Proton exchange membrane fuel cell(PEMFC)faults,especially dehydration and flooding,cause distinct changes in electrochemical behavior.Consequently,real-time monitoring is essential for early and accurate diagnosis.Ho...Proton exchange membrane fuel cell(PEMFC)faults,especially dehydration and flooding,cause distinct changes in electrochemical behavior.Consequently,real-time monitoring is essential for early and accurate diagnosis.However,acquiring real-world fault data is challenging,and the rarity of such faults results in severe class imbalance.This imbalance limits the performance,reliability,and practical applicability of conventional diag-nostic methods.To address these limitations,this study proposes a unified diagnostic framework that integrates multi-sine AC voltage response,boundary-aware resampling,and attention-guided generative modeling.The key innovations of the proposed approach include:(1)Enhanced fault separability through the first application of multi-sine AC voltage response under data imbalance,enabling real-time extraction of critical electrochemical spectral features for early-stage diagnosis;(2)Improved data balance and clearer class boundaries using synthetic minority oversampling with Tomek links,which oversamples minority classes and removes borderline samples;(3)Realistic minority class synthesis using a dual attention Wasserstein generative adversarial networks,where channel attention focuses on diagnostically relevant spectral features and temporal attention models the dynamic evolution of PEMFC electrochemical behavior,ensuring high-quality,diagnostically informative synthetic fault data.The integrated framework achieves 99.67%overall diagnostic accuracy and,under an extreme 1:200 class imbalance,outperforms state-of-the-art methods by 14%.This approach enables rapid,data-efficient PEMFC fault diagnosis,strengthening fault management and advancing the performance of energy systems.展开更多
为克服传统白鲸优化算法(Beluga Whale Optimization,BWO)在3-5-3多项式插值机械臂轨迹优化中存在的路径长、时间耗费高及易陷入局部最优的问题,本文提出了一种增强型白鲸-蝠鲼融合优化算法(Enhanced Beluga Whale and manta ray fusion...为克服传统白鲸优化算法(Beluga Whale Optimization,BWO)在3-5-3多项式插值机械臂轨迹优化中存在的路径长、时间耗费高及易陷入局部最优的问题,本文提出了一种增强型白鲸-蝠鲼融合优化算法(Enhanced Beluga Whale and manta ray fusion Optimization algorithm,EBWO).该算法以机械臂最优运动时间为目标,构建约束优化模型,并通过增广拉格朗日乘子法转化为无约束形式.首先,利用改进的对数非线性Halton混沌序列优化种群初始化,提高搜索多样性与质量;其次,设计多方向正余弦白鲸位置更新机制,增强开发阶段搜索能力;再次,在中期迭代阶段引入改进的蝠鲼旋风链式觅食策略,并结合Levy飞行机制构建新觅食因子,以强化局部开发与全局跳跃能力;最后,提出基于资源竞争耦合机制的自适应鲸落策略,并引入量子隧穿效应,以提升算法跳出局部最优的能力与收敛速度.实验结果表明:在3-5-3轨迹优化中,EBWO较于传统BWO将时间优化效果提升了8.69%,并且与未优化的轨迹相比,优化后的时间缩短了42.13%.这一结果验证了其在复杂优化任务时的有效性与实用性.展开更多
基金supported by Anhui Quality Infrastructure Standardi-zation Project(Grant No.2024MKSO7)Anhui Provincial Natural Sci-ence Foundation(Grant No.2208085UD03)National Natural Science Foundation of China(Grant No.52405142).
文摘Proton exchange membrane fuel cell(PEMFC)faults,especially dehydration and flooding,cause distinct changes in electrochemical behavior.Consequently,real-time monitoring is essential for early and accurate diagnosis.However,acquiring real-world fault data is challenging,and the rarity of such faults results in severe class imbalance.This imbalance limits the performance,reliability,and practical applicability of conventional diag-nostic methods.To address these limitations,this study proposes a unified diagnostic framework that integrates multi-sine AC voltage response,boundary-aware resampling,and attention-guided generative modeling.The key innovations of the proposed approach include:(1)Enhanced fault separability through the first application of multi-sine AC voltage response under data imbalance,enabling real-time extraction of critical electrochemical spectral features for early-stage diagnosis;(2)Improved data balance and clearer class boundaries using synthetic minority oversampling with Tomek links,which oversamples minority classes and removes borderline samples;(3)Realistic minority class synthesis using a dual attention Wasserstein generative adversarial networks,where channel attention focuses on diagnostically relevant spectral features and temporal attention models the dynamic evolution of PEMFC electrochemical behavior,ensuring high-quality,diagnostically informative synthetic fault data.The integrated framework achieves 99.67%overall diagnostic accuracy and,under an extreme 1:200 class imbalance,outperforms state-of-the-art methods by 14%.This approach enables rapid,data-efficient PEMFC fault diagnosis,strengthening fault management and advancing the performance of energy systems.
