基于布谷鸟算法优化随机共振参数的轴承故障检测算法

Cuckoo search algorithm based optimization of stochastic resonance parameters for bearing fault detection

轴承故障信号的提取易受工作环境中强背景噪声的影响,特别在早期故障信号检测中,轴承故障信号被噪声淹没,导致检测受限。针对传统的自适应随机共振理论在轴承故障信号检测中参数优化单一的缺陷,提出一种基于布谷鸟算法优化随机共振参数的轴承故障检测算法,该方法以输出信号信噪比作为适应度函,对随机共振理论中两个参数协调优化,得到一组最优参数,自适应实现与输入信号、噪声、非线性系统三者最匹配的随机共振效果。最后通过仿真对比,所提出的算法信号检测结果优于传统随机共振方法;通过轴承故障诊断实验数据验证,该算法实现的轴承故障信号的检测误差为0.15%。实验结果表明所提方法具有寻优参数准确度高、可靠性好等优点,对轴承故障的精准检测和工业设备稳定运行具有重要意义。

Bearing fault signal extraction is susceptible to strong background noise in the working environment,especially in the early fault signal detection,bearing fault signal is submerged by noise,resulting in limited detection.In view of the traditional adaptive stochastic resonance theory in bearing fault signal detection parameters optimization of a single defect,put forward a cuckoo algorithm to optimize stochastic resonance parameters based on the bearing fault detection algorithms,this method takes the output signal-to-noise ratio as fitness function,the theory of stochastic resonance in the two coordinate parameter optimization,get a set of optimal parameters,adaptive stochastic resonance is best matched with input signal,noise and nonlinear system.Finally,through simulation comparison,the singal detection result of the proposed algorithm is better than that of the tradition stochastic resonance method.Experimental data of bearing fault diagnosis show that the detection error of bearing fault signals achieved by this algorithm is 0.15%.Experimental results show that the proposed method has the adwantages of high accuracy and good reliability,which is of great significance to the accurate detection of bearing faults and the stable operation of industrial equipment.