作者:牛国君 ， 唐振浩 ， 王孟姣
Authors:NIU Guojun, TANG Zhenhao , WANG Mengjiao摘要:轴承工作在复杂的环境中 ,使得轴承振动信号具有 一定的非线性 。为了解决轴承故障诊断中的这 一 难题 ,提出 一种基于时频域特征提取的轴承故障深度建模方法 。首先提取振动信号时域中的描述性统计参数 ,然 后用快速傅里叶变换(fast fourier transform, FFT)将时域信号转换到频域 ,提取频域特征以及频域中的描述性统计 参数 ,使用卡方检验选择时频域组合特征集中的重要特征 。最后使用深度置信神经网络(deep belief network, DBN)对轴承故障进行分类 。在两个数据集上通过对比单域特征提取以及不同分类算法的故障诊断结果 ,验证了 所提方法能够有效的提高故障识别准确率。
Abstract:Bearing works in complex environment, which makes the bearing vibration signal have a certain nonlinearity. In order to solve this problem in bearing fault diagnosis, this study proposes a bearing fault depth modeling method based on time-frequency domain feature extraction. Firstly, the descriptive statistical parameters in the time domain of the vibration signal are extracted, and then the fast Fourier transform (FFT) is used to convert the time domain signal to the frequency domain. The frequency frequency domain features and descriptive statistical parameters in the frequency domain are extracted. Use the chi-square test to select important features in the time-frequency domain combined feature set. Finally, a deep belief network (DBN) is used to classify bearing faults. By comparing the single-domain feature extraction and the fault diagnosis results of different classification algorithms on the two datasets, it is verified that the proposed method can effectively improve the fault identification accuracy.

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