FPCA-RBF-ELM-Based Gearbox Fault Detection Method
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摘要: 为了克服在数据处理中出现的信息缺失和冗余以及在故障检测上准确率较低等缺陷，利用函数型主成分所具有的鲁棒性和稳定性强的优点来弥补极限学习机在稳定性方面的不足，结合径向基极限学习机，提出了一种基于FPCA（函数型主成分分析）-RBF（径向基函数）-ELM（极限学习机）的齿轮箱故障检测方法。首先用基函数对原始数据进行预处理，然后应用FPCA提取特征信息建立RBF-ELM齿轮诊断模型，最后利用行星齿轮箱实验数据验证故障检测性能，并与FPCA、FPCA-SVDD和PCA-RBF-ELM的行星齿轮箱故障检测结果对比。结果表明：FPCA-RBF-ELM检测率最高且检测效率快，可用于行星齿轮箱的故障检测，此方法具有可行性和有效性。Abstract: In order to overcome the lack of information in the data processing and redundancy and low defect on the fault detection accuracy, using functional principal component with the advantages of strong robustness and stability to fill the extreme learning machine in the lack of stability, combined with the RBF-ELM, a new gear fault detection model was proposed based on FPCA (principal component analysis)-RBF(radial basis function)-ELM(extreme learning machine) method. First, the basis function was used to preprocess the original gear vibration data, and then FPCA was used to extract the characteristic information as the training set to establish the ELM gear fault diagnosis model. Finally, the planetary gear box experimental data were used to verify the fault detection performance, and the fault detection results of the planetary gear box were compared with those of FPCA, FPCA-SVDD and PAC-RBF-ELM. The results show that the FPCA-RBF-ELM method has the highest detection rate and fastest detection efficiency, which can be used for fault detection of planetary gear box. This method is effective and feasible.
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表 2 主成分累计贡献率
主成分个数 累计贡献值/% 垂直方向 水平径向 轴向 1 50.78 47.95 45.94 2 73.42 73.83 72.34 3 84.48 87.63 85.16 4 92.31 94.89 91.88 
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表 3 4种法类比
检测方法 训练集
准确率/%测试集
准确率/%反应
时间/sFPCA 95 90 9.233 FPCA-SVDD 90 97 12.632 PCA-RBF-ELM 93 95 6.526 FPCA-RBF-ELM 100 100 6.188
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