Huang Weichao, Zhang Ganggang
Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China.
School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel). 2023 Jul 19;23(14):6529. doi: 10.3390/s23146529.
In an effort to overcome the problem that the traditional stochastic resonance system cannot adjust the structural parameters adaptively in bearing fault-signal detection, this article proposes an adaptive-parameter bearing fault-detection method. First of all, the four strategies of Sobol sequence initialization, exponential convergence factor, adaptive position update, and Cauchy-Gaussian hybrid variation are used to improve the basic grey wolf optimization algorithm, which effectively improves the optimization performance of the algorithm. Then, based on the multistable stochastic resonance model, the structure parameters of the multistable stochastic resonance are optimized through improving the grey wolf algorithm, so as to enhance the fault signal and realize the effective detection of the bearing fault signal. Finally, the proposed bearing fault-detection method is used to analyze and diagnose two open-source bearing data sets, and comparative experiments are conducted with the optimization results of other improved algorithms. Meanwhile, the method proposed in this paper is used to diagnose the fault of the bearing in the lifting device of a single-crystal furnace. The experimental results show that the fault frequency of the inner ring of the first bearing data set diagnosed using the proposed method was 158 Hz, and the fault frequency of the outer ring of the second bearing data set diagnosed using the proposed method was 162 Hz. The fault-diagnosis results of the two bearings were equal to the results derived from the theory. Compared with the optimization results of other improved algorithms, the proposed method has a faster convergence speed and a higher output signal-to-noise ratio. At the same time, the fault frequency of the bearing of the lifting device of the single-crystal furnace was effectively diagnosed as 35 Hz, and the bearing fault signal was effectively detected.
为了克服传统随机共振系统在轴承故障信号检测中不能自适应调整结构参数的问题，本文提出了一种自适应参数轴承故障检测方法。首先，采用索博尔序列初始化、指数收敛因子、自适应位置更新和柯西 - 高斯混合变异这四种策略对基本灰狼优化算法进行改进，有效提高了算法的优化性能。然后，基于多稳态随机共振模型，通过改进灰狼算法对多稳态随机共振的结构参数进行优化，以增强故障信号，实现对轴承故障信号的有效检测。最后，利用所提出的轴承故障检测方法对两个开源轴承数据集进行分析诊断，并与其他改进算法的优化结果进行对比实验。同时，将本文所提方法用于单晶炉提升装置中轴承的故障诊断。实验结果表明，采用该方法诊断的第一个轴承数据集内圈故障频率为158Hz，第二个轴承数据集外圈故障频率为162Hz。两个轴承的故障诊断结果与理论推导结果一致。与其他改进算法的优化结果相比，所提方法具有更快的收敛速度和更高的输出信噪比。同时，有效诊断出单晶炉提升装置轴承的故障频率为35Hz，有效检测到了轴承故障信号。
