基于ICEEMD及AWOA优化ELM的机械故障诊断方法

Machinery fault diagnosis method based on ICEMMD and AWOA optimized ELM

旋转机械设备故障检测及识别一直是研究的热点。针对目前故障特征提取和诊断方法的不足,提出一种基于改进的完备集合经验模态分解(ICEEMD)与自适应鲸鱼优化算法(AWOA)优化极限学习机(ELM)的机械故障诊断方法。ICEEMD能够避免在分解过程中产生伪模态,其模式中残留噪声小,使提取故障信息更加准确。利用ICEEMD将采集到的信号分解成多个本征模态函数(IMF),对滚动轴承不同故障状态IMF的斯皮尔曼等级相关系数(SRCC)的计算结果进行分析,得出筛选IMF的标准为其SRCC大于0.02;将筛选后的IMF的混合熵(HE)作为特征向量。WOA相比其他仿生算法所需要调整的相关参数少、收敛速度快、稳定性好。AWOA利用自适应权重优化WOA的局部搜索方式,进一步提高了收敛精度。利用AWOA对ELM的权值和阈值进行优化,可以提高故障诊断的准确率。通过对比实验证明,AWOA-ELM的学习能力强、故障诊断的准确率更高。AWOA-ELM应用在滚动轴承不同尺寸滚珠和外圈故障诊断中,对滚珠故障诊断的准确率达到99.5%,对外圈故障诊断的准确率达到100%。

Rotating machinery equipment fault detection and identification has always been a research hotspot.Aiming at the deficiency of current fault feature extraction and diagnosis methods,this study proposes a method based on improved complete ensemble empirical mode decomposition(ICEEMD)and adaptive whale optimization algorithm(AWOA)optimized extreme learning machine(ELM).The generation of pseudo-modality can be avoided by ICEEMD during the decomposition process,and the residual noise in the mode is small.The extracted fault information is more accurate.ICEEMD is used to decompose the collected signals into intrinsic mode function(IMF).Through analyzing Spearman rank correlation coefficient(SRCC)among IMFs of rolling bearings in different fault states,the conclusion is that the IMF should be screened out when its SRCC is larger than 0.02.The hybrid entropy(HE)of the screened IMF is further calculated as feature vectors.Compared with other bionic algorithms,the whale optimization algorithm(WOA)has advantages of fewer related parameters to be adjusted,faster convergence speed,and better stability.AWOA improves the convergence accuracy further through optimizing WOA′s local search mode by adaptive weight.Through AWOA optimizing the weight and threshold of ELM,the accuracy of fault diagnosis is improved.Comparison experiments show that AWOA-ELM has strong learning ability and higher accuracy of fault diagnosis.The AWOA-ELM method is applied to the fault diagnosis of ball bearings and outer rings of rolling bearings with different sizes.The accuracy of ball fault diagnosis is 99.5%,and the accuracy of external loop fault diagnosis is 100%.