Degradation Mechanism Detection in Photovoltaic Backsheets by Fully Convolutional Neural Network
Abstract
Materials and devices age with time. Material aging and degradation has important implications for lifetime performance of materials and systems. While consensus exists that materials should be studied and designed for degradation, materials inspection during operation is typically performed manually by technicians. The manual inspection makes studies prone to errors and uncertainties due to human subjectivity. In this work, we focus on automating the process of degradation mechanism detection through the use of a fully convolutional deep neural network architecture (F-CNN). We demonstrate that F-CNN architecture allows for automated inspection of cracks in polymer backsheets from photovoltaic (PV) modules. The developed F-CNN architecture enabled an end-to-end semantic inspection of the PV module backsheets by applying a contracting path of convolutional blocks (encoders) followed by an expansive path of decoding blocks (decoders). First, the hierarchy of contextual features is learned from the input images by encoders. Next, these features are reconstructed to the pixel-level prediction of the input by decoders. The structure of the encoder and the decoder networks are thoroughly investigated for the multi-class pixel-level degradation type prediction for PV module backsheets. The developed F-CNN framework is validated by reporting degradation type prediction accuracy for the pixel level prediction atmore »
- Authors:
-
[3];
- Univ. at Buffalo, NY (United States). Mechanical and Aerospace Engineering Dept.
- Mechanical Engineering Tuskegee Univ., Tuskegee, AL (United States)
- Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Materials Science and Engineering
- Univ. at Buffalo, NY (United States). Mechanical and Aerospace Engineering Dept.; Univ. at Buffalo, NY (United States). Materials Design and Innovation Dept.
- Publication Date:
- Research Org.:
- UL, LLC, Northbrook, IL (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- OSTI Identifier:
- 1799274
- Grant/Contract Number:
- EE0007143
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Scientific Reports
- Additional Journal Information:
- Journal Volume: 9; Journal Issue: 1; Journal ID: ISSN 2045-2322
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 14 SOLAR ENERGY; Science & Technology - Other Topics; Computational methods; Computer science
Citation Formats
Zhang, Binbin, Grant, Joydan, Bruckman, Laura S., Wodo, Olga, and Rai, Rahul. Degradation Mechanism Detection in Photovoltaic Backsheets by Fully Convolutional Neural Network. United States: N. p., 2019.
Web. doi:10.1038/s41598-019-52550-6.
Zhang, Binbin, Grant, Joydan, Bruckman, Laura S., Wodo, Olga, & Rai, Rahul. Degradation Mechanism Detection in Photovoltaic Backsheets by Fully Convolutional Neural Network. United States. https://doi.org/10.1038/s41598-019-52550-6
Zhang, Binbin, Grant, Joydan, Bruckman, Laura S., Wodo, Olga, and Rai, Rahul. Wed .
"Degradation Mechanism Detection in Photovoltaic Backsheets by Fully Convolutional Neural Network". United States. https://doi.org/10.1038/s41598-019-52550-6. https://www.osti.gov/servlets/purl/1799274.
@article{osti_1799274,
title = {Degradation Mechanism Detection in Photovoltaic Backsheets by Fully Convolutional Neural Network},
author = {Zhang, Binbin and Grant, Joydan and Bruckman, Laura S. and Wodo, Olga and Rai, Rahul},
abstractNote = {Materials and devices age with time. Material aging and degradation has important implications for lifetime performance of materials and systems. While consensus exists that materials should be studied and designed for degradation, materials inspection during operation is typically performed manually by technicians. The manual inspection makes studies prone to errors and uncertainties due to human subjectivity. In this work, we focus on automating the process of degradation mechanism detection through the use of a fully convolutional deep neural network architecture (F-CNN). We demonstrate that F-CNN architecture allows for automated inspection of cracks in polymer backsheets from photovoltaic (PV) modules. The developed F-CNN architecture enabled an end-to-end semantic inspection of the PV module backsheets by applying a contracting path of convolutional blocks (encoders) followed by an expansive path of decoding blocks (decoders). First, the hierarchy of contextual features is learned from the input images by encoders. Next, these features are reconstructed to the pixel-level prediction of the input by decoders. The structure of the encoder and the decoder networks are thoroughly investigated for the multi-class pixel-level degradation type prediction for PV module backsheets. The developed F-CNN framework is validated by reporting degradation type prediction accuracy for the pixel level prediction at the level of 92.8%.},
doi = {10.1038/s41598-019-52550-6},
journal = {Scientific Reports},
number = 1,
volume = 9,
place = {United States},
year = {Wed Nov 06 00:00:00 EST 2019},
month = {Wed Nov 06 00:00:00 EST 2019}
}
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