Zhang Lin, Wang Zhijian, Quan Long
College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education, Taiyuan 030024, China.
Entropy (Basel). 2018 May 21;20(5):387. doi: 10.3390/e20050387.
Compared with the strong background noise, the energy entropy of early fault signals of bearings are weak under actual working conditions. Therefore, extracting the bearings' early fault features has always been a major difficulty in fault diagnosis of rotating machinery. Based on the above problems, the masking method is introduced into the Local Mean Decomposition (LMD) decomposition process, and a weak fault extraction method based on LMD and mask signal (MS) is proposed. Due to the mode mixing of the product function (PF) components decomposed by LMD in the noisy background, it is difficult to distinguish the authenticity of the fault frequency. Therefore, the MS method is introduced to deal with the PF components that are decomposed by the LMD and have strong correlation with the original signal, so as to suppress the modal aliasing phenomenon and extract the fault frequencies. In this paper, the actual fault signal of the rolling bearing is analyzed. By combining the MS method with the LMD method, the fault signal mixed with the noise is processed. The kurtosis value at the fault frequency is increased by eight-fold, and the signal-to-noise ratio (SNR) is increased by 19.1%. The fault signal is successfully extracted by the proposed composite method.
与强背景噪声相比，在实际工作条件下，轴承早期故障信号的能量熵较弱。因此，提取轴承的早期故障特征一直是旋转机械故障诊断中的一大难题。基于上述问题，将掩码方法引入局部均值分解（LMD）分解过程，提出了一种基于LMD和掩码信号（MS）的微弱故障提取方法。由于LMD在噪声背景下分解得到的乘积函数（PF）分量存在模态混叠，难以区分故障频率的真实性。因此，引入MS方法处理LMD分解得到的与原始信号相关性较强的PF分量，以抑制模态混叠现象并提取故障频率。本文对滚动轴承的实际故障信号进行了分析。通过将MS方法与LMD方法相结合，对混有噪声的故障信号进行处理。故障频率处的峭度值提高了8倍，信噪比（SNR）提高了19.1%。所提复合方法成功提取了故障信号。
