基于LMD近似熵和PSO-ELM的齿轮箱故障诊断

Gearbox Fault Diagnosis based on LMD Approximate Entropy and PSO-ELM

针对齿轮箱使用中常见的故障检测与识别问题,考虑到齿轮箱振动响应信号非线性、非平稳的特性,提出基于局域均值分解(LMD)的近似熵和粒子群优化的极限学习机(PSO-ELM)结合的齿轮箱故障诊断方法。首先,使用LMD分解方法对齿轮箱各工况的振动信号进行分解,结合相关系数选取反映主要故障信息的前4个PF分量。利用近似熵进行定量描述,组成特征向量。最后用粒子群算法对ELM的输入权值与隐含层神经元阈值进行优化,建立PSO-ELM模型,并将近似熵特征值输入到ELM和PSO-ELM模型中,对齿轮箱不同工况进行故障识别与分类。结果表明,基于LMD近似熵和粒子群优化的ELM有更高的分类正确率,验证了该方法的可行性。

For the detection and identification problems of common faults in the use of gearbox, considering the nonlinear, non - stationary properties of the gearbox vibration response signal, a method for the gearbox fault diagnosis based on local mean decomposition （LMD） approximate entropy and PSO - ELM is proposed. Firstly, the LMD decomposition method is used for gearbox vibration signal, with correlation coefficient method extracted the first four PF components which contain the main fault information. By using the approximate entro- py to describe quantitatively, and the feature vector is formed. Finally, the input weights of ELM and the threshold value of the hidden layer neurons are optimized by the particle swarm optimization algorithm, the model of PSO -ELM is established, and the approximate entropy values are input into the ELM and PSO - ELM models to recognize and classify the fault types of the gearbox of different conditions. The results show that based on LMD approximate entropy and PSO -ELM has the higher classification accuracy, the feasibility of this method is verified.