Zhang Chunguang, Wang Yao, Deng Wu
School of Electronics and Information Engineering, Dalian Jiaotong University, Dalian 116028, China.
Traction Power State Key Laboratory of Southwest Jiaotong University, Chengdu 610031, China.
Entropy (Basel). 2020 Jul 3;22(7):739. doi: 10.3390/e22070739.
It is difficult to extract the fault signal features of locomotive rolling bearings and the accuracy of fault diagnosis is low. In this paper, a novel fault diagnosis method based on the optimized variational mode decomposition (VMD) and resonance demodulation technology, namely GNVRFD, is proposed to realize the fault diagnosis of locomotive rolling bearings. In the proposed GNVRFD method, the genetic algorithm and nonlinear programming are combined to design a novel parameter optimization algorithm to adaptively optimize the two parameters of the VMD. Then the optimized VMD is employed to decompose the collected vibration signal into a series of intrinsic mode functions (IMFs), and the kurtosis value of each IMF is calculated, respectively. According to the principle of maximum value, two most sensitive IMF components are selected to reconstruct the vibration signal. The resonance demodulation technology is used to decompose the reconstructed vibration signal in order to obtain the envelope spectrum, and the fault frequency of locomotive rolling bearings is effectively obtained. Finally, the actual data of rolling bearings is selected to testify the effectiveness of the proposed GNVRFD method. The experiment results demonstrate that the proposed GNVRFD method can more accurately and effectively diagnose the fault of locomotive rolling bearings by comparing with other fault diagnosis methods.
机车滚动轴承故障信号特征难以提取，故障诊断准确率较低。为此，本文提出一种基于优化变分模态分解（VMD）和共振解调技术的新型故障诊断方法，即GNVRFD，以实现机车滚动轴承的故障诊断。在所提出的GNVRFD方法中，将遗传算法与非线性规划相结合，设计一种新型参数优化算法，自适应优化VMD的两个参数。然后采用优化后的VMD将采集到的振动信号分解为一系列固有模态函数（IMF），并分别计算各IMF的峭度值。根据最大值原理，选取两个最敏感的IMF分量重构振动信号。利用共振解调技术对重构后的振动信号进行分解以获得包络谱，有效得到机车滚动轴承的故障频率。最后，选取滚动轴承实际数据验证所提GNVRFD方法的有效性。实验结果表明，与其他故障诊断方法相比，所提GNVRFD方法能够更准确、有效地诊断机车滚动轴承故障。
