支持向量机
频域
时域
特征提取
快速傅里叶变换
稳健性(进化)
方位(导航)
断层(地质)
计算机科学
算法
振动
时频分析
模式识别(心理学)
人工智能
计算机视觉
声学
生物化学
化学
物理
地震学
基因
地质学
滤波器(信号处理)
作者
Mei Zhang,Jie Yin,Wanli Chen
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:10: 85641-85654
被引量:9
标识
DOI:10.1109/access.2022.3198701
摘要
Accurate fault diagnosis of rolling bearings is necessary to ensure the safe and reliable operation of mechanical equipment. Aiming at the problem of low accuracy of rolling bearing fault diagnosis, a rolling bearing fault diagnosis algorithm based on time-frequency feature extraction and improved bat algorithm-support vector machine (IBA-SVM) model is proposed in this paper. In this algorithm, the feature of the vibration signal in time domain and the frequency spectrum signal obtained by fast Fourier transform (FFT) is extracted, and then the multi-dimensional scaling is used. Multiple dimensional scaling (MDS) algorithm is adopted to reduce the data dimension of eigenvalues to reduce the model complexity, and finally improves the iteration speed and diagnosis accuracy. The improved bat algorithm (IBA) algorithm is used to optimize the parameters of the support vector machine (SVM) model, and the optimal IBA-SVM diagnosis model is obtained for determining the fault type of the rolling bearing. The experimental results show that the accuracy of the proposed rolling bearing fault diagnosis method can reach 99.6667%, which is significantly higher than the state-of-the-art models, and its robustness is stronger. Compared with the existing that use the time-domain or frequency-domain features alone, the proposed algorithm that combines time-domain and frequency-domain features shows significantly improved accuracy in fault diagnosis.
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