基于ICEEMDAN-NOA-SVM的采煤机截割部轴承故障诊断技术  

Fault diagnosis method of bearing in cutting section of shearers based on ICEEMDAN-NOA-SVM

作　　者：郭晋辉 GUO Jinhui(Lu’an Chemical Group Co.,Ltd.,Changzhi 046299,China)

机构地区：[1]潞安化工集团有限公司,山西长治046299

出　　处：《煤炭工程》2025年第2期156-162,共7页Coal Engineering

基　　金：山西省揭榜招标项目(20201101005)。

摘　　要：针对复杂环境下的采煤机截割部轴承易出现故障,且现有故障诊断模型实际应用效果不佳等问题,提出了基于改进自适应噪声集合经验模态分解(ICEEMDAN)与改进支持向量机(SVM)的采煤机截割部轴承故障诊断方法。首先对振动信号进行ICEEMDAN分解,通过选取合适的分量进行重构;然后对重构的分量提取能量特征,并与重构后的信号所提取的时域频域特征组成高维的特征矩阵,使用PCA降维算法对其进行降维;最后利用改进的SVM分类模型对低维特征矩阵进行故障诊断识别,并与多种主流分类算法进行对比。训练结果表明,该方法的故障诊断准确率高达99.3%,比SVM、PSO-SVM和GA-SVM分别高出3.9%、1.1%和1.7%,加噪条件下依然有95.2%的分类准确率,比其他三种分类模型分别高出8.9%、3.9%和3.1%,且收敛速度更快。在实际工程应用中具有94.7%的分类准确率,可有效提高煤矿智能化程度。In order to solve the problem that the bearing of the cutting part of the shearer is prone to failure in complex environment and the application effect of the existing fault diagnosis model is not satisfying,a fault diagnosis method for the bearing in the cutting part of the shearer based on the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(ICEEMDAN)and the improved Support Vector Machines(SVM)was proposed.Then,the energy features of the reconstructed IMF components were extracted,and a high-dimensional feature matrix was formed with the time-domain frequency domain features extracted from the reconstructed signal,and the PCA dimensionality reduction algorithm was used to reduce the dimensionality.Finally,the improved Support Vector Machine(SVM)classification model was used to diagnose and identify the faults of the low-dimensional feature matrix,and the proposed method was compared with a variety of mainstream classification algorithms.The training results show that the proposed method has a fault diagnosis accuracy of 99.3%,which is 3.9,1.1 and 1.7 percentage points higher than that of SVM,PSO-SVM and GA-SVM,respectively,and still has a classification accuracy of 95.2%under the noise condition,which is 8.9,3.9 and 3.1 percentage points higher than the other three classification models,respectively,and the convergence speed is faster.It has a classification accuracy of 94.7%in practical engineering applications,which can effectively improve the intelligence of coal mines.

关 键 词：采煤机 轴承故障 经验模态分解 分类算法 

分 类 号：TD421.6[矿业工程—矿山机电] TP277[自动化与计算机技术—检测技术与自动化装置]