College of Mathematics and Statistics, Chongqing University, Chongqing 401331, China.
College of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China.
Sensors (Basel). 2021 Jan 21;21(3):707. doi: 10.3390/s21030707.
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.
随机共振（SR）是一种典型的随机性辅助信号处理方法，已在轴承故障诊断中得到广泛研究，以增强周期性信号的特征。在本研究中，我们摒弃了非线性的基本约束，将其扩展到线性 Langevin 系统中的新型广义 SR（GSR），并提出了波动质量诱导线性振荡器（FMLO）。然后，通过广义标度变换（GST），它被改进为更适合提取高频故障特征。此外，通过分析系统的稳态响应，我们发现线性系统、内部随机调节和外部激励的协同作用可以产生丰富多样的非单调行为，如真实 SR、传统 SR、GSR 和随机抑制（SI）。基于数值实现，发现这些行为在自适应优化系统参数方面发挥着重要作用，可以最大限度地提高强背景噪声下弱高频信号的性能和识别能力。最后，进一步进行实验数据分析，以验证与传统动力学方法相比的有效性和优越性。结果表明，所提出的 GST-FMLO 系统在轴承内圈、外圈和滚动体的故障诊断中表现最佳。特别是，通过放大特征谐波，可以使低谐波与特征相比变得极其微弱。此外，效率提高了 5 倍以上，明显优于非线性动力学方法，具有在线故障诊断的巨大潜力。
