基于快速自适应经验模态分解的高速经编机振动分析

Vibration analysis of high speed warp knitting machine based on fast empirical mode decomposition

针对某型高速经编机在高转速下结构振动过大以及机构运动信号与结构振动信号相混叠,故障特征难以分离的问题,提出基于快速自适应经验模态分解(FAEMD)算法的经编机振动故障诊断方法。首先运用FAEMD算法将原始振动信号分解成有限个本征模态函数(IMF),然后计算各IMF分量与原信号的相关性,结合经编机运动特点,判断其中相关性最大的本征模态函数为机构运动分量并去除,最后将剩余分量重组实现结构振动信号的提取。将该方法应用于经编机振动故障诊断中,对动态振动数据进行处理,结合静态固有频率测试,成功提取出与实际故障现象相同的信号频率特征,判断出经编机在高转速下振动过大的原因,为后续经编机振动优化提供了参考。

Objective Warp knitting machine is one of the most important machines used widely in the textile industry.Aiming at the problems that excessive vibration of a high-speed warp knitting machine at high speed and the overlapping of mechanism motion signal and structure vibration signal,which makes it difficult to separate fault characteristics,a vibration fault diagnosis method for high speed warp knitting machine based on fast empirical mode decomposition(FAEMD)algorithm is proposed.Method Firstly,the original vibration signal was decomposed into finite intrinsic mode functions(IMFs)by FAEMD algorithm.Then,the correlation between each IMF component and the original signal was calculated.Combining with the motion characteristics of the warp knitting machine,an analysis was carried out to determine the most relevant intrinsic mode function and its removal.Finally,the remaining components are recombined to extract the structural vibration signal.The specific process is shown in Fig.3.Results The proposed method was applied to the vibration analysis of a high speed warp knitting machine.Abnormal sound caused by excessive vibration occurred when a certain type of high-speed warp knitting machine runs at the speed of 1700 r/min,1900 r/min and 2000 r/min.To tackle this,the location of measuring points and the directions of measuring signals were determined according to the structural characteristics of the warp knitting machine.The measuring points were bed,comb bed,slotted needle bed,needle core bed and settler sheet bed,and the directions were vertical and length(Fig.5).Then static test was carried out to determine the natural frequencies of the main parts of the warp knitting machine in three directions i.e.length,front,back and vertical,before the dynamic test was carried out.The speed change started from 1600 r/min and increased to 2000 r/min at a 50 r/min step to obtain vibration signals of main components at different speeds.According to the structural characteristics of the drive crankshaft of the warp knitting machine(Fig.6),the main frequency of analysis was determined to be three times the frequency of the speed.The original signal features and the features extracted by the traditional EMD algorithm were not consistent with the fault phenomena.The proposed method was applied to the vibration signals of warp knitting machines,and the signal features consistent with the fault phenomena were successfully extracted(Fig.7).By combining the static test results with the dynamic test results,it was finally determined that the reason for the excessive vibration of the structure was that the frequency of the driving force was close to the natural frequency of the bed,the settler sheet bed and the comb bed in the vertical direction at some specific speed,so as to produce the resonance phenomenon.Conclusion In order to solve the problem of excessive vibration of high-speed warp knitting machine at a specific speed,a new vibration analysis method of warp knitting machine is proposed in this paper.The FAEMD algorithm and Pearson correlation coefficient are innovated to remove the mechanism motion signals of warp knitting machine and keep the structural vibration signals for analysis.In practical application,it is found that for the same signals,the number of IMF decomposed by EMD algorithm is more than that obtained by FAEMD algorithm,and the correlation of signals is poor.This method can improve the problem of end-point effect and mode aliasing of traditional EMD algorithm,and can effectively extract the fault characteristics of vibration acceleration signal of warp knitting machine,which provides a feasible method for vibration fault diagnosis of warp knitting machine.