Fault Diagnosis of Rolling Bearing Based on EEMD-Hilbert and FWA-SVM
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摘要: 为有效提取非平稳特性的滚动轴承振动信号特征，提高故障诊断效率，提出一种采用集合经验模态分解（empiricalmode decomposition，EEMD）、Hilbert变换的特征提取方法，并利用烟花算法优化支持向量机（support vector machine，SVM）分类参数的滚动轴承故障诊断方法. 通过EEMD方法将目标信号分解成若干个模态函数，采取Hilbert变换获取模态函数的瞬时频率，并对模态函数及其瞬时频率进行统计特征提取，从而实现特征的有效降维. 结果表明：信号经过EEMD-Hilbert处理后特征能有效提取，将训练集和测试集各600组数据代入烟花算法优化SVM模型得到测试集正确率为99.63%；比传统的遗传算法和粒子群算法优化模型分别提高0.4%和0.2%左右；同时收敛时间更短，验证了该算法模型的可行性与有效性.Abstract: To effectively extract the non-stationary characteristics of the rolling bearing vibration signal and improve the fault diagnosis efficiency, a feature extraction method based on the ensemble empirical mode decomposition (EEMD) and Hilbert transform was proposed. The support vector machine (SVM) classification parameters were optimised using the fireworks algorithm (FWA) for the rolling bearing fault diagnosis method. The EEMD method was used to decompose the target signal into several modal functions. The instantaneous frequencies of the modal functions were obtained through Hilbert transforms. Statistical feature extraction and dimensionality reduction were respectively performed for the modal function and instantaneous frequency. The fireworks algorithm model was used to optimise the SVM parameters as well as the multi-classification fault diagnosis with training and test sets drawn from 600 datasets. The accuracy of the signal is estimated to be 99.633%, which is 0.4% and 0.2% higher than that of the traditional genetic algorithm and particle swarm optimisation algorithm, respectively. Further, the ability of iterative convergence is also seen to have obvious advantages. The feasibility and validity of the algorithm models are thus verified.
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表 1 轴承故障样本
Table 1. Bearing failure samples
轴承状态 故障点直径/mm 训练集 测试集 正常 — 60 60 内圈故障 0.178 60 60 滚动体故障 0.178 60 60 外圈故障 0.178 60 60 内圈故障 0.356 60 60 滚动体故障 0.356 60 60 外圈故障 0.356 60 60 内圈故障 0.533 60 60 滚动体故障 0.533 60 60 外圈故障 0.533 60 60 
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表 2 3种分类结果
Table 2. Classification results for the three methods
模型 迭代时间/s 正确率/% EEMD_H模型 282.9 99.43 EEMD模型 149.8 94.17 EMD_H模型 304.3 95.10
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表 3 FWA、PSO、GA对SVM参数寻优
Table 3. FWA，PSO，and GA for SVM parameter optimisation
算法 C $\sigma $ 正确率/% PSO 38.12 9.35 98.67 FWA 35.39 6.88 99.00 GA 57.86 23.97 98.67
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表 4 3种分类结果
Table 4. Classification results for the three algorithms
模型 迭代时间/s 正确率/% FWA 14.4 99.63 GA 114.3 99.20 PSO 282.9 99.43
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