摘要:
煤矿机械设备工作环境恶劣，背景噪声强，轴承早期的故障特征信号微弱，从传感器所测得的振动信号中提取反映故障状态的信息比较困难；同时，煤矿机械设备工作在高速、冲击等工况下，是典型的非平稳工况，不稳定的激励及复杂工况直接导致提取轴承故障特征信号困难。针对以上问题，以矿井提升设备的运行工况为背景，提出了一种基于计算阶次分析与自适应随机共振的滚动轴承故障诊断方法。首先，模拟了矿井提升机运行过程中典型的变转速工况，分别构造故障仿真信号，并采集了轴承振动实验信号；其次，通过等角度采集同步时域鉴相序列，利用计算阶次分析将轴承非平稳的振动信号重采样为平稳信号；然后，利用变分模态分解(VMD)方法将平稳信号分解为若干本征模态函数(IMF)分量，通过轴承故障阶次实现对轴承故障类型的判断；最后，利用自适应随机共振方法来增强轴承故障特征阶次，从而实现故障特征的提取与增强，达到故障诊断的目的。仿真和实验结果证明了该方法的有效性。将该方法与最大相关峭度反褶积(MCKD)方法进行了对比，结果表明，MCKD方法虽然也可以观察到故障特征阶次，但是特征阶次比周围干扰阶次幅值仅高0.001 96，低于本文所提方法的结果，说明了本文所提方法具有一定的优越性。
Abstract:
The working environment of coal mine mechanical equipment is harsh, the background noise is strong, and the early fault characteristic information of the bearing is weak. Therefore, it is difficult to extract the information reflecting the fault state from the vibration signal measured by the sensor. Moreover, the coal mine mechanical equipment work in high speed, shock and other working conditions, which are typical non-stationary working conditions. The unstable excitation and complex working conditions directly lead to the difficulty of extracting the bearing fault characteristic signal. In order to solve the above problems, a rolling bearing fault diagnosis method based on computed order analysis and adaptive stochastic resonance is proposed in the background of the working conditions of mine hoisting equipment. Firstly, the method simulates the typical variable speed working conditions in the operation of mine hoist, constructs the fault simulation signals and collects the experimental signals of bearing vibration. Secondly, by collecting synchronous time-domain key-phase signal at equal angles, the non-stationary vibration signal of the bearing is resampled into a stationary signal by using computed order analysis. Thirdly, the stationary signal is decomposed into a number of intrinsic mode function (IMF) components by using the variational mode decomposition (VMD) method, and the bearing fault type is judged by the bearing fault order. Finally, the adaptive stochastic resonance method is used to enhance the bearing fault characteristic order so as to achieve the extraction and enhancement of fault characteristics for fault diagnosis. The simulation and experimental results prove the effectiveness of the method. And the method is compared with the maximum correlation kurtosis deconvolution (MCKD) method. The results show that although the MCKD method can also observe the fault characteristic order, but the characteristic order is only 0.001 96 higher than the amplitude of the surrounding interference order, which is lower than the results of the proposed method, indicating the superiority of the proposed method.
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