Multi-kernel Multi-class Relevance Vector Machine and its Application to Fault Diagnosis of Rolling Bearing with Multi-feature Fusion
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摘要: 考虑到滚动轴承振动信号的复杂性, 单一故障特征难以获得较理想的故障诊断结果, 提出一种基于多核多分类相关向量机(Multi-kernel multi-class relevance vector machine, MMRVM)的多特征融合智能故障诊断方法。该方法将具有不同特性的故障特征通过核函数映射到高维特征空间, 按照特征贡献量大小进行加权求和从而融合形成多特征空间, 充分利用各特征向量的有效属性, 有效避免不同特征直接融合导致的维数增高问题。此外, 通过量子遗传算法自适应选取不同特征对应的最优核参数, 进一步提高了故障识别准确率。滚动轴承故障诊断实例表明, 与其它方法相比, 所提方法可有效融合多种滚动轴承故障特征信息, 具有更高的故障诊断准率。Abstract: Considering the complexity of rolling bearing vibration signals, it is difficult to obtain ideal fault diagnosis results only using single fault feature, a novel multi-feature fusion intelligent fault diagnosis method based on multi-kernel multi-class relevance vector machine was proposed. According to the contribution of fault features, different fault features are mapped to high dimensional feature space and fused to multiple feature spaces with weighted summation in this method. It can overcome the curse of dimensionality if different features are integrated directly, because the effective properties of different feature vector are fully utilized. Additionally, corresponding kernel parameters of different fault features are selected adaptively by quantum genetic algorithm, and the fault identification accuracy can be further improved. Compared with other methods, the experiment of rolling bearing fault diagnosis shows that the proposed method can fuse various fault features of rolling bearing effectively and achieve higher fault diagnosis accuracy.
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Key words:
- fault diagnosis /
- rolling bearing /
- feature fusion /
- multi-kernel multi-class /
- relevance vector machine
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表 1 各特征向量类别及其对应的表示方法
特征向量类别 基本属性 小波包时域特征向量 [x0, x1, …, xn] EEMD域特征向量 [c0, c1, …, cN] 统计域特征向量 [Xrms2, Ip, Cf, Ce] 
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表 2 不同特征融合方法的故障诊断性能对比情况
融合特征类别 训练时间/s Rvs/个 准确率/% EEMD 10.01 13 98.08 WPT 9.46 10 97.44 时域 7.36 4 33.46 EEMD+WPT 12.94 9 99.36 WPT+时域 10.45 10 98.95 EEMD+时域 11.77 12 99.23 EEMD+WPT+时域 12.96 7 99.87 EEMD∩WPT∩时域 28.75 16 98.92 注: EEMD∩WPT∩时域为3种特征直接混合
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表 3 不同诊断模型的故障诊断实验结果
故障识别器 Rvs或Svs/个 准确率/% QGA-SVM 19 94.23 MMRVM 10 96.37 QGA-MMRVM 7 99.87 KNN - 94.16 BPNN - 94.06
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