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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%。

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王树宇 , 袁嫣红 , 张建义 . 基于对抗自编码模型的高速泵异常检测 [J ] . 机床与液压 , 2022 , 50 ( 7 ): 176 - 180 . DOI: 10.3969/j.issn.1001-3881.2022.07.032 http://doi.org/10.3969/j.issn.1001-3881.2022.07.032 针对传统大型旋转机械健康状态评估中过分依赖人工经验和对复杂信号的处理通用性较差的问题,基于对抗自编码模型提出一种误差阈值异常检测方法。直接利用设备振动信号进行特征提取与运行状态建模,利用正常状态下设备的振动状态数据建立分布模型;通过深度学习的方式学习振动数据的内在特征,并引入误差阈值作为故障预警的决策准则,实现设备运行状态的高效评估;以一台高转速离心泵为测试对象验证所提方法。结果表明:对抗自编码模型对异常数据的判断准确率能达到100%,该方法能够基于监测数据对旋转设备运行状态进行有效检测;相比于传统自编码神经网络,该方法的诊断准确度和精度大幅提高。

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徐活耀 , 陈里里 . 基于堆栈稀疏自编码器的滚动轴承故障诊断 [J ] . 机床与液压 , 2020 , 48 ( 14 ): 190 - 194 . DOI: 10.3969/j.issn.1001-3881.2020.14.041 http://doi.org/10.3969/j.issn.1001-3881.2020.14.041 针对提取有效滚动轴承特征和消除特征之间的冗余，提出一种基于堆栈稀疏自编码器和Softmax层构建的深度神经网络（DNN）用于轴承故障诊断。首先从振动信号提取12个统计特征和6个时频域特征，然后将获得的特征用于构建18维特征向量；高维特征向量通过堆栈稀疏自编码器逐层贪婪学习获得无冗余的高级特征；最后将高级特征输入Softmax分类层进行轴承故障诊断。实验结果表明：相比于传统BP和SVM分类器，DNN能更准确地识别滚动轴承故障类型。

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