Photovoltaic Panels Classification Using Isolated and Transfer Learned Deep Neural Models Using Infrared Thermographic Images

Ahmed, Waqas; Hanif, Aamir; Kallu, Karam Dad; Kouzani, Abbas Z.; Ali, Muhammad Umair; Zafar, Amad

doi:10.3390/s21165668

Cited by 41 publications

(12 citation statements)

References 33 publications

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“…In terms of inspection efficiency, infrared thermography can rapidly give preliminary results about the inspected object, since it is a non-contact technique that enables the drawing of a whole scale picture, while the UT techniques only detect a specific area according to designed detectors [ 90 , 91 ]. However, the processing technique for the picture dataset will require pixel-by-pixel processes, which typically leads to issues in processing approaches, computation efficiency, and collected dataset storage [ 92 , 93 ]. As for the ultrasonic phased arrays technique, the scanning areas are limited to the areas of multiple probes, though it possesses all the advantages of conventional techniques containing only one or two probes.…”

Section: Technologies For Aluminum Window Inspectionmentioning

confidence: 99%

Deterioration Mechanisms and Advanced Inspection Technologies of Aluminum Windows

2022

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Aluminum windows are crucial components of building envelopes since they connect the indoor space to the external environment. Various external causes degrade or harm the functioning of aluminum windows. In this regard, inspecting the performance of aluminum windows is a necessary task to keep buildings healthy. This review illustrates the deterioration mechanisms of aluminum windows under various environmental conditions with an intention to provide comprehensive information for developing damage protection and inspection technologies. The illustrations reveal that moisture and chloride ions have the most detrimental effect on deteriorating aluminum windows in the long run, while mechanical loads can damage aluminum windows in a sudden manner. In addition, multiple advanced inspection techniques potential to benefit assessing aluminum window health state are discussed in order to help tackle the efficiency problem of traditional visual inspection. The comparison among those techniques demonstrates that infrared thermography can help acquire a preliminary defect profile of inspected windows, whereas ultrasonic phased arrays technology demonstrates a high level of competency in analyzing comprehensive defect information. This review also discusses the challenges in the scarcity of nanoscale corrosion information for insightful understandings of aluminum window corrosion and reliable window inspection tools for lifespan prediction. In this regard, molecular dynamics simulation and artificial intelligence technology are recommended as promising tools for better revealing the deterioration mechanisms and advancing inspection techniques, respectively, for future directions. It is envisioned that this paper will help upgrade the aluminum window inspection scheme and contribute to driving the construction of intelligent and safe cities.

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Section: Technologies For Aluminum Window Inspectionmentioning

confidence: 99%

Deterioration Mechanisms and Advanced Inspection Technologies of Aluminum Windows

2022

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“…In this case, many types of sensors can be used that rely on the information required by the ML developers. For example, image sensors, such as thermographic [46,47], X-ray [38,48], and electroluminescence cameras [49,50], are, in general, used to analyze the types of external defects related to degradation features by providing images of the surface of photovoltaic panels or cells. By comparison, traditional sensors such as I-V, P-V, temperature, and radiation sensors can be used to determine symptoms of both external and internal defects in the system.…”

Section: Technologiesof Detection Sensorsmentioning

confidence: 99%

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

et al. 2021

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To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.

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“…Because multiple ellipsoids are learned in parallel in EMDO, an effective approach is designed to adaptively determine the number of synthetic samples to be generated in every ellipsoid. Similar ideas that design suitable algorithms to search for model parameters for specific applications are shown in [ 50 , 51 , 52 ].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Mahalanobis Distance-Based Oversampling for Multi-Class Imbalanced Data Classification

Yao

Lin

2021

Sensors

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The number of sensing data are often imbalanced across data classes, for which oversampling on the minority class is an effective remedy. In this paper, an effective oversampling method called evolutionary Mahalanobis distance oversampling (EMDO) is proposed for multi-class imbalanced data classification. EMDO utilizes a set of ellipsoids to approximate the decision regions of the minority class. Furthermore, multi-objective particle swarm optimization (MOPSO) is integrated with the Gustafson–Kessel algorithm in EMDO to learn the size, center, and orientation of every ellipsoid. Synthetic minority samples are generated based on Mahalanobis distance within every ellipsoid. The number of synthetic minority samples generated by EMDO in every ellipsoid is determined based on the density of minority samples in every ellipsoid. The results of computer simulations conducted herein indicate that EMDO outperforms most of the widely used oversampling schemes.

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Photovoltaic Panels Classification Using Isolated and Transfer Learned Deep Neural Models Using Infrared Thermographic Images

Cited by 41 publications

References 33 publications

Deterioration Mechanisms and Advanced Inspection Technologies of Aluminum Windows

Deterioration Mechanisms and Advanced Inspection Technologies of Aluminum Windows

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

Evolutionary Mahalanobis Distance-Based Oversampling for Multi-Class Imbalanced Data Classification

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