Ningbo Cigarette Factory, China Tobacco Zhejiang Industry Co., Ltd., Ningbo 315040, China.
School of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, China.
Sensors (Basel). 2023 Apr 10;23(8):3860. doi: 10.3390/s23083860.
Although stochastic resonance (SR) has been widely used to enhance weak fault signatures in machinery and has obtained remarkable achievements in engineering application, the parameter optimization of the existing SR-based methods requires the quantification indicators dependent on prior knowledge of the defects to be detected; for example, the widely used signal-to-noise ratio easily results in a false SR and decreases the detection performance of SR further. These indicators dependent on prior knowledge would not be suitable for real-world fault diagnosis of machinery where their structure parameters are unknown or are not able to be obtained. Therefore, it is necessary for us to design a type of SR method with parameter estimation, and such a method can estimate these parameters of SR adaptively by virtue of the signals to be processed or detected in place of the prior knowledge of the machinery. In this method, the triggered SR condition in second-order nonlinear systems and the synergic relationship among weak periodic signals, background noise and nonlinear systems can be considered to decide parameter estimation for enhancing unknown weak fault characteristics of machinery. Bearing fault experiments were performed to demonstrate the feasibility of the proposed method. The experimental results indicate that the proposed method is able to enhance weak fault characteristics and diagnose weak compound faults of bearings at an early stage without prior knowledge and any quantification indicators, and it presents the same detection performance as the SR methods based on prior knowledge. Furthermore, the proposed method is more simple and less time-consuming than other SR methods based on prior knowledge where a large number of parameters need to be optimized. Moreover, the proposed method is superior to the fast kurtogram method for early fault detection of bearings.
尽管随机共振（SR）已被广泛应用于增强机械中的微弱故障特征，并在工程应用中取得了显著的成果，但现有的基于 SR 的方法的参数优化需要依赖于待检测缺陷的先验知识的量化指标；例如，广泛使用的信噪比容易导致虚假 SR，并进一步降低 SR 的检测性能。这些依赖于先验知识的指标不适合机械的实际故障诊断，因为它们的结构参数是未知的，或者无法获得。因此，我们有必要设计一种具有参数估计的 SR 方法，这种方法可以通过处理或检测到的信号来自适应地估计这些 SR 参数，而不是机械的先验知识。在这种方法中，可以考虑二阶非线性系统中的触发 SR 条件以及弱周期信号、背景噪声和非线性系统之间的协同关系，以决定增强机械未知微弱故障特征的参数估计。进行了轴承故障实验，以验证所提出方法的可行性。实验结果表明，该方法无需先验知识和任何量化指标，即可增强微弱故障特征，并在早期诊断轴承的微弱复合故障，其检测性能与基于先验知识的 SR 方法相同。此外，与需要优化大量参数的基于先验知识的其他 SR 方法相比，该方法更简单、耗时更少。而且，该方法在轴承早期故障检测方面优于快速峭度图方法。
