A Rolling Bearing Fault Diagnosis Method Based on Variational Mode Decomposition and an Improved Kernel Extreme Learning Machine
A Rolling Bearing Fault Diagnosis Method Based on Variational Mode Decomposition and an Improved Kernel Extreme Learning Machine
复制标题
DOI：
10.3390/app7101004
复制
发表时间：
2017-10-01
影响因子：
2.7
通讯作者：
Chen, Peng
中科院分区：
文献类型：
--
作者：
Li, Ke;Su, Lei;Chen, Peng
滚动轴承是旋转机械的关键部件。为了保证滚动轴承早期有效的故障诊断，提出了一种基于变分模式分解(VMD)和改进的核极值学习机(KELM)的滚动轴承故障诊断方法。利用VMD对故障信号进行分解，得到本征模式函数(IMF)分量，并计算包含主要故障信息的IMF分量的近似熵。根据每个分量的近似熵创建特征向量。通过KELM建立轴承诊断模型，利用粒子群算法对KELM参数进行优化，得到参数最优的KELM诊断模型。最后，通过风机轴承故障诊断试验，验证了本文提出的诊断方法的有效性。在相同条件下，将结果与反向传播(BP)神经网络、传统的极限学习机(ELM)和支持向量机(SVM)的结果进行了比较。测试结果表明，本文提出的方法在诊断准确率方面优于其他三种方法。
Rolling bearings are key components of rotary machines. To ensure early effective fault diagnosis for bearings, a new rolling bearing fault diagnosis method based on variational mode decomposition (VMD) and an improved kernel extreme learning machine (KELM) is proposed in this paper. A fault signal is decomposed via VMD to obtain the intrinsic mode function (IMF) components, and the approximate entropy (ApEn) of the IMF component containing the main fault information is calculated. An eigenvector is created from the approximate entropy of each component. A bearing diagnosis model is created via a KELM; the KELM parameters are optimized using the particle swarm optimization (PSO) algorithm to obtain a KELM diagnosis model with optimal parameters. Finally, the effectiveness of the diagnosis method proposed in this paper is verified via a fan bearing fault diagnosis test. Under identical conditions, the result is compared with the results obtained using a back propagation (BP) neural network, a conventional extreme learning machine (ELM), and a support vector machine (SVM). The test result shows that the method proposed in this paper is superior to the other three methods in terms of diagnostic accuracy.