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陈子涵 . 基于多模态Transformer的机电作动器剩余寿命预测 [J ] . 兵工学报 , 2023 , 44 ( 10 ): 2920 - 2931 . DOI: 10.12382/bgxb.2022.0581 http://doi.org/10.12382/bgxb.2022.0581 机电作动器在航空航天装备中扮演着重要角色。针对机电作动器剩余寿命预测问题,提出一种基于多模态Transformer模型的机电作动器寿命预测方法。该方法直接使用多通道传感器数据作为输入,综合考虑多模态数据信息,并且不需要人工特征提取等预处理步骤。多模态Transformer模型利用多头自注意力机制从不同的表示子空间中自适应学习全局特征,能够避免传统深度学习方法难以学习全局特征的缺点。利用多模态Transformer的编码器部分并行提取多模态传感器时间序列中不同传感器的特征,并实时直接预测剩余使用寿命。采用由编码器和解码器组成的完整多模态Transformer模型预测机电作动器的关键性能参数,可同时更直观地预测关键寿命参数的退化过程。使用机电作动器全寿命试验数据验证该方法用于寿命预测的有效性。试验结果表明,所提方法能够准确地直接预测剩余寿命,同时预测关键性能参数的寿命退化过程。

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万芯炜 , 王晶 , 杨辉 , 等 . BP神经网络结合粒子群优化卡尔曼滤波的MEMS陀螺随机误差补偿方法 [J ] . 兵工学报 , 2023 , 44 ( 2 ): 556 - 565 . DOI: 10.12382/bgxb.2022.0110 http://doi.org/10.12382/bgxb.2022.0110 针对微机电系统(MEMS)陀螺仪随机误差相对较大、影响其精度这一问题,提出一种基于BP神经网络结合具有量子行为的粒子群优化(QPSO)算法优化卡尔曼滤波(KF)的补偿方法。采集MEMS陀螺和转台数据作为样本,采用BP神经网络进行训练,建立误差模型;利用训练好的模型对MEMS陀螺进行误差补偿;利用QPSO算法优化KF,以达到更好的降噪效果。实验结果表明,该方法较BP神经网络优化KF、QPSO优化KF与变分模态分解结合小波阈值去噪等方法去噪处理后的平均绝对误差(MAE)和均方误差(MSE)更小,具有更好的降噪效果。

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刘芳 , 李士伟 , 卢熹 , 等 . 基于PSO-CNN-XGBoost水下柱形装药峰值超压预测 [J ] . 兵工学报 , 2024 , 45 ( 5 ): 1602 - 1612 . DOI: 10.12382/bgxb.2023.0743 http://doi.org/10.12382/bgxb.2023.0743 为探索水下柱形装药结构、爆距等参数与水下柱形装药峰值超压的关系,将装药样本数据视为二维数据,建立粒子群优化(Particle Swarm Optimization, PSO)算法、一维卷积神经网络(1D Convolutional Neural Network,1DCNN)和极端梯度提升(Extreme Gradient Boosting, XGBoost)的水下柱形装药峰值超压融合预测算法。采用相关性分析与数据可视化方法,分析装药结构参数、爆距与峰值超压之间的关联关系。设计1DCNN深度网络挖掘不同长径比、爆距等参数与峰值超压之间的纵向时序关系。运用XGBoost算法寻找装药结构参数、爆距与峰值超压之间的横向非线性关系,提升小样本数据的预测精度。使用PSO算法优化1DCNN和XGBoost的超参数,获得最优算法结构。研究结果表明,在包含10种智能算法的对比实验中,PSO-CNN-XGBoost水下柱形装药峰值超压预测算法在精度、稳定性、拟合程度上均高于其他模型。

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JIAO K R , SHEN X J . Real-time evaluation model construction of GIS conductor thermal state based on PSO-BP neural network[C ] //Proceedings of the 2023 3rd International Conference on Electrical Engineering and Control Science ( IC2ECS ). Hangzhou,China:IEEE ,2023: 1690 - 1695 .