Dynamic Feature Learning and Assembly Tightness Intelligent Monitoring of Bolted Joint Structure
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摘要: 自动特征提取在机械系统智能状态监测中起着至关重要的作用，可以自适应地从原始数据中学习特征并发现新的状态敏感特征。本文重点研究了不同深度的卷积神经网络（CNN）模型在没有先验知识的情况下从激励响应信号中挖掘代表信息和敏感特征的能力，并将螺栓连接结构的特征提取和装配紧度分类过程融合在一起。通过车架试验台螺栓连接转子激振实验数据验证该方法的有效性。结果表明，CNN方法自适应学习的特征可以表示信号与装配状态之间的复杂映射关系，并且比其他方法具有更高的准确率。Abstract: Automatic feature extraction plays a crucial role in the intelligent state monitoring of mechanical systems, which can adaptively learn features from raw data and discover new state-sensitive features. This research focuses on the ability of different depth convolution neural network (CNN) models to mine representative information and sensitive features from the excitation response signal without prior knowledge, and combine the feature extraction with assembly tightness classification process of the bolted structure. The effectiveness of the method is verified by excitation test data of car frame test bench bolt connection rotor. The results show that the feature learned adaptively by CNN can represent the complex mapping relationship between response signal and assembly state, and has higher accuracy than other methods.
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Key words:
- bolt /
- monitoring /
- convolution neural network /
- features extraction
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表 1 模型结构示意表
网络层 卷积核 输出大小
(宽度×深度)大小/步长 数目 卷积1 64×1/16×1 16 256×16 最大池化1 2×1/2×1 16 128×16 卷积2 3×1/1×1 32 128×32 最大池化2 2×1/2×1 32 64×32 卷积3 3×1/1×1 64 64×64 卷积4 3×1/1×1 64 64×64 最大池化3 2×1/2×1 64 32×64 卷积5 3×1/1×1 128 32×128 卷积6 3×1/1×1 128 32×128 最大池化4 2×1/2×1 128 16×128 卷积7 3×1/1×1 256 16×256 卷积8 3×1/1×1 256 16×256 最大池化5 2×1/2×1 256 8×256 全连接层1 2 048 1 2 048×1 全连接层2 1 024 1 1 024×1 Softmax 6 1 6 
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表 2 螺栓连接状态分类
螺栓连接状态 拧紧力矩 训练集/测试集数量 状态标签 正常装配 M1 6 600/35 1 个别螺栓松动 M2 6 600/35 2 局部螺栓松动 M3 6 600/35 3 整体轻微松动 M4 6 600/35 4 整体中度松动 M5 6 600/35 5 整体严重松动 M6 6 600/35 6
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表 3 不同规模CNN模型参数
模型名称 模型参数 Model 1 16, 32, 64, 64, 128, 128, 256, 256-2048-1024 Model 2 16, 32, 64, 64, 64-100 Model 3 16, 32-100
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表 4 不同方法的螺栓装配紧度评估准确率
方法 样本类型 样本数
(训练/测试)诊断
准确率SVM+随机特征量 时域特征 396/60 85% SVM+精选特征量 时频特征 396/60 90% 深层去噪自编码器方法 时域信号 39 600/210 94.285 7% CNN方法 时域信号 39 600/210 100%
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