针对深度残差网络（Deep residual network, ResNet）无法在噪声环境下精确诊断的问题，提出了一种基于直接快速迭代滤波（Direct fast iterative filtering，DFIF）和自适应深度残差网络（Adaptive deep residual network, AResNet）的方法，并将其应用于噪声环境下旋转机械的故障诊断中。首先，在采集的振动信号中增加不同强度的噪声，再经DFIF分解得到若干个本征模态函数（Intrinsic mode function, IMF）分量，选取综合评价指标值最小的IMF分量作为输入样本。其次，提出了自适应残差单元（Adaptive residual building unit, ARBU），ARBU通过计算各个通道的最优系数，自适应地放大故障敏感特征和抑制无关特征，能够更好地替代传统的残差单元（Residual building unit, RBU）。最后，基于ARBU构造AResNet，输入样本经过AResNet得到故障诊断结果。将所提方法应用于噪声背景下旋转机械的故障诊断中，在两个不同数据集中进行了验证。研究结果表明，与现有方法相比，所提方法具有更高的噪声鲁棒性、稳定性和更优的计算效率，且能够更好地解决旋转机械在噪声背景下故障特征难以有效挖掘的问题。
Abstract
Aiming at the problem that deep residual network (ResNet) cannot diagnose accurately in noisy environment, a method based on direct fast iterative filtering (DFIF) and an adaptive deep residual network (AResNet) is proposed. The method is applied to the fault diagnosis of rotating machinery in noisy environment. Firstly, different intensities of noise are added to the collected vibration signal, and then several Intrinsic Mode Function (IMF) components are obtained through DFIF decomposition. The IMF component with the smallest comprehensive evaluation index value is selected as the input sample. Secondly, Adaptive Residual Building Unit (ARBU) is proposed, which identifies the optimal coefficients of each channel, adaptively amplifies fault sensitive features and suppresses irrelevant features, and can better replace traditional Residual Building Unit (RBU). Finally, AResNet is constructed based on ARBU, and the input samples are processed through AResNet to obtain fault diagnosis results. The proposed method is applied to the fault diagnosis of rotating machinery under noisy backgrounds and validated in two different data sets. The results indicate that the proposed method has higher noise robustness, stability and better computational efficiency compared to existing methods. And it can better solve the problem that it is difficult to effectively mine the fault features of rotating machinery under noise backgrounds.
关键词
故障诊断 /
旋转机械 /
深度残差网络 /
直接快速迭代滤波 /
噪声环境
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Key words
fault diagnosis /
rotating machinery /
deep residual network /
direct fast iterative filtering /
noise environment
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