基于振动信号显著性序列的滚动轴承状态诊断方法研究

Rolling bearing state diagnosis method based on vibration signal significance sequence

为提升基于时域信号的滚动轴承状态诊断准确率,提出了一种基于振动信号显著性序列的滚动轴承状态诊断方法。首先,将采集得到的振动信号归一化后进行了傅里叶变换,得到对数幅度谱和平均对数谱,并将二者相减获得了信号的谱残差;然后,对谱残差通过傅里叶逆变换映射回时间域,得到了信号的显著性序列;最后,将显著性序列输入到状态诊断模型中,对滚动轴承运行状态进行了分类,实现了对滚动轴承的状态诊断。实验及研究结果表明:相对于原振动信号,显著性序列可以有效地提高分类准确率,特别是对信噪比(SNR)较差的振动信号,如混有-6 dB的高斯白噪声,以支持向量机(SVM)及卷积神经网络(CNN)分别作为状态诊断模型,显著性序列的状态诊断准确率较原振动信号可分别提高9%和10.75%;对于利用卷积神经网络的状态诊断模型,显著性序列还能有效缩短网络模型训练时间,提高系统的时效性。

In order to improve the accuracy rate of state diagnosis of rolling bearing based on time domain signals,a novel diagnosis system which combined significance sequence of vibration signals and machine learning was proposed.Firstly,the residual spectral of the signal was obtained by subtracting the logarithmic amplitude spectrum from the mean logarithmic amplitude spectrum which was acquired by analyzing the normalized vibration signals in frequency domain.Then,the spectral residual of the vibration signals was mapped back to the time domain by inverse Fourier transform to obtain the significance sequence.Finally,the state diagnosis model was used to classify the significance sequence to achieve fault detection of the bearing state.The experiment results indicate that the significant sequence can effectively improve the classification accuracy rate comparing with the original vibration signal,especially for the vibration signal mixed with the different signal noise ratio(SNR)of Gaussian white noise,such as mixed with white Gaussian noise of-6 dB,using support vector machine(SVM)or convolution neural network(CNN)as the state diagnosis model respectively,the significance sequence can respectively improve the accuracy rate of state diagnosis by 9%and 10.75%.What s more,this method can also effectively shorten the training time of the network model.