Online remaining useful lifetime prediction of proton exchange membrane fuel cells using a novel robust methodology

Zhou, Daming; Al‐Durra, Ahmed; Zhang, Ke; Ravey, Alexandre; Gao, Fei

doi:10.1016/j.jpowsour.2018.06.098

Cited by 104 publications

(54 citation statements)

References 43 publications

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“…Step 3 Set the parameters and initialize fuzzy partition matrix. Step 4 Calculate membership matrix in formula (13). Step 5 Update cluster center in formula (14).…”

Section: Accuracy Assessmentmentioning

confidence: 99%

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A novel ensemble support vector machine model for land cover classification

Liu

Huang

2019

International Journal of Distributed Sensor Networks

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Nowadays, support vector machines are widely applied to land cover classification although this method is sensitive to parameter selection and noise samples. AdaBoost is an effective approach to find a highly accurate classifier by combining many weak and accurate classifiers. In this article, a novel ensemble support vector machine model that uses AdaBoost approach is proposed to mitigate the influence of noises and error parameters with focus on application on land cover classification. The key characteristics of this approach are that (1) a novel noise filtering scheme that avoids the noisy examples based on fuzzy clustering and principal component analysis algorithm is proposed to remove both attribute noises and class noises to achieve an optimal clean set and (2) support vector machine classifiers, based on the particle swarm optimization algorithm, are seen to component classifiers. We then combined finally individual prediction through AdaBoost algorithm to induce the final classification results on this new training set. A set of experiments is conducted on land cover classification for testing the performance of the proposed algorithm. Experimental results show that the classification accuracy can be increased using our proposed learning model, which results in the smallest generalization error compared with the other learning methods.

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“…Step 3 Set the parameters and initialize fuzzy partition matrix. Step 4 Calculate membership matrix in formula (13). Step 5 Update cluster center in formula (14).…”

Section: Accuracy Assessmentmentioning

confidence: 99%

“…In real-world datasets, class noises will also confuse a machine-learning algorithm in the training phase. 13,14 Usually, noises usually exist in classification problems, including class noises and attribute noises. Class noises are regarded as ineffective samples, namely, misclassification samples and contradictory samples.…”

Section: Introductionmentioning

confidence: 99%

A novel ensemble support vector machine model for land cover classification

Liu

Huang

2019

International Journal of Distributed Sensor Networks

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“…In order to ensure that the database can resist attacks, an effective marking algorithm must have strong robustness. Inspired by literature, [15][16][17] in this article, a one-way hash function is used as the marking algorithm, which always outputs a fixed length hash value for a certain length of input message, and the hash function is characterized by the easy computation of the forward calculation, and the difficulty of the reverse computer is greatly improved ( Figure 5).…”

Section: Generation Of Watermark Independent Component and Marking Ofmentioning

confidence: 99%

Research on database watermarking based on Independent Component Analysis and multiple rolling

Cao

Shi

2019

International Journal of Distributed Sensor Networks

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Digital watermarking is an important branch of information hiding technology research field. It is a technology that embeds identifiable data into digital works. In order to protect the copyright of digital products, the basic idea is to embed confidential information in digital products such as images, audio, and video to realize data fusion. The current digital watermarking technology is mainly concentrated in the field of multimedia information (such as images, audio, and video) and made good progress. In this article, based on the large database capacity, and much numerical data characteristics, we proposed a separate component analysis on the database watermarking method. First, this article uses a oneway hash function as the marking algorithm to determine the tuple according to the user's given key, the tuple primary key value, and the tuple scale that needs to be marked. Then the watermark is extracted through the key, and the watermarked information is taken out in the database. The matrix is separated by the ratio Independent Component Analysis algorithm, and the watermark is used to separate the matrix. This kind of watermark information is rolled up, and the information in the original database is kept independent of each other, and the embedded watermark information is changed by the smaller carrier. The Independent Component Analysis method is used to extract the watermark image, and the ratio Independent Component Analysis method is used to solve the problem of the influence of the uncertainty of the arrangement order. The experimental results show that the proposed method has a good detection effect.

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“…Thus, the data-driven methods have received widespread attention, such as autoregressive moving average (ARMA) model, support vector regression (SVR), and artificial neural networks. 14 In terms of RUL prediction of lithium-ion batteries, the AR model describes the future performance state as a combination of linear equations and random errors based on historical state observations. The advantages of the AR are simple calculation and low complexity.…”

Section: Introductionmentioning

confidence: 99%

A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks

Wang

Dong

Yang

2019

International Journal of Distributed Sensor Networks

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Lithium-ion battery has been widely used in various fields due to its excellent performance. How to accurately predict its current capacity throughout a battery full lifetime has been a key technology for power system management, assurance, and predictive maintenance. In order to overcome low precision problem in long-term prediction for lithium-ion battery capacity, this article proposes a multi-scale fusion prediction method based on ensemble empirical mode decomposition and nonlinear autoregressive models neural networks. The proposed method uses ensemble empirical mode decomposition to decompose the battery capacity measurement sequence to generate multiple intrinsic mode function components on different scales. Then, each component is predicted by nonlinear autoregressive neural networks; finally, the prediction results of each component are reconstructed to obtain the final battery capacity prediction sequence. Experimental results show that the proposed method has higher prediction accuracy and signal adaptability than single nonlinear autoregressive neural networks.

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Online remaining useful lifetime prediction of proton exchange membrane fuel cells using a novel robust methodology

Cited by 104 publications

References 43 publications

A novel ensemble support vector machine model for land cover classification

A novel ensemble support vector machine model for land cover classification

Research on database watermarking based on Independent Component Analysis and multiple rolling

A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks

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