algeria
| | | | - Mohamed Zemmouri
- Université Kasdi Merbah, Ouargla, Ouargla, 30000, Ouargla, Algeria
| | - Sahla Ferhat
- Centre de Développement des Technologies Avancées (CDTA), Algiers, 16018, Algeria
| | - Nadia Zenati
- Centre de Développement des Technologies Avancées (CDTA), Algiers, 16018, Algeria
| | - Shadi Atalla
- College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates
| | - Wathiq Mansoor
- College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates
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Abstract
Solar energy is the fastest-growing clean and sustainable energy source, outperforming other forms of energy generation. Usually, solar panels are low maintenance and do not require permanent service. However, plenty of problems can result in a production loss of up to ~20% since a failed panel will impact the generation of a whole array. High-quality and timely maintenance of the power plant will reduce the cost of its repair and, most importantly, increase the life of the power plant and the total generation of electricity. Manual monitoring of panels is costly and time-consuming on large solar plantations; moreover, solar plantations located distantly are more complicated for humans to access. This paper presents deep learning-based photovoltaics fault detection techniques using thermal images obtained from an unmanned aerial vehicle (UAV) equipped with infrared sensors. We implemented the three most accurate segmentation models to detect defective panels on large solar plantations. The models employed in this work are DeepLabV3+, Feature Pyramid Network (FPN) and U-Net with different encoder architectures. The obtained results revealed intersection over union (IoU) of 79%, 85%, 86%, and dice coefficients of 87%, 92%, 94% for DeepLabV3+, FPN, and U-Net, respectively. The implemented models showed efficient performance and proved effective to resolve these challenges.
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Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review. ENERGIES 2022. [DOI: 10.3390/en15062055] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In recent years, aerial infrared thermography (aIRT), as a cost-efficient inspection method, has been demonstrated to be a reliable technique for failure detection in photovoltaic (PV) systems. This method aims to quickly perform a comprehensive monitoring of PV power plants, from the commissioning phase through its entire operational lifetime. This paper provides a review of reported methods in the literature for automating different tasks of the aIRT framework for PV system inspection. The related studies were reviewed for digital image processing (DIP), classification and deep learning techniques. Most of these studies were focused on autonomous fault detection and classification of PV plants using visual, IRT and aIRT images with accuracies up to 90%. On the other hand, only a few studies explored the automation of other parts of the procedure of aIRT, such as the optimal path planning, the orthomosaicking of the acquired images and the detection of soiling over the modules. Algorithms for the detection and segmentation of PV modules achieved a maximum F1 score (harmonic mean of precision and recall) of 98.4%. The accuracy, robustness and generalization of the developed algorithms are still the main issues of these studies, especially when dealing with more classes of faults and the inspection of large-scale PV plants. Therefore, the autonomous procedure and classification task must still be explored to enhance the performance and applicability of the aIRT method.
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Ahmed W, Hanif A, Kallu KD, Kouzani AZ, Ali MU, Zafar A. Photovoltaic Panels Classification Using Isolated and Transfer Learned Deep Neural Models Using Infrared Thermographic Images. SENSORS (BASEL, SWITZERLAND) 2021; 21:5668. [PMID: 34451108 PMCID: PMC8402304 DOI: 10.3390/s21165668] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 08/17/2021] [Accepted: 08/17/2021] [Indexed: 11/18/2022]
Abstract
Defective PV panels reduce the efficiency of the whole PV string, causing loss of investment by decreasing its efficiency and lifetime. In this study, firstly, an isolated convolution neural model (ICNM) was prepared from scratch to classify the infrared images of PV panels based on their health, i.e., healthy, hotspot, and faulty. The ICNM occupies the least memory, and it also has the simplest architecture, lowest execution time, and an accuracy of 96% compared to transfer learned pre-trained ShuffleNet, GoogleNet, and SqueezeNet models. Afterward, ICNM, based on its advantages, is reused through transfer learning to classify the defects of PV panels into five classes, i.e., bird drop, single, patchwork, horizontally aligned string, and block with 97.62% testing accuracy. This proposed approach can identify and classify the PV panels based on their health and defects faster with high accuracy and occupies the least amount of the system's memory, resulting in savings in the PV investment.
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Affiliation(s)
- Waqas Ahmed
- Department of Electrical Engineering, University of Wah, Wah Cantt 47040, Pakistan; (W.A.); (A.H.)
| | - Aamir Hanif
- Department of Electrical Engineering, University of Wah, Wah Cantt 47040, Pakistan; (W.A.); (A.H.)
| | - Karam Dad Kallu
- Department of Robotics and Intelligent Machine Engineering (RIME), School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST) H-12, Islamabad 44000, Pakistan;
| | - Abbas Z. Kouzani
- School of Engineering, Deakin University, Geelong, VIC 3216, Australia;
| | - Muhammad Umair Ali
- Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Korea
| | - Amad Zafar
- Department of Electrical Engineering, Islamabad Campus, University of Lahore, Islamabad 54590, Pakistan
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