Abstract:Aiming at the problems that swarm intelligence algorithm optimization Support Vector Machine model (SVM)is easy to fall into local optimum and low accuracy in the field of rolling bearing fault diagnosis, a method of optimizing Support Vector Machine (SVM) based on improved Sparrow Search Algorithm (SSA) is proposed for fault diagnosis of rolling bearings. First, the evenly distributed Chebyshev chaotic map is introduced to initialize the sparrow population in order to improve the spatial distribution uniformity of the population. Then, the adaptive inertia weights are integrated into the location update of the discoverer of the sparrow algorithm. Finally, the optimal sparrow after the updated position is randomly disturbed to improve the global and local search ability of the algorithm and avoid falling into the local optimization. The algorithm is applied to the parameter optimization of support vector machine, and the ISSA-SVM fault diagnosis model is constructed to realize the classification and diagnosis of bearing fault signals. The analysis results of the rolling bearing fault diagnosis test show that the fault classification effect of the ISSA-SVM model is obviously better than that of the PSO-SVM, GA-SVM and SSA-SVM models, which can effectively identify the types of faults of rolling bearings.
李昕燃,靳伍银. 基于改进麻雀算法优化支持向量机的滚动轴承故障诊断研究[J]. 振动与冲击, 2023, 42(6): 106-114.
LI Xinran,JIN Wuyin. Fault diagnosis of rolling bearings based on ISSA-SVM. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(6): 106-114.
[1] 路小娟,石成基. 一种基于概率盒—HGWO优化SVM的滚动轴承故障诊断方法[J]. 振动与冲击,2021, 40(22): 234-241.
LU Xiaojuan, SHI Chengji. A rolling bearing fault diagnosis method based on probability box-HGWO optimized SVM[J]. Vibration and Shock, 2021, 40(22): 234-241.
[2] MA C, GU X, WANG Y. Fault diagnosis of power electronic system based on fault gradation and neural network group[J]. Neurocomputing, 2008, 72(13): 2909-2914.
[3] 陈如清,沈士根. 基于递归神经网络的旋转机械故障诊断方法[J]. 振动、测试与诊断,2005, 25(03): 70-72.
CHEN Ruqing, SHEN Shigen. Rotating machinery fault diagnosis method based on recurrent neural network[J]. Journal of Vibration, Testing & Diagnosis, 2005, 25(03): 70-72.
[4] WU S D, WU C W, WU T Y, et al. Multi-Scale Analysis Based Ball Bearing Defect Diagnostics Using Mahalanobis Distance and Support Vector Machine[J]. Entropy, 2013, 15(2): 416-433.
[5] CHEN F F, TANG B P, SONG T, et al. Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization[J]. Measurement, 2014, 47(1): 576-590.
[6] ZHANG M J, CHAI K, HUANG J, et al. Combined Improved EEMD with SVM in the Bearing Low Dimensional Small Sample Fault Diagnosis[J]. Applied Mechanics and Materials, 2013, 2748(427-429): 354-357.
[7] 吐松江•卡日,高文胜,张紫薇,等.基于支持向量机和遗传算法的变压器故障诊断[J]. 清华大学学报(自然科学版), 2018, 58(07): 623-629.
TUSONGJIANG•Kari, GAO Wensheng, ZHANG Ziwei, et al. Transformer fault diagnosis based on support vector machine and genetic algorithm[J]. Journal of Tsinghua University(Natural Science Edition), 2018, 58(07): 623-629.
[8] LIU Z, CAO H, CHEN X, et al. Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings[J]. Neurocomputing, 2013, 99(1): 399-410.
[9] TANG X, ZHUANG L, CAI J, et al. Multi-fault classification based on support vector machine trained by chaos particle swarm optimization[J]. Knowledge-Based Systems, 2010, 23(5): 486-490.
[10] XUE J K, SHEN B. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering, 2020, 8(1): 22-34.
[11] LIU L, SUN S Z, YU H, et al. A modified Fuzzy C-Means (FCM) Clustering algorithm and its application on carbonate fluid identification[J]. Journal of Applied Geophysics, 2016, 129: 28-35.
[12] 黄滨,保利勇,丁洪伟. 基于均匀化分布的Chebyshev映射系统构建及特性分析[J]. 计算机应用, 2019, 39(10): 2997-3001.
HUANG Bin, BAO Liyong, DING Hongwei. Construction and characteristic analysis of Chebyshev mapping system based on uniform distribution[J]. Journal of Computer Applications, 2019, 39(10): 2997-3001.
[13] 段玉先,刘昌云. 基于Sobol序列和纵横交叉策略的麻雀搜索算法[J/OL]. 计算机应用: 1-9[2021-12-22].
DUAN Yuxian, LIU Changyun. Sparrow search algorithm based on Sobol sequence and crisscross strategy [J]. Computer Application,2022,42(1):36-43.
[14] SHI Y, EBERHART R C. A Modified Particle Swarm Optimizer[C]//Proc of IEEE International Conference on Evolutionary Computation Proceedings. Piscataway, NJ: IEEE Press, 1998: 69-73.
[15] 廉小亲,刘钰,陈彦铭,等. 基于自适应粒子群算法的多峰谱线分离方法研究[J]. 光谱学与光谱分析, 2021, 41(05): 1452-1457.
LIAN Xiaoqin, LIU Yu, CHEN Yanming, et al. Research on multi-peak spectral line separation method based on adaptive particle swarm optimization[J]. Spectroscopy and Spectral Analysis, 2021, 41(05): 1452-1457.
[16] GRADY L. Multilable random walker image segmentation using prior models[Z]. IEEE Computer Society Conference on Computer Vision & Pattern Recognition, San Diego, USA, 2005.
[17] 张小龙,张氢,秦仙蓉,等. 基于ITD复杂度和PSO-SVM的滚动轴承故障诊断[J]. 振动与冲击, 2016, 35(24): 102-107+138.
ZHANG Xiaolong, ZHANG Qin, QIN Xianrong, et al. Fault diagnosis of rolling bearing based on ITD complexity and PSO-SVM[J]. Journal of Vibration and Shock, 2016, 35(24): 102-107+138.
[18] Case Western Reserve University Bearing Data Center. Bearing data center fault test data [DB/OL]. [2009-10-01]. http://www.eecs.case.edu/aboratory/bearing.