可变负载的准零刚度隔振器特性

针对风机滚动轴承微弱故障信号所具有的非线性和非平稳特征及易被强背景噪声掩盖的特点，提出了一种变分模态分解(variational modal decomposition, 简称VMD)和最大相关峭度解卷积(maximum correlated kurtosis deconvolution, 简称MCKD)相结合的滚动轴承微弱故障诊断方法。为实现VMD和MCKD的参数自适应选择，采用粒子群优化算法(particle swarm optimization, 简称PSO)，对两种算法中的参数进行优化。首先，利用PSO优化VMD算法中的α和K，再基于VMD对微弱故障信号分解后的结果，选取最优模态分量；其次，利用PSO优化MCKD算法中的L和T，再基于MCKD算法加强最优分量信号中的故障冲击成分；最后，通过包络谱提取出轴承微弱故障特征。仿真和试验均表明，此方法能够自适应增强轴承微弱故障中的冲击成分，有效提取出被强噪声淹没的轴承微弱故障特征。

Analysis of Quasi-zero-Stiffness Vibration Isolator with Variable Load

A quasi-zero-stiffness vibration isolator with a variable load is designed to make up for the weakened ability to isolate vibration caused by the changed load. First, the principle of the vibration isolator is discussed in terms of its static characteristics based on its mathematical model. Then, the main resonance response is analyzed by the averaging method including the amplitude-frequency characteristic function of relative displacement and the displacement transfer rate of vibration. Besides, the rules of vibration changing with the parameters are investigated. The results show that the vibration isolator adapts to load by changing the angle of the quasi-zero-stiffness spring when the resonance is weakened and the initial isolation frequency is reduced with increasing angle. The vibration isolator is also characterized by gradual hardening, which compresses the resonant frequency bandwidth and reduces the descending frequency so that the quasi-zero-stiffness isolator with variable load excels the normal one in the low-frequency isolation performance.