Fault diagnosis research of gearbox based on ICEEMDAN and PFA-ELM
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摘要: 齿轮箱是工业设备中常用的传动部件。针对齿轮箱故障特征提取及诊断精度不足的问题，提出一种基于改进的自适应噪声完备集合经验模态分解（ICEEMDAN）及探路者算法（PFA）优化极限学习机（ELM）的故障诊断方法。首先，利用ICEEMDAN对信号进行分解，得到多个本征模态函数（IMF）。其次，基于斯皮尔曼相关系数，筛选出有效的IMF，并计算出每个有效IMF的模糊熵和排列熵作为故障特征向量。最后，利用PFA算法优化ELM中的权值和阈值，构建基于PFA-ELM的故障诊断模型。实验表明，PFA-ELM的故障诊断精度高达98.67%。该方法能够准确描述齿轮箱的工作状态，具有较高的实用价值。
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关键词:
- 故障诊断 /
- 齿轮箱 /
- 改进的自适应噪声完备集合经验模态分解 /
- 探路者算法 /
- 极限学习机
Abstract: Gearboxes are commonly used transmission components in industrial equipment. To address the problem of insufficient gearbox fault feature extraction and diagnosis accuracy, a fault diagnosis method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and pathfinder algorithm (PFA) optimized extreme learning machine (ELM) is proposed. First, the signal is decomposed using ICEEMDAN to obtain intrinsic mode functions (IMFs). Secondly, based on the Spearman correlation coefficient, the valid IMFs are screened out and the fuzzy entropy and permutation entropy of each valid IMF are calculated as the fault feature vector. Finally, the PFA algorithm is used to optimize the weights and biases in ELM and construct a fault diagnosis model based on PFA-ELM. Experiments show that the fault diagnosis accuracy of PFA-ELM is as high as 98.67%. The method can accurately describe the working condition of gearboxes and has high practical value. -
表 1 IMF分量与正常信号斯皮尔曼相关系数
IMF分量名 斯皮尔曼相关系数 IMF1 0.699 6 IMF2 0.401 9 IMF3 0.256 3 IMF4 0.195 6 IMF5 0.139 3 IMF6 0.114 9 IMF7 0.053 4 IMF8 0.046 5 IMF9 0.051 8 IMF10 0.030 0 IMF11 0.005 7 
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表 3 不同算法诊断结果对比
齿轮箱
状态故障
标签标签识别数量 PFA-ELM WOA-ELM GWO-ELM PSO-ELM ELM 正常状态 1 14 14 14 14 14 小齿轮断齿 2 15 13 11 11 11 小齿轮磨损 3 15 15 15 15 12 大齿轮断齿 4 15 13 13 12 11 大小齿轮均
断齿5 15 15 15 15 12 诊断正确率/(%) 98.67 93.33 90.67 89.33 80
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