Research on Gearbox Fault Diagnosis Method based onParameter Optimized VMD and CNN
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摘要: 针对齿轮箱的故障诊断的优化问题，提出了一种基于参数优化的变分模态分解（VMD）与卷积神经网络（CNN）相融合的故障诊断方法。该算法首先通过鲸鱼优化算法对VMD算法进行优化，之后通过正交实验法与粒子群优化算法进行了CNN模型中的重要参数进行优化，最后将分解后得到的固有模态分量输入CNN模型中进行训练学习。诊断完成后得到训练与检测结果，其中经过算法优化后CNN模型的训练与检测准确率可达98.7%与95.7%，优于未优化的准确率94.3%与91.8%。通过对结果的分析验证出该算法的可行性以及在诊断成功率方面的优越性，实现了故障特征信息的自适应性提取，并将故障类型进行分类，最终实现齿轮箱故障诊断的智能化。Abstract: Aiming at the optimization problem of gearbox fault diagnosis, a fault diagnosis method based on parameter optimization of variational mode decomposition (VMD) and convolutional neural network (CNN) is proposed. Firstly, the VMD algorithm is optimized by whale optimization algorithm. Then, the important parameters of CNN model are optimized by orthogonal experiment and particle swarm optimization algorithm. Next, the decomposed natural mode components are input into CNN model for training. After the diagnosis, the training and detection results are obtained. After the algorithm optimization, the training and detection accuracy of CNN model can reach 98.7% and 95.7% respectively, which is better than the non optimization accuracy of 94.3% and 91.8%. Through the analysis of the results, the feasibility of the proposed method and the superiority in the success rate of diagnosis are verified. The adaptive extraction of fault feature information is realized, and the fault types are classified. Finally, the intelligent fault diagnosis on gearboxis realized.
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Key words:
- gearbox /
- fault diagnosis /
- variational mode decomposition /
- convolutional neural network
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表 2 正交实验结果
编号 A B C D 准确率/% 时间t 1 5 3 0.001 0.1 77.2 78.03 2 5 5 0.010 0.2 94.1 42.48 3 5 7 0.100 0.3 83.4 38.49 4 10 3 0.010 0.3 88.7 80.04 5 10 5 0.100 0.1 61.2 213.75 6 10 7 0.001 0.2 96.4 189.20 7 15 3 0.100 0.2 63.0 267.36 8 15 5 0.001 0.3 91.3 280.19 9 15 7 0.010 0.1 60.2 645.27 
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表 3 实验数据分析统计结果
参数 A B C D E1 254.7%（159.00） 228.9%（425.43） 264.9%（547.42） 198.6%（937.05） E2 246.3%（482.99） 246.6%（536.42） 243.0%（767.79） 253.5%（499.04） E3 214.5%（1192.87） 240.0%（872.96） 207.6%（519.60） 263.4%（398.72） e1 84.9%（53.00） 76.3%（141.81） 88.3%（182.47） 66.2%（312.35） e2 82.1%（161.00） 82.2%（178.81） 81.0%（255.93） 84.5%（166.35） e3 71.5%（397.62） 80.0%（290.99） 69.2%（173.20） 87.8%（132.91） Q 13.4%（344.62） 5.9%（149.18） 19.1%（82.73） 21.6%（179.44）
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表 4 齿轮箱在齿轮故障中的CNN诊断成功率结果对比
优化结果 文献[17] 正交试验 正交试验+
MOPSO诊断成功率/% 95.4 96.1 98.9 运行时间/s 163.5 154.4 166.7
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表 5 训练实验结果统计
编号 样本个数 故障类型 诊断
成功率/%诊断偏差
平均值/mmA B C A B C 1 100 Z 96 94 99 − − − 2 100 V 97 95 99 − − − 3 100 N 96 93 98 − − − 4 100 M 95 95 99 − − − 5 100 J 97 94 99 − − − 6 100 K 95 95 98 − − − 7 500 Z1 − − − 0.85 1.04 0.63 8 500 Z2 − − − 0.74 1.10 0.65 9 500 Z3 − − − 0.82 0.99 0.61 10 500 Z4 − − − 0.74 1.11 0.58 11 500 Z5 − − − 0.81 0.98 0.59 12 500 Z6 − − − 0.77 1.03 0.66 13 500 V1 − − − 0.72 0.96 0.58 14 500 V2 − − − 0.76 1.05 0.65 15 500 V3 − − − 0.80 0.97 0.61 16 500 V4 − − − 0.82 1.04 0.62 17 500 V5 − − − 0.78 1.12 0.59 18 500 V6 − − − 0.81 1.01 0.65
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表 6 检测实验结果统计
编号 样本个数 故障类型 诊断
成功率/%诊断偏差
平均值/mmA B C A B C 19 100 X1 94 92 95 − − − 20 100 X2 93 91 96 − − − 21 100 X3 94 92 96 − − − 22 100 X4 93 93 96 − − − 23 100 X5 94 91 95 − − − 24 100 X6 95 92 96 − − − 25 500 ZX1 − − − 1.34 1.72 0.98 26 500 ZX2 − − − 1.29 1.69 1.10 27 500 VX3 − − − 1.37 1.74 1.06 28 500 VX4 − − − 1.41 1.66 1.02
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