| Intelligent classification method of water faults for proton exchange membrane fuel cell based on improved SSA-DBN |
| 投稿时间：2023-02-10 修订日期：2023-07-01 |
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| English Abstract: |
| In order to realize efficient and rapid classification of water faults in proton exchange membrane fuel cell(PEMFC) system, a fault classification method of PEMFC based on improved sparrow search algorithm(SSA) optimizing deep confidence network(DBN) is proposed. The normalization process is adopted to eliminate the impact of different dimensions among fault data parameters, and the kernel principal component analysis is used to extract fault features from the data, which effectively reduces the dimensions of the original data and the calculation complexity and avoids the interference of low-contribution data on fault classification. The Cauchy-Gauss variation strategy is introduced to improve SSA, the parameters of DBN are optimized by SSA to determine the network structure, and the optimized DBN is used to quickly classify the water faults of PEMFC. Three thousand sets of PEMFC water fault data are tested. The results show that the proposed method can quickly and accurately identify three health states of PEMFC: normal state, membrane dry fault and flooding fault. The overall classification accuracy is 98.67% and the calculation time is 0.89 s. Compared with the support vector machine and the probabilistic neural network method, the fault classification accuracy of the proposed method is increased by 4 % and 3.34 % respectively, and the calculation time is reduced by 15.35 s and 0.35 s respectively. |
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| English and key words:proton exchange membrane fuel cell fault classification deep confidence network sparrow search algorithm kernel principal component analysis Cauchy-Gaussian variation strategy |
| 基金项目:国家自然科学基金资助项目(52007157,52077180)；四川省自然科学基金资助项目(2022NSFSC0269) |
| DOI：10.16081/j.epae.202310008 |
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