排列熵與核極限學習機在齒輪故障診斷中的應用
發(fā)布時間：2019-01-09 16:25
【摘要】：針對齒輪故障難提取和極限學習機(extreme learning machine,ELM)隱層節(jié)點數(shù)需要人為設定,致使齒輪故障分類模型準確度低、穩(wěn)定性差的問題,提出基于核極限學習機(kernel extreme learning machine,K-ELM)的齒輪故障診斷方法。首先,將測得信號經經驗模態(tài)分解(empirical mode decomposition,EMD)處理后得到一系列IMF本征模式分量,并提取各分量的排列熵(permutation entropy,PE)值組成高維特征向量集;然后利用高斯核函數(shù)的內積表達ELM輸出函數(shù),從而自適應確定隱層節(jié)點數(shù);最后,將所得高維特征向量集作為K-ELM算法的輸入建立核函數(shù)極限學習機齒輪故障分類模型,進行齒輪不同故障狀態(tài)的分類辨識。實驗結果表明:與SVM、ELM故障分類模型相比,核函數(shù)ELM滾動齒輪故障診斷分類模型具有更高的準確度和穩(wěn)定性。
[Abstract]:Aiming at the problem that the hidden node points of gear fault extraction and (extreme learning machine,ELM) need to be set artificially, the accuracy of gear fault classification model is low and the stability of gear fault classification model is poor, a new method based on kernel limit learning machine (kernel extreme learning machine,) is proposed. The method of gear fault diagnosis based on K-ELM. First, a series of IMF eigenmode components are obtained after the measured signal is processed by empirical mode decomposition (empirical mode decomposition,EMD), and the permutation entropy (permutation entropy,PE) values of each component are extracted to form a high dimensional eigenvector set. Then the inner product of Gao Si kernel function is used to express the ELM output function, which adaptively determines the number of hidden layer nodes. Finally, the high dimensional eigenvector set is used as the input of K-ELM algorithm to establish the kernel function extreme learning machine gear fault classification model, and to classify and identify the different fault states of gear. The experimental results show that the kernel function ELM rolling gear fault diagnosis model has higher accuracy and stability than the SVM,ELM fault classification model.
【作者單位】： 內蒙古科技大學機械工程學院;
【基金】：國家自然科學基金(51565046) 內蒙古自然科學基金(2015MS0512) 內蒙古科技大學創(chuàng)新基金(2015QDL12)
【分類號】：TH132.41
[Abstract]:Aiming at the problem that the hidden node points of gear fault extraction and (extreme learning machine,ELM) need to be set artificially, the accuracy of gear fault classification model is low and the stability of gear fault classification model is poor, a new method based on kernel limit learning machine (kernel extreme learning machine,) is proposed. The method of gear fault diagnosis based on K-ELM. First, a series of IMF eigenmode components are obtained after the measured signal is processed by empirical mode decomposition (empirical mode decomposition,EMD), and the permutation entropy (permutation entropy,PE) values of each component are extracted to form a high dimensional eigenvector set. Then the inner product of Gao Si kernel function is used to express the ELM output function, which adaptively determines the number of hidden layer nodes. Finally, the high dimensional eigenvector set is used as the input of K-ELM algorithm to establish the kernel function extreme learning machine gear fault classification model, and to classify and identify the different fault states of gear. The experimental results show that the kernel function ELM rolling gear fault diagnosis model has higher accuracy and stability than the SVM,ELM fault classification model.
【作者單位】： 內蒙古科技大學機械工程學院;
【基金】：國家自然科學基金(51565046) 內蒙古自然科學基金(2015MS0512) 內蒙古科技大學創(chuàng)新基金(2015QDL12)
【分類號】：TH132.41
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