For the problem of inconsistent quantitative standards for running status analysis of rolling bearings, this paper uses principal component analysis (PCA) to extract a new index F, which is the joint parameters of time domain and frequency domain, and by establishing the value of F to analyze the running states of the rolling bearings. Firstly, the acceleration sensors are used to collect the vibration signal of the whole life cycle of the rolling bearings. Secondly, empirical mode decomposition (EMD) method is used to denoise the acquired vibration signal. Then, the main components of the denoised vibration signal are used to propose the characteristic parameters and synthesized into new parameter indicators. Finally, envelope analysis spectrum is used to analyze the fault classification under the new parameter index. The exepriment results show that the whole life cycle of the rolling bearings can be classified into five different operating periods by using the new parameter index, and each period represents a different bearing operating state.

中文翻译：

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基于主成分分析和经验模态分解的滚动轴承故障检测

针对滚动轴承运行状态分析定量标准不一致的问题，本文采用主成分分析法提取了新的指标F，即时域和频域的联合参数，并确定了F的值。分析滚动轴承的运行状态。首先，加速度传感器用于收集滚动轴承整个生命周期的振动信号。其次，采用经验模态分解（EMD）方法对获取的振动信号进行去噪。然后，使用去噪后的振动信号的主要成分提出特征参数，并合成为新的参数指标。最后，利用包络分析谱对新参数指标下的故障分类进行分析。