在工程实践 中，旋转机械故障诊断常面临噪声干扰、故障样本稀缺以及工况变化等各种复杂情况，这给先验知识缺乏的数据驱动深度学习方法应用带来了新的挑战。传统基于小波分析的故障诊断方法可提取到故障丰富的先验知识，但固定(结构化)或单一的小波基难以直接适应复杂故障场景。针对上述问题，在传统多尺度小波包分析思想启发下，本文提出一种基于多尺度小波包启发卷积网络(multiscale wavelet packet-inspired convolutional network, MWPICNet)的端到端旋转机械故障诊断方法。MWPICNet在神经网络内部实现了时频域转换与滤波降噪、特征提取与分类过程的有机耦合。首先，通过交替使用多尺度小波包启发卷积层和软阈值激活层进行信号分解和非线性变换，逐层挖掘多尺度时频故障特征和过滤噪声冗余信息，其中每个多尺度小波包启发卷积层可近似看作在多个可学习小波基下对信号的单层小波包变换，软阈值激活层中用于稀疏化系数的阈值是可学习的；然后，设计频带加权层动态调整各频带通道的权重；最后，引入全局功率池化层提取有助于故障状态识别的判别性频带能量特征。在三种不同应用场景下分别采用对应的机械故障数据集进行案例研究，验证了所提模型在复杂故障场景下的可行性和有效性。
Abstract
In practical engineering, fault diagnosis of rotating machinery often faces various complex situations such as noise interference, limited fault samples and variable working conditions, which pose new challenges to the application of data-driven deep learning methods that lack prior knowledge. Traditional fault diagnosis methods based on wavelet analysis can extract rich prior knowledge of faults, but a fixed (structured) or single wavelet basis is difficult to directly adapt to complex fault scenarios. To address these issues, a multiscale wavelet packet-inspired convolutional network (MWPICNet) was proposed for fault diagnosis of rotating machinery in this paper, inspired by traditional multiscale wavelet packet analysis. The proposed MWPICNet internally coupled the time-frequency domain conversion with filtering denoising, feature extraction and classification. First, the multiscale wavelet packet-inspired convolutional (MWPIC) layer and soft-thresholding activation (ST) layer were alternately used for signal decomposition and nonlinear transformation, extracting multiscale time-frequency fault features and filtering out the noise layer by layer. Each MWPIC layer could be approximately seen as a single-layer wavelet packet transform of the signal under multiple learnable wavelet bases, and learnable thresholds in the ST layer were used to sparse the wavelet coefficients. Then, the frequency band weighting (FBW) layer was designed to dynamically adjust the weights of each frequency band channel. Finally, a global power pooling layer (GPP) was introduced to extract discriminative frequency band energy features that were helpful for fault identification. The efficacy of the proposed MWPICNet is verified through case studies designed for different complex scenarios on three fault diagnosis datasets.
关键词
小波包变换 /
卷积神经网络 /
多小波基融合 /
故障诊断
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Key words
wavelet packet transform /
convolutional neural network /
multiple wavelet bases fusion /
fault diagnosis
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