QIAN H Y , YU Y G , LIU J . Effects of module number and firing condition on charge thermal safety in gun chamber [J ] . Defence Technology , 2022 , 18 ( 1 ): 27 - 37 . DOI: 10.1016/j.dt.2020.11.003 http://doi.org/10.1016/j.dt.2020.11.003 Thermal safety of modular charge which is fed into and retained in the chamber after gun fires consecutively is first investigated with cook-off method. A two-dimensional cook-off model of modular charge in gun chamber is established and the cook-off process of modular charge in gun chamber is numerically simulated. Then the effects of module number and firing condition on charge thermal safety are evaluated by researching the cook-off response characteristics of modules. The results show that, under conditions of different module numbers the cook-off responses all occur on the module closest to the boundary of missile, and the single-base propellants located at the inner surface of cartridge ignite first. When the number of loaded module changes from 1 to 6, the cook-off response temperatures vary little, only in a small range of 478.1 K–482.4 K. The cook-off response times decrease logarithmically in the range of 211.2 s–166.7 s with the increasing length of residual air gap in gun chamber. The simulation results are well matched with the experimental data. Furthermore, different firing conditions have great influence on the cook-off response time, minor influence on the initial response position and little influence on the response temperature. Under the three conditions of consecutive 32 launches with 5 rounds/min, 43 launches with 1 round/min, and 41 launches with different firing frequencies, the cook-off response temperatures are 479.2 K, 481.1 K and 479.9 K respectively and the response times are 709.2 s, 211.2 s and 214.4 s respectively. The response position is near the middle area of the inner cartridge surface in the former condition and near the right area in the latter two conditions. © 2020 The Authors

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李良钰 , 苏铁熊 , 马富康 , 等 . 基于集合经验模态分解-支持向量机的高压共轨系统故障诊断方法 [J ] . 兵工学报 , 2022 , 43 ( 5 ): 992 - 1001 . DOI: 10.12382/bgxb.2021.0155 http://doi.org/10.12382/bgxb.2021.0155 柴油机高压共轨系统运行时轨压波动信号波动较大且非线性特征较为明显，使其故障诊断较为困难。针对高压共轨系统轨压信号状态参数难以提取与识别的问题，提出一种基于集合经验模态分解（EEMD）—支持向量机（SVM）的故障诊断方法。通过EEMD将轨压信号分解为一系列固有模态函数，利用过零率曲线确定的特征提取准则提取本征模态函数中的特征值。将提取的特征值输入SVM中进行故障类型的诊断。通过AME Sim软件仿真实验获得轨压信号，对比7种不 同的特征值选择方法，最终选取能量特征值构建特征值向量并进行识别和诊断结果分析，以验证该方法的正确性与准确性。结果表明：所提出的基于EEMD—SVM的高压共轨系统故障诊断方法能够对6种不同的运行状态进行状态识别，平均故障诊断正确率可达96.11%。

LI L Y , SU T X , MA F K , et al . Fault diagnosis method of high-pressure common rail system based on EEMD-SVM [J ] . Acta Armamentarii , 2022 , 43 ( 5 ): 992 - 1001 . (in Chinese) DOI: 10.12382/bgxb.2021.0155 http://doi.org/10.12382/bgxb.2021.0155 When the high-pressure common rail system for diesel engine is running， the rail pressure fluctuation signal fluctuates greatly and has obvious nonlinear characteristics， which makes the fault diagnosis more difficult. For the problem that the state parameters of rail pressure signal in high-pressure common rail system are difficult to extract and identify， a fault diagnosis method based on ensemble empirical mode decomposition (EEMD)-support vector machine (SVM) is proposed. The rail pressure signal is decomposed into a series of eigenmode functions by EEMD， and the eigenvalues in the eigenmode functions are extracted using the feature extraction criterion determined by the zero-crossing rate curve. The extracted eigenvalues are input into SVM for fault type diagnosis. The rail pressure signal is obtained through AMESim software simulation experiment， and seven different eigenvalue selection methods are compared. Finally， the energy eigenvalue is selected to construct the eigenvalue vector for identification， and the diagnosis results are analyzed to verify the correctness and accuracy of the proposed method. The results show that the proposed EEMD-SVM-based fault diagnosis method for high-pressure common rail system can be used to identify six different operating states， with the average fault diagnostic accuracy rate of 96.11%.

HOANG D T , KANG H J . A survey on deep learning based bearing fault diagnosis [J ] . Neurocomputing , 2019 , 335 : 327 - 335 . DOI: 10.1016/j.neucom.2018.06.078 http://doi.org/10.1016/j.neucom.2018.06.078 https://linkinghub.elsevier.com/retrieve/pii/S0925231218312657 https://linkinghub.elsevier.com/retrieve/pii/S0925231218312657

钱林方 , 孙乐 , 陈光宋 , 等 . 无位置传感器电机控制在火炮装填应用的关键技术 [J ] . 兵工学报 , 2022 , 43 ( 10 ): 2417 - 2428 .

QIAN L F , SUN L , CHEN G S , et al . Investigation of the sensorless motor control technology for gun autoloading [J ] . Acta Armamentarii , 2022 , 43 ( 10 ): 2417 - 2428 . (in Chinese) DOI: 10.12382/bgxb.2022.0330 http://doi.org/10.12382/bgxb.2022.0330 The modern automatic loading system for large caliber guns involves the collaborative control of multiple motors. The application of sensorless technology can significantly improve the reliability of driving systems. The rotor position identification of surface-mounted permanent-magnet synchronous motor at zero-/low-speeds is a difficult problem in sensorless control. To deal with it, an extended Kalman filter based on mechanical motion model is proposed to assist rotor position estimation. At the same time, the observer disturbance rejection technology is employed to eliminate the influence of disturbances such as model parameter uncertainty to rotor position identification, and to realize reliable start-stop control under zero/low speeds and heavy loads. The proposed method overcomes the limitation of the conventional rotor position estimation method based on the electrical model, greatly improves the load capacity of the sensorless technology at zero/low speeds, and realizes the performance comparable to that of the position sensor servo. This sensorless motor control technology is applied to the 155 mm gun loading system. Finally, the technology is verified by the operation simulation of the modular explosive loader and projectile loader as examples.

刘太素 , 钱林方 , 陈光宋 . 火炮输药机构小射角输药到位一致性稳健优化设计 [J ] . 兵工学报 , 2020 , 41 ( 8 ): 1473 - 1482 . DOI: 10.3969/j.issn.1000-1093.2020.08.001 http://doi.org/10.3969/j.issn.1000-1093.2020.08.001 针对火炮输药机构在小射角非满装药输药过程中到位一致性差的问题，提出基于稀疏混沌多项式展开的输药过程稳健优化设计方法。建立输药机构的动力学模型，并通过试验验证模型的正确性和有效性；基于稀疏混沌多项式展开求取不确定性输入参数对输药到位的代理模型；以模块药到位以及到位标准差最小为目标，建立输药过程的稳健优化设计模型，采用NSGA-Ⅱ优化算法对优化模型进行求解。计算结果表明：输药到位一致性得到了提高，且到位均方差较初始减小了26.74%，验证了该方法的有效性。

LIU T S , QIAN L F , CHEN G S . Robust optimal design of a propellant transport mechanism for in-place consistency with small-angle propellant transport [J ] . Acta Armamentarii , 2020 , 41 ( 8 ): 1473 - 1482 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2020.08.001 http://doi.org/10.3969/j.issn.1000-1093.2020.08.001 For a propellant transport mechanism with small-angle propellant transport and non-full charge transport, a robust optimal design method for propellant transport mechanism based on polynomial sparse chaos expansion (SPCE) is proposed. A dynamic model of a propellant transport mechanism is established, and the correctness and validity of the model are verified through experiment. A surrogate model of uncertainty input parameters for position of propellant transport is obtained using SPCE. Finally, a robust optimal design model of a propellant transport process is constructed with the target of minimizing the in-place and standard deviations. The optimization model is calculated with NSGA-Ⅱ algorithm. The results show that the in-place consistency is improved after optimization, and the standard deviation is reduced by 26.74% relative to initial deviation, thus proving the effectiveness of the method.

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XIAO D Y , HUANG Y X , QIN C J , et al . Transfer learning with convolutional neural networks for small sample size problem in machinery fault diagnosis [J ] . Proceedings of the Institution of Mechanical Engineers Part C:Journal of Mechanical Engineering Science , 2019 , 233 ( 14 ): 5131 - 5143 . DOI: 10.1177/0954406219840381 http://doi.org/10.1177/0954406219840381 http://journals.sagepub.com/doi/10.1177/0954406219840381 http://journals.sagepub.com/doi/10.1177/0954406219840381 Data-driven machinery fault diagnosis has gained much attention from academic research and industry to guarantee the machinery reliability. Traditional fault diagnosis frameworks are commonly under a default assumption: the training and test samples share the similar distribution. However, it is nearly impossible in real industrial applications, where the operating condition always changes over time and the quantity of the same-distribution samples is often not sufficient to build a qualified diagnostic model. Therefore, transfer learning, which possesses the capacity to leverage the knowledge learnt from the massive source data to establish a diagnosis model for the similar but small target data, has shown potential value in machine fault diagnosis with small sample size. In this paper, we propose a novel fault diagnosis framework for the small amount of target data based on transfer learning, using a modified TrAdaBoost algorithm and convolutional neural networks. First, the massive source data with different distributions is added to the target data as the training data. Then, a convolutional neural network is selected as the base learner and the modified TrAdaBoost algorithm is employed for the weight update of each training sample to form a stronger diagnostic model. The whole proposition is experimentally demonstrated and discussed by carrying out the tests of six three-phase induction motors under different operating conditions and fault types. Results show that compared with other methods, the proposed framework can achieve the highest fault diagnostic accuracy with inadequate target data.

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