Bayesian optimized deep convolutional network for bearing diagnosis
Bayesian optimized deep convolutional network for bearing diagnosis
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DOI：
10.1007/s00170-020-05390-y
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发表时间：
2020-05
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影响因子：
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通讯作者：
Yanfei Lu;Zengyan Wang;Rui Xie;Jialin Zhang;Z. Pan;S. Liang
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作者：
Yanfei Lu;Zengyan Wang;Rui Xie;Jialin Zhang;Z. Pan;S. Liang
滚动轴承故障信号的成功诊断依赖于对轴承部件内部存在的早期故障的准确评估。由于系统的不完善和数据采集设备之间的干扰，故障信号被噪声严重掩盖。因此，为了提取故障信息，信号处理技术被广泛应用于轴承诊断中。虽然许多研究致力于寻找代表性的功能，以表明轴承元件的损坏，缺陷尺寸和采集的振动信号之间的相关性还没有得到适当的建立。近年来，深度学习在轴承诊断中得到了广泛的应用。通常，未处理的信号直接输入到深度学习模型中，神经网络在优化过程中提取有用的特征。到目前为止，从信号中选择特征是任意的，这并不能对诊断和预后产生太多的见解。此外，这些特征可能包含噪声信息，这可能会恶化诊断或预后结果，而使用预处理数据的效果尚未得到充分探讨。在本文中，我们提出了一种创新的诊断模型，使用贝叶斯优化的深度卷积网络来诊断轴承的缺陷严重程度。采集到的信号进行初步处理，使用互补集成经验模式分解方法，以提取包含故障特征的频带。一个基于实验的缺陷尺寸估计方程被实现以基于信号和实验设置来计算缺陷尺寸。在获得估计的缺陷尺寸之后，实现深度卷积神经网络以分类缺陷严重性。采用贝叶斯算法对网络参数进行优化。该算法可用于各种旋转机械的健康状态诊断。
The successful diagnosis of the faulty signal in rolling element bearings relies on the accurate evaluation of the early fault present within the components of bearings. Because of system imperfection and interference between the data acquisition devices, the fault signal is heavily masked by noise. Hence, to extract the fault information, signal processing techniques are widely used in bearing diagnosis. Although numerous research have dedicated on finding representative features to indicate the damage of the bearing elements, the correlation between defect size and acquired vibration signal has not been properly established. In the recent few years, deep learning has been widely used in bearing diagnosis. In general, the unprocessed signal is directly input into the deep learning model and the neural network extracts useful features during the optimization process. Until now, the selection of features from signals is arbitrary which does not yield much insights into the diagnosis and prognosis. In addition, the features could contain noise information, which could possibly deteriorate diagnostic or prognostic results, while the effect of using preprocessed data has not been fully explored. In this paper, we present an innovative diagnosis model using the deep convolutional network with Bayesian optimization to diagnose the defect severity of bearings. The acquired signal is initially processed using the complementary ensemble empirical mode decomposition method to extract the frequency band containing the fault signature. An experimental based defect size estimation equation is implemented to calculate the defect size based on the signal and experimental setup. After the estimated defect size is obtained, the deep convolutional neural network is implemented to categorize the defect severity. The parameters of the network are optimized by the Bayesian algorithm. The proposed algorithm can be used for diagnosis of the health condition of various rotating machinery.