改进的经验模态分解方法和解析能量算子在电机轴承故障诊断中的应用

摘要:

电机轴承在运行中一般受到载荷、传输和冲击的共同影响，导致轴承极易出现故障，从而导致整个电机的机械故障。振动信号分析方法是目前用于电机轴承故障监测和诊断的最常用技术。然而，由于轴承故障脉冲信号的能量非常微弱，因此极易被振动信号中的噪声与其他干扰所淹没。为了解决这一问题，本文在基于经验模态分解方法的故障特征提取与理论计算的基础上，提出了一种改进的经验模态分解方法和一种新颖的解析能量算子相结合的电机轴承故障诊断方法。在该提出的诊断方法中，首先采用一种改进的经验模态分解方法将振动信号分解成多个信号分量，从而去除背景噪声和振动干扰。然后，利用一种包络谱峭度指标来衡量振动信号的复杂度，从而选择出合适的信号分量进行进一步分析。最后，采用解析能量算子从分解后的信号分量中提取电机轴承故障特征。通过仿真信号和实际电机轴承振动信号验证了所提方法的有效性和优越性。

Abstract:

Motor bearings are commonly subjected to load, transmission, and impact during operation, resulting in bearing failure that ultimately leads to mechanical breakdown of the entire motor. Vibration signal analysis is a widely adopted technique for monitoring and diagnosing motor bearing faults. However, due to the low energy level of the bearing fault signal, it is susceptible to being overwhelmed by noise and other disturbances present in the vibration signal. In order to address this issue, an enhanced empirical mode decomposition method and a novel analytical energy operator are proposed in this study for motor bearing fault diagnosis, based on fault feature extraction and theoretical calculation using the empirical mode decomposition method. In the proposed diagnostic method, an improved empirical mode decomposition technique is employed to decompose the vibration signal into multiple components in order to eliminate background noise and vibration interference. Subsequently, an measurement index called envelope spectrum kurtosis is utilized to quantify the complexity of the vibration signal, enabling selection of appropriate signal components for further analysis. Finally, fault characteristics of motor bearings are extracted from the decomposed signal components using the analytic energy operator. Simulation signals as well as actual motor bearing vibration signals are employed to validate the efficacy and superiority of this proposed methodology.