Abstract:In order to solve the problem of traditional centrifugal pump fault diagnosis using only a single vibration signal and unable to comprehensively utilize the correlation information of multi-physics, this paper proposes a fault diagnosis based on the combination of multi-physical field signals correlation analysis and support vector machine (SVM) method. First, normalize the collected multi-physics signals of the centrifugal pump in different states; secondly, calculate the correlation of any two normalized multi-physics signals and form the correlation matrix; and finally, Using the correlation matrix as a feature to use SVM for diagnosis. In order to verify the effectiveness of the method proposed in this paper, the failure data of g centrifugal pump is used to verify the proposed method. The results show that: compared with the fault diagnosis method that only uses a single signal, the method proposed in this paper can fully extract the multi-physics correlation information of the centrifugal pump, feature extraction is more sufficient, and effectively improve the fault diagnosis rate of the centrifugal pump.
孙原理1,2,宋志浩2. 基于多物理场信号相关分析与支持向量机的离心泵故障诊断方法[J]. 振动与冲击, 2022, 41(6): 206-212.
SUN Yuanli1,2,SONG Zhihao2. Centrifugal pump fault diagnosis based on the multi-physical field signals correlation analysis and support vector machine. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(6): 206-212.
[1] 李涛, 段礼祥, 张东宁, 等. 自适应卷积神经网络在旋转机械故障诊断中的应用[J]. 振动与冲击, 2020, 39(16):275–282+288.
LI Tao, DUAN Lixiang, ZHANG Dongning, etal. Application of adaptive convolutional neural network in rotating machinery fault diagnosis [J]. Journal of Vibration and Shock, 2020, 39(16):275–282+288.
[2] 李恒, 张氢, 秦仙蓉, 等. 基于短时傅里叶变换和卷积神经网络的轴承故障诊断方法[J]. 振动与冲击, 2018, 37(19):124–131.
LI Heng, ZHANG Qin, QIN Xianrong, et al. Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neural network[J]. Journal of Vibration and Shock, 2018, 37(19):124–131.
[3] Lu C, Wang Y, Ragulskis M, Cheng Y. Fault diagnosis for rotating machinery: A method based on image processing, PloS One, 2016, 11(10), E0164111.
[4] Wen L, Li X Y, Gao L, Zhang Y Y. A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method. IEEE Transactions on Industrial Electronics, 2018, 65(7): 5990 – 5998.
[5] Wen L, Gao L, Li X Y. A New Snapshot Ensemble Convolutional Neural Network for Fault Diagnosis. IEEE Access, 2019, 7:32037–32047.
[6] Chen Z H, Chen J, Xiong J B. Rolling Bearing Fault Diagnosis Using Time-Frequency Analysis and Deep Transfer Convolutional Neural Network. IEEE Access, 2020, 8: 150248 – 150261.
[7] Liu Q, Huang C X. A Fault Diagnosis Method Based on Transfer Convolutional Neural Networks. IEEE Access, 2019, 7: 171423 –171430.
[8] Zhang Y Y, Li X Y, Gao L, Chen W, Li P G. Ensemble deep contractive auto-encoders for intelligent fault diagnosis of machines under noisy environment. Knowledge-Based Systems, 96, 2020, 105764.
[9] 黄海松, 魏建安, 任竹鹏, 等. 基于失衡样本特性过采样算法与SVM的滚动轴承故障诊断[J]. 振动与冲击, 2020, 39(10):65–74+132.
HUANG Haisong, WEI Jian’an, REN Zhupeng, et al. Rolling bearing fault diagnosis based on imbalanced sample characteristics oversampling algorithm and SVM[J]. Journal of Vibration and Shock, 2020, 39(10):65–74+132.
[10] Widodo A, Yang B-S. Support vector machine in machine condition monitoring and fault diagnosis[J]. Mechanical systems and signal processing, 2007, 21(6):2560–2574.
[11] V. N. Vapnik. The nature of statistical learning theory[M]. 1995, Berlin, Germany: Springer-Verlag.
[12] 王新, 闫文源. 基于变分模态分解和SVM的滚动轴承故障诊断[J]. 振动与冲击, 2017, 36(18):252–256.
WANG Xin, YAN Wenyuan. Fault diagnosis of roller bearings based on the variational mode decomposition and SVM[J]. Journal of Vibration and Shock, 2017, 36(18):252–256.
[13] 何大伟, 彭靖波, 胡金海, 等. 基于改进FOA优化的CS- SVM轴承故障诊断研究[J]. 振动与冲击, 2018, 37(18):108–114.
HE Dawei, PENG Jingbo, HU Jinhai, et al. Bearing fault diagnosis based on a modified CS-SVM model optimized by an improved FOA algorithm[J]. Journal of Vibration and Shock, 2018, 37(18):108–114.
[14] 饶雷, 唐向红, 陆见光. 基于CNN-SVM和特征融合的齿轮箱故障诊断[J]. 组合机床与自动化加工技术, 2020(08):130–133+142.
RAO Lei, TANG Xianghong, LU Jianguang. Gearbox Fault Diagnosis Based on CNN-SVM and Feature Fusion [J]. Modular Machine Tool & Automatic Manufacturing Technique, 2020(08):130–133+142.
[15] Zhang M, Jiang Z, Feng K. Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump[J]. Mechanical systems and signal processing, 2017, 93:460–493.
[16] Muralidharan V, Sugumaran V, Indira V. Fault diagnosis of monoblock centrifugal pump using SVM [J]. Engineering Science and Technology, an International Journal, 2014, 17(3):152–157.
[17] Azadeh A, Saberi M, Kazem A, et al. A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization[J]. Applied Soft Computing, 2013, 13(3):1478–1485.
[18]杨林.自吸泵故障分析及改造处理.化工设备与管道. 2018, 55(6): 50–52
YANG Lin. Analysis of Faults Occurred in Self Priming Pump and Reconstruction of Machine. Process Equipment & Piping. 2018, 55(6): 50–52
[19] Zhang X, Liang Y, Zhou J. A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM[J]. Measurement, 2015, 69:164–179.
[20] Yin Z, Hou J. Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes[J]. Neuro Computing, 2016, 174:643–650.
[21]Chang C-C, Lin C-J. LIBSVM: A library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3):1–27.
[22] Nguyen P H, Kim J-M. Multifault diagnosis of rolling element bearings using a wavelet kurtogram and vector median-based feature analysis [J]. Shock and Vibration, 2015, 2015: 1–14.
[23] VanderMaaten L, Hinton G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9: 2579–2605.