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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >A New Dynamical Method for Bearing Fault Diagnosis Based on Optimal Regulation of Resonant Behaviors in a Fluctuating-Mass-Induced Linear Oscillator
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A New Dynamical Method for Bearing Fault Diagnosis Based on Optimal Regulation of Resonant Behaviors in a Fluctuating-Mass-Induced Linear Oscillator

机译：一种新的轴承故障诊断动力学方法其基于波动 - 质量诱导的线性振荡器谐振行为的最佳调节

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Stochastic resonance (SR), a typical randomness-assisted signal processing method, has been extensively studied in bearing fault diagnosis to enhance the feature of periodic signal. In this study, we cast off the basic constraint of nonlinearity, extend it to a new type of generalized SR (GSR) in linear Langevin system, and propose the fluctuating-mass induced linear oscillator (FMLO). Then, by generalized scale transformation (GST), it is improved to be more suitable for exacting high-frequency fault features. Moreover, by analyzing the system stationary response, we find that the synergy of the linear system, internal random regulation and external excitement can conduct a rich variety of non-monotonic behaviors, such as bona-fide SR, conventional SR, GSR, and stochastic inhibition (SI). Based on the numerical implementation, it is found that these behaviors play an important role in adaptively optimizing system parameters to maximally improve the performance and identification ability of weak high-frequency signal in strong background noise. Finally, the experimental data are further performed to verify the effectiveness and superiority in comparison with traditional dynamical methods. The results show that the proposed GST-FMLO system performs the best in the bearing fault diagnoses of inner race, outer race and rolling element. Particularly, by amplifying the characteristic harmonics, the low harmonics become extremely weak compared to the characteristic. Additionally, the efficiency is increased by more than 5 times, which is significantly better than the nonlinear dynamical methods, and has the great potential for online fault diagnosis.
机译：随机共振（SR），典型的随机辅助信号处理方法已被广泛研究了轴承故障诊断，以增强周期信号的特征。在这项研究中，我们抛弃了非线性的基本限制，将其扩展到线性Langevin系统中的一种新型广义SR（GSR），并提出了波动 - 质量诱导的线性振荡器（FMLO）。然后，通过广义缩放变换（GST），改进为更适合于严格的高频故障特征。此外，通过分析系统静止响应，我们发现线性系统的协同作用，内部随机调节和外部兴奋可以开展丰富的非单调行为，例如Bona-FIDE SR，常规SR，GSR和随机抑制（Si）。基于数值实现，发现这些行为在自适应地优化系统参数中发挥着重要作用，以便最大地改善弱高频信号在强大的背景噪声中的性能和识别能力。最后，进一步执行实验数据以验证与传统动态方法相比的有效性和优越性。结果表明，所提出的GST-FMLO系统在内部竞争，外圈和滚动元件的轴承故障诊断中表现最佳。特别是，通过放大特征谐波，与特性相比，低谐波变得极其弱。此外，效率增加了超过5倍，这显着优于非线性动力学方法，并且具有在线故障诊断的巨大潜力。

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