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Статті в журналах з теми "Fuel cell prognostics"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

Zhang, Dacheng, Catherine Cadet, Nadia Yousfi-Steiner, and Christophe Bérenguer. "Proton exchange membrane fuel cell remaining useful life prognostics considering degradation recovery phenomena." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 4 (May 27, 2018): 415–24. http://dx.doi.org/10.1177/1748006x18776825.

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Анотація:

This work explores the challenges of handling the recovery phenomena in the degradation behavior of the proton exchange membrane fuel cells, from the perspective of the prognostics. An adaptive prognostics and health management approach with additional knowledge, such as the electrochemical impedance spectroscopy, from the state of health characterization, is applied on two fuel cell stacks under both stationary and quasi-dynamic operating regimes. Some improvements in the prognostic performance are obtained in the view of the remaining useful life predictions by comparing with a classical particle filtering–based prognostic approach.

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2

Ma, Rui, Zhongliang Li, Elena Breaz, Chen Liu, Hao Bai, Pascal Briois, and Fei Gao. "Data-Fusion Prognostics of Proton Exchange Membrane Fuel Cell Degradation." IEEE Transactions on Industry Applications 55, no. 4 (July 2019): 4321–31. http://dx.doi.org/10.1109/tia.2019.2911846.

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3

Lechartier, Elodie, Elie Laffly, Marie-Cécile Péra, Rafael Gouriveau, Daniel Hissel, and Noureddine Zerhouni. "Proton exchange membrane fuel cell behavioral model suitable for prognostics." International Journal of Hydrogen Energy 40, no. 26 (July 2015): 8384–97. http://dx.doi.org/10.1016/j.ijhydene.2015.04.099.

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4

Jouin, Marine, Rafael Gouriveau, Daniel Hissel, Marie-Cécile Péra, and Noureddine Zerhouni. "Prognostics of PEM fuel cell in a particle filtering framework." International Journal of Hydrogen Energy 39, no. 1 (January 2014): 481–94. http://dx.doi.org/10.1016/j.ijhydene.2013.10.054.

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5

An, Dawn. "Prediction-Interval-Based Credibility Criteria of Prognostics Results for Practical Use." Processes 10, no. 3 (February 26, 2022): 473. http://dx.doi.org/10.3390/pr10030473.

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Анотація:

Prognostics is an AI-based technique for predicting the degrading/damaging behavior and remaining useful life (RUL) of a system, which facilitates a cost-effective and smart maintenance process. Many prognostics methods have been developed for various applications, such as bearings, aircraft engines, batteries, and fuel cell stacks. Once a new prognostics method is developed, it is evaluated using several metrics based on the true value of the RUL. However, these typical evaluation metrics are not applicable in real-world applications, as the true RUL cannot be known before the actual failure of a system. There are no ways to determine the reliability of prognostics results in practice. Therefore, this article presents the credibility criteria of prognostics results based on prediction intervals (PI), which are known values, unlike the true RUL. The PI-based credibility criteria for prognostics results are explained with two simple examples under different levels of noise to help with the decision making on prognostics results in the industrial field.

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6

Yue, Meiling, Zeina Al Masry, Samir Jemei, and Noureddine Zerhouni. "An online prognostics-based health management strategy for fuel cell hybrid electric vehicles." International Journal of Hydrogen Energy 46, no. 24 (April 2021): 13206–18. http://dx.doi.org/10.1016/j.ijhydene.2021.01.095.

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7

Yue, Meiling, Zhongliang Li, Robin Roche, Samir Jemei, and Noureddine Zerhouni. "Degradation identification and prognostics of proton exchange membrane fuel cell under dynamic load." Control Engineering Practice 118 (January 2022): 104959. http://dx.doi.org/10.1016/j.conengprac.2021.104959.

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8

Sutharssan, Thamo, Diogo Montalvao, Yong Kang Chen, Wen-Chung Wang, Claudia Pisac, and Hakim Elemara. "A review on prognostics and health monitoring of proton exchange membrane fuel cell." Renewable and Sustainable Energy Reviews 75 (August 2017): 440–50. http://dx.doi.org/10.1016/j.rser.2016.11.009.

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9

Chen, K., S. Laghrouche, and A. Djerdir. "Proton Exchange Membrane Fuel Cell Prognostics Using Genetic Algorithm and Extreme Learning Machine." Fuel Cells 20, no. 3 (April 21, 2020): 263–71. http://dx.doi.org/10.1002/fuce.201900085.

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10

Yue, Meiling, Samir Jemei, Noureddine Zerhouni, and Rafael Gouriveau. "Proton exchange membrane fuel cell system prognostics and decision-making: Current status and perspectives." Renewable Energy 179 (December 2021): 2277–94. http://dx.doi.org/10.1016/j.renene.2021.08.045.

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11

Jha, Mayank Shekhar, Mathieu Bressel, Belkacem Ould-Bouamama, and Genevieve Dauphin-Tanguy. "Particle filter based hybrid prognostics of proton exchange membrane fuel cell in bond graph framework." Computers & Chemical Engineering 95 (December 2016): 216–30. http://dx.doi.org/10.1016/j.compchemeng.2016.08.018.

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12

Mao, Lei, Ben Davies, and Lisa Jackson. "Application of the Sensor Selection Approach in Polymer Electrolyte Membrane Fuel Cell Prognostics and Health Management." Energies 10, no. 10 (September 29, 2017): 1511. http://dx.doi.org/10.3390/en10101511.

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13

Yue, Meiling, Samir Jemei, and Noureddine Zerhouni. "Health-Conscious Energy Management for Fuel Cell Hybrid Electric Vehicles Based on Prognostics-Enabled Decision-Making." IEEE Transactions on Vehicular Technology 68, no. 12 (December 2019): 11483–91. http://dx.doi.org/10.1109/tvt.2019.2937130.

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14

Jouin, Marine, Rafael Gouriveau, Daniel Hissel, Marie-Cecile Pera, and Noureddine Zerhouni. "Joint Particle Filters Prognostics for Proton Exchange Membrane Fuel Cell Power Prediction at Constant Current Solicitation." IEEE Transactions on Reliability 65, no. 1 (March 2016): 336–49. http://dx.doi.org/10.1109/tr.2015.2454499.

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15

Chang, Te-Jen, Shan-Jen Cheng, Chang-Hung Hsu, Jr-Ming Miao, and Shih-Feng Chen. "Prognostics for remaining useful life estimation in proton exchange membrane fuel cell by dynamic recurrent neural networks." Energy Reports 8 (November 2022): 9441–52. http://dx.doi.org/10.1016/j.egyr.2022.07.032.

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16

Marra, Dario, Marco Sorrentino, Cesare Pianese, and Boris Iwanschitz. "A neural network estimator of Solid Oxide Fuel Cell performance for on-field diagnostics and prognostics applications." Journal of Power Sources 241 (November 2013): 320–29. http://dx.doi.org/10.1016/j.jpowsour.2013.04.114.

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17

Meraghni, Safa, Labib Sadek Terrissa, Meiling Yue, Jian Ma, Samir Jemei, and Noureddine Zerhouni. "A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction." International Journal of Hydrogen Energy 46, no. 2 (January 2021): 2555–64. http://dx.doi.org/10.1016/j.ijhydene.2020.10.108.

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18

Li, Mingfei, Jiajian Wu, Zhengpeng Chen, Jiangbo Dong, Zhiping Peng, Kai Xiong, Mumin Rao, Chuangting Chen, and Xi Li. "Data-Driven Voltage Prognostic for Solid Oxide Fuel Cell System Based on Deep Learning." Energies 15, no. 17 (August 29, 2022): 6294. http://dx.doi.org/10.3390/en15176294.

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Анотація:

A solid oxide fuel cell (SOFC) is an innovative power generation system that is green, efficient, and promising for a wide range of applications. The prediction and evaluation of the operation state of a solid oxide fuel cell system is of great significance for the stable and long-term operation of the power generation system. Prognostics and Health Management (PHM) technology is widely used to perform preventive and predictive maintenance on equipment. Unlike prediction based on the SOFC mechanistic model, the combination of PHM and deep learning has shown wide application prospects. Therefore, this study first obtains an experimental dataset through short-term degradation experiments of a 1 kW SOFC system, and then proposes an encoder-decoder RNN-based SOFC state prediction model. Based on the experimental dataset, the model can accurately predict the voltage variation of the SOFC system. The prediction results of the four different prediction models developed are compared and analyzed, namely, long short-term memory (LSTM), gated recurrent unit (GRU), encoder–decoder LSTM, and encoder–decoder GRU. The results show that for the SOFC test set, the mean square error of encoder–decoder LSTM and encoder–decoder GRU are 0.015121 and 0.014966, respectively, whereas the corresponding error results of LSTM and GRU are 0.017050 and 0.017456, respectively. The encoder–decoder RNN model displays high prediction precision, which proves that it can improve the accuracy of prediction, which is expected to be combined with control strategies and further help the implementation of PHM in fuel cells.

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19

Mao, Lei, Lisa Jackson, and Tom Jackson. "Investigation of polymer electrolyte membrane fuel cell internal behaviour during long term operation and its use in prognostics." Journal of Power Sources 362 (September 2017): 39–49. http://dx.doi.org/10.1016/j.jpowsour.2017.07.018.

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20

Benaggoune, Khaled, Meiling Yue, Samir Jemei, and Noureddine Zerhouni. "A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell." Applied Energy 313 (May 2022): 118835. http://dx.doi.org/10.1016/j.apenergy.2022.118835.

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21

Alboghobeish, Mohammad, Andrea Monforti Ferrario, Davide Pumiglia, Massimiliano Della Pietra, Stephen J. McPhail, Sergii Pylypko, and Domenico Borello. "Developing an Automated Tool for Quantitative Analysis of the Deconvoluted Electrochemical Impedance Response of a Solid Oxide Fuel Cell." Energies 15, no. 10 (May 18, 2022): 3702. http://dx.doi.org/10.3390/en15103702.

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Анотація:

Despite being commercially available, solid oxide fuel cell (SOFC) technology requires further study to understand its physicochemical processes for diagnostics, prognostics, and quality assurance purposes. Electrochemical impedance spectroscopy (EIS), a widely used characterization technique for SOFCs, is often accompanied by the distribution of relaxation times (DRT) as a method for deconvoluting the contribution of each physicochemical process from the aggregated impedance response spectra. While EIS yields valuable information for the operation of SOFCs, the quantitative analysis of the DRT and its shifts remains cumbersome. To address this issue, and to create a replicable benchmark for the assessment of DRT results, a custom tool was developed in MATLAB to numerically analyze the DRT spectra, identify the DRT peaks, and assess their deviation in terms of peak frequency and DRT amplitude from nominal operating conditions. The preliminary validation of the tool was carried out by applying the tool to an extensive experimental campaign on 23 SOFC button-sized samples from three production batches in which EIS measurements were performed in parametric operating conditions. It was concluded that the results of the automated analysis via the developed tool were in accordance with the qualitative analysis of previous studies. It is capable of providing adequate additional quantitative results in terms of DRT shifts for further analysis and provides the basis for better interoperability of DRT analyses between laboratories.

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22

Cheng, Shan-Jen, Wen-Ken Li, Te-Jen Chang, and Chang-Hung Hsu. "Data-Driven Prognostics of the SOFC System Based on Dynamic Neural Network Models." Energies 14, no. 18 (September 15, 2021): 5841. http://dx.doi.org/10.3390/en14185841.

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Анотація:

Prognostics technology is important for the sustainability of solid oxide fuel cell (SOFC) system commercialization, i.e., through failure prevention, reliability assessment, and the remaining useful life (RUL) estimation. To solve SOFC system issues, data-driven prognostics methods based on the dynamic neural network (DNN), one of non-linear models, were investigated in this study. Based on DNN model types, the neural network autoregressive (NNARX) model with external inputs, the neural network autoregressive moving average (NNARMAX) model with external inputs, and the neural network output error (NNOE) were utilized to predict the degradation trend and estimate the RUL. First, the degradation trend prediction was executed to evaluate the correctness of the proposed DNN model structures in the first learning phase. Then, the RUL was estimated on the basis of the degradation trend of the NN models in the second inference phase. The comparison test results show the prediction accuracy of the NNARX model is higher and the RUL estimation can be given within a smaller relative error than the NNARMAX and NNOE models. The evaluation criteria of the root mean square error and mean absolute error of the NNARX model are the smallest among these three models. Therefore, the proposed NNARX model can effectively and precisely provide degradation trend prediction and RUL estimation of the SOFC system.

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23

Chrétien, Stéphane, Nathalie Herr, Jean-Marc Nicod, and Christophe Varnier. "Post-Prognostics Decision for Optimizing the Commitment of Fuel Cell Systems**This work has been supported by the Labex ACTION project (contract “ANR-11-LABX-0001-01”)." IFAC-PapersOnLine 49, no. 28 (2016): 168–73. http://dx.doi.org/10.1016/j.ifacol.2016.11.029.

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24

Wu, XiaoJuan, and Qianwen Ye. "Fault diagnosis and prognostic of solid oxide fuel cells." Journal of Power Sources 321 (July 2016): 47–56. http://dx.doi.org/10.1016/j.jpowsour.2016.04.080.

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25

Bressel, Mathieu, Mickael Hilairet, Daniel Hissel, and Belkacem Ould Bouamama. "Extended Kalman Filter for prognostic of Proton Exchange Membrane Fuel Cell." Applied Energy 164 (February 2016): 220–27. http://dx.doi.org/10.1016/j.apenergy.2015.11.071.

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26

Liu, Hao, Jian Chen, Ming Hou, Zhigang Shao, and Hongye Su. "Data-based short-term prognostics for proton exchange membrane fuel cells." International Journal of Hydrogen Energy 42, no. 32 (August 2017): 20791–808. http://dx.doi.org/10.1016/j.ijhydene.2017.06.180.

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27

Liu, Hao, Jian Chen, Daniel Hissel, and Hongye Su. "Short-Term Prognostics of PEM Fuel Cells: A Comparative and Improvement Study." IEEE Transactions on Industrial Electronics 66, no. 8 (August 2019): 6077–86. http://dx.doi.org/10.1109/tie.2018.2873105.

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28

Liu, Hao, Jian Chen, Chuyan Zhu, Hongye Su, and Ming Hou. "Prognostics of Proton Exchange Membrane Fuel Cells Using A Model-based Method." IFAC-PapersOnLine 50, no. 1 (July 2017): 4757–62. http://dx.doi.org/10.1016/j.ifacol.2017.08.947.

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29

Jouin, Marine, Rafael Gouriveau, Daniel Hissel, Marie Cécile Péra, and Noureddine Zerhouni. "Prognostics of PEM fuel cells under a combined heat and power profileÕ." IFAC-PapersOnLine 48, no. 3 (2015): 26–31. http://dx.doi.org/10.1016/j.ifacol.2015.06.053.

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30

Zhu, Li, and Junghui Chen. "Prognostics of PEM fuel cells based on Gaussian process state space models." Energy 149 (April 2018): 63–73. http://dx.doi.org/10.1016/j.energy.2018.02.016.

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31

Wu, Xiaojuan, Liangfei Xu, Junhao Wang, Danan Yang, Fusheng Li, and Xi Li. "A prognostic-based dynamic optimization strategy for a degraded solid oxide fuel cell." Sustainable Energy Technologies and Assessments 39 (June 2020): 100682. http://dx.doi.org/10.1016/j.seta.2020.100682.

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32

Cheng, Yujie, Noureddine Zerhouni, and Chen Lu. "A hybrid remaining useful life prognostic method for proton exchange membrane fuel cell." International Journal of Hydrogen Energy 43, no. 27 (July 2018): 12314–27. http://dx.doi.org/10.1016/j.ijhydene.2018.04.160.

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33

Wang, Chu, Zhongliang Li, Rachid Outbib, Manfeng Dou, and Dongdong Zhao. "Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells." Applied Energy 305 (January 2022): 117918. http://dx.doi.org/10.1016/j.apenergy.2021.117918.

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34

Liu, Hao, Jian Chen, Daniel Hissel, Jianguo Lu, Ming Hou, and Zhigang Shao. "Prognostics methods and degradation indexes of proton exchange membrane fuel cells: A review." Renewable and Sustainable Energy Reviews 123 (May 2020): 109721. http://dx.doi.org/10.1016/j.rser.2020.109721.

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35

Pan, Rui, Duo Yang, Yujie Wang, and Zonghai Chen. "Performance degradation prediction of proton exchange membrane fuel cell using a hybrid prognostic approach." International Journal of Hydrogen Energy 45, no. 55 (November 2020): 30994–1008. http://dx.doi.org/10.1016/j.ijhydene.2020.08.082.

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36

Xie, Renyou, Rui Ma, Sicheng Pu, Liangcai Xu, Dongdong Zhao, and Yigeng Huangfu. "Prognostic for fuel cell based on particle filter and recurrent neural network fusion structure." Energy and AI 2 (November 2020): 100017. http://dx.doi.org/10.1016/j.egyai.2020.100017.

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37

Zuo, Jian, Hong Lv, Daming Zhou, Qiong Xue, Liming Jin, Wei Zhou, Daijun Yang, and Cunman Zhang. "Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application." Applied Energy 281 (January 2021): 115937. http://dx.doi.org/10.1016/j.apenergy.2020.115937.

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38

Jacome, Andres, Daniel Hissel, Vincent Heiries, Mathias Gerard, and Sebastien Rosini. "Prognostic methods for proton exchange membrane fuel cell under automotive load cycling: a review." IET Electrical Systems in Transportation 10, no. 4 (December 1, 2020): 369–75. http://dx.doi.org/10.1049/iet-est.2020.0045.

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39

Zhou, Daming, Fei Gao, Elena Breaz, Alexandre Ravey, and Abdellatif Miraoui. "Degradation prediction of PEM fuel cell using a moving window based hybrid prognostic approach." Energy 138 (November 2017): 1175–86. http://dx.doi.org/10.1016/j.energy.2017.07.096.

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40

Wang, Penghao, Hao Liu, Ming Hou, Limin Zheng, Yue Yang, Jiangtao Geng, Wei Song, and Zhigang Shao. "Estimating the Remaining Useful Life of Proton Exchange Membrane Fuel Cells under Variable Loading Conditions Online." Processes 9, no. 8 (August 21, 2021): 1459. http://dx.doi.org/10.3390/pr9081459.

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Анотація:

The major challenges for the commercialization of proton exchange membrane fuel cells (PEMFCs) are durability and cost. Prognostics and health management technology enable appropriate decisions and maintenance measures by estimating the current state of health and predicting the degradation trend, which can help extend the life and reduce the maintenance costs of PEMFCs. This paper proposes an online model-based prognostics method to estimate the degradation trend and the remaining useful life of PEMFCs. A non-linear empirical degradation model is proposed based on an aging test, then three degradation state variables, including degradation degree, degradation speed and degradation acceleration, can be estimated online by the particle filter algorithm to predict the degradation trend and remaining useful life. Moreover, a new health indicator is proposed to replace the actual variable loading conditions with the simulated constant loading conditions. Test results using actual aging data show that the proposed method is suitable for online remaining useful life estimation under variable loading conditions. In addition, the proposed prognostics method, which considers the activation loss and the ohmic loss to be the main factors leading to the voltage degradation of PEMFCs, can predict the degradation trend and remaining useful life at variable degradation accelerations.

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41

Zhang, Dacheng, Xinru Li, Wei Wang, and Zhengang Zhao. "Internal Characterization-Based Prognostics for Micro-Direct-Methanol Fuel Cells under Dynamic Operating Conditions." Sensors 22, no. 11 (June 1, 2022): 4217. http://dx.doi.org/10.3390/s22114217.

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Micro-direct-methanol fuel cells (μDMFCs) use micro-electro mechanical system (MEMS) technology, which offers high energy density, portable use, quick replenishment, and free fuel reforming and purification. However, the μDMFC is limited by a short effective service life due to the membrane electrode’s deterioration in electrochemical reactions. This paper presents a health status assessment and remaining useful life (RUL) prediction approach for μDMFC under dynamic operating conditions. Rather than making external observations, an internal characterization is used to describe the degradation indicator and to overcome intrusive influences in operation. Then, a Markov-process-based usage behavior prediction mechanism is proposed to account for the randomness of real-world operation. The experimental results show that the proposed degradation indicator alleviates the reduction in μDMFC output power degradation behavior caused by the user loading profile. Compared with the predictions of RUL using traditional external observation, the proposed approach achieved superior prognostic performance in both accuracy and precision.

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42

Xia, Zetao, Yining Wang, Longhua Ma, Yang Zhu, Yongjie Li, Jili Tao, and Guanzhong Tian. "A Hybrid Prognostic Method for Proton-Exchange-Membrane Fuel Cell with Decomposition Forecasting Framework Based on AEKF and LSTM." Sensors 23, no. 1 (December 24, 2022): 166. http://dx.doi.org/10.3390/s23010166.

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Анотація:

Durability and reliability are the major bottlenecks of the proton-exchange-membrane fuel cell (PEMFC) for large-scale commercial deployment. With the help of prognostic approaches, we can reduce its maintenance cost and maximize its lifetime. This paper proposes a hybrid prognostic method for PEMFCs based on a decomposition forecasting framework. Firstly, the original voltage data is decomposed into the calendar aging part and the reversible aging part based on locally weighted regression (LOESS). Then, we apply an adaptive extended Kalman filter (AEKF) and long short-term memory (LSTM) neural network to predict those two components, respectively. Three-dimensional aging factors are introduced in the physical aging model to capture the overall aging trend better. We utilize the automatic machine-learning method based on the genetic algorithm to train the LSTM model more efficiently and improve prediction accuracy. The aging voltage is derived from the sum of the two predicted voltage components, and we can further realize the remaining useful life estimation. Experimental results show that the proposed hybrid prognostic method can realize an accurate long-term voltage-degradation prediction and outperform the single model-based method or data-based method.

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43

Ma, Tiancai, Jianmiao Xu, Ruitao Li, Naiyuan Yao, and Yanbo Yang. "Online Short-Term Remaining Useful Life Prediction of Fuel Cell Vehicles Based on Cloud System." Energies 14, no. 10 (May 13, 2021): 2806. http://dx.doi.org/10.3390/en14102806.

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Анотація:

The durability of automotive fuel cells is one of the main factors restricting their commercial application. Therefore, establishing a remaining useful life (RUL) prediction model and developing an online operational method to apply it to the RUL optimization of fuel cell vehicles is an urgent academic problem. In this work, a short-term RUL prediction model and an online operation scheme for fuel cell vehicles are proposed. Firstly, based on historical data of a fuel cell bus under multiple conditions, the daily mode of stack voltage under a 75 A operation condition was selected as a health indicator that could better reflect the health status of a fuel cell stack. Then, an adaptive locally weighted scatterplot smoothing (LOWESS) algorithm was developed to adjust the most appropriate step size to smooth the original data automatically. Furthermore, for better prediction accuracy and stronger adaptability, a short-term RUL prediction model consisting of the adaptive LOWESS and bi-directional long short-term memory was established. Finally, an online operation scheme of the RUL prediction model based on a cloud system gave the model a strong powerful practicability. After validation, this work demonstrated good application prospects in the prognostic and health management of automotive fuel cells.

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Wu, Xiaojuan, Liangfei Xu, Junhao Wang, Danan Yang, Mingtao Zhang, and Xi Li. "Discharge performance recovery of a solid oxide fuel cell based on a prognostic-based control strategy." Journal of Power Sources 480 (December 2020): 229102. http://dx.doi.org/10.1016/j.jpowsour.2020.229102.

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Yali Xiong, Xu Cheng, Z. J. Shen, Chunting Mi, Hongjie Wu, and V. K. Garg. "Prognostic and Warning System for Power-Electronic Modules in Electric, Hybrid Electric, and Fuel-Cell Vehicles." IEEE Transactions on Industrial Electronics 55, no. 6 (June 2008): 2268–76. http://dx.doi.org/10.1109/tie.2008.918399.

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Zhang, Xiaochen, Zhenyu He, Zhongliang Zhan, and Te Han. "Performance degradation analysis and fault prognostics of solid oxide fuel cells using the data-driven method." International Journal of Hydrogen Energy 46, no. 35 (May 2021): 18511–23. http://dx.doi.org/10.1016/j.ijhydene.2021.01.126.

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Javed, Kamran, Rafael Gouriveau, Noureddine Zerhouni, and Daniel Hissel. "Prognostics of Proton Exchange Membrane Fuel Cells stack using an ensemble of constraints based connectionist networks." Journal of Power Sources 324 (August 2016): 745–57. http://dx.doi.org/10.1016/j.jpowsour.2016.05.092.

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Wang, Penghao, Hao Liu, Jian Chen, Xiaoping Qin, Werner Lehnert, Zhigang Shao, and Ruiyu Li. "A novel degradation model of proton exchange membrane fuel cells for state of health estimation and prognostics." International Journal of Hydrogen Energy 46, no. 61 (September 2021): 31353–61. http://dx.doi.org/10.1016/j.ijhydene.2021.07.004.

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Zhang, Zhendong, Ya-Xiong Wang, Hongwen He, and Fengchun Sun. "A short- and long-term prognostic associating with remaining useful life estimation for proton exchange membrane fuel cell." Applied Energy 304 (December 2021): 117841. http://dx.doi.org/10.1016/j.apenergy.2021.117841.

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Hughes, Caryn S., Mick D. Brown, Peter Gardner, Johnathan H. Shanks, Noel W. Clarke, and Melody Jimenez-Hernandez. "Renal cell carcinoma: A prognostic target for spectral pathology." Journal of Clinical Oncology 31, no. 6_suppl (February 20, 2013): 459. http://dx.doi.org/10.1200/jco.2013.31.6_suppl.459.

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Анотація:

459 Background: In contrast to standard pathology, where stained cells and tissues are examined visually under a microscope, spectral pathology is the study of changes in cellular composition of unstained tissue using molecular fingerprint techniques, such as infrared and Raman spectroscopy. Hyperspectral images are collected which provide not only spatial information but also data of high dimensionality, probing the global biochemistry of the biological specimen in the mid-infrared range. Methods: 9 paraffin-embedded, formalin-fixed clear cell renal carcinoma tissue sections (8μm thick) of different grade, stage and with respective matched pairs, were floated onto infrared-transmitting substrates. After dewaxing, Fourier transform infrared imaging spectroscopy was performed on a Varian 670-IR spectrometer coupled with a Varian 620-IR imaging microscope (Agilent Technologies, CA) equipped with a 128×128 pixel Mercury-Cadmium-Telluride focal planar array detector. A hyperspectral image is obtained whereby each pixel records an infrared spectrum at a pixel resolution of ~5.5x5.5μm. Mosaic images were captured of full tissue specimens (one imaging tile equates to a tissue sampling area of ~700×700μm). Results: We have been able to detect the phenotype of ccRCC and observed a diminishing glycogen-rich signal during tumour progression towards high grade tissue. The spectral characteristics of ccRCC have been correlated with H and E, PAS, and CA-IX staining. Conclusions: Cancer metabolism can be tracked by lipid and glycogen infrared bands that carry a unique spectral signature, depending on tumour aggressiveness. The future focus is to generate a prognostic model for small renal masses.

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