JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2023, Vol. 58 ›› Issue (5): 63-75.doi: 10.6040/j.issn.1671-9352.0.2022.130

Previous Articles    

Signal detection and fault diagnosis based on improved particle swarm optimization algorithm

ZHANG Jinke, ZHANG Jiangang*   

  1. School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China
  • Published:2023-05-15

PDF (PC)

210

Abstract: A piecewise bistable stochastic resonance system is investigated, and the parameters of the bistable system are optimized by using the improved particle swarm optimization(PSO)algorithm, which is applied to weak signal detection and bearing fault diagnosis. First, the piecewise potential function is introduced, and the output signal-to-noise ratio of the system is derived theoretically. Next, the effect of the average first passage time and signal-to-noise ratio on the system parameters from the transition of particles in the potential wells are discussed and analyzed, and the model is evaluated. Second, by combining the random weight particle swarm optimization algorithm and adaptive weight particle swarm optimization algorithm with stochastic resonance, and taking the signal-to-noise ratio of the output signal as the evaluation index, the system parameters are optimized and adjusted. The improved algorithms of the two particle swarm optimization algorithms are compared. Finally, the improved particle swarm optimization algorithm is applied to fault diagnosis. The effectiveness and superiority of the random weight particle swarm optimization algorithm are verified by comparing the output effects of several algorithms.

Key words: piecewise potential function, stochastic weighted particle swarm optimization algorithm, weak signal detection, fault diagnosis

CLC Number: 

  • O193
[1] 吴正毅.测试技术与测试信号处理[M].北京:清华大学出版社,1991: 186-238. WU Zhengyi. Test technology and test signal processing[M]. Beijing: Tsinghua University Press, 1991: 186-238.
[2] 蔡剑华,胡惟文,王先春.基于高阶统计量的大地电磁数据处理与仿真[J].中南大学学报(自然科学版),2010,41(4):1556-1560. CAI Jianhua, HU Weiwen, WANG Xianchun. Magnetotelluric data processing and simulation based on higher-order statistics[J]. Journal of Central South University(Natural Science), 2010, 41(4):1556-1560.
[3] 郑安总.奇异值分解在微弱信号检测中的应用[D].天津:天津大学,2014. ZHENG Anzong. The application of singular value decomposition in weak signal detection[D]. Tianjin: Tianjin University, 2014.
[4] ROBERTO B, ALFONSO S, ANGELO V. The mechanism of stochastic resonance[J]. Journal of Physics A: Mathematical and General, 1981,14(11):L453-L457.
[5] FAUVE S, HESLOT F. Stochastic resonance in a bistable system[J]. Physics Letters A, 1983, 97(1/2):5-7.
[6] HE Qingbo, WANG Jun, LIU Yongbin, et al. Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines[J]. Mechanical Systems and Signal Processing, 2012, 28(2):443-457.
[7] LI Huafeng, BAO Ronghao, XU Bohou, et al. Intrawell stochastic resonance of bistable system[J]. Journal of Sound and Vibration, 2004, 272(1):155-167.
[8] TAN Jiyong, CHEN Xuefeng, WANG Junying, et al. Study of frequency-shifted and re-scaling stochastic resonance and its application to fault diagnosis[J]. Mechanical Systems and Signal Processing, 2009, 23(3):811-822.
[9] CALISTO H, CLERC M G. A new perspective on stochastic resonance in monostable systems[J]. New Journal of Physics, 2010, 12(11):113027.
[10] JHA R K, CHOUHAN R. Dynamic stochastic resonance-based grayscale logo extraction in hybrid SVD-DCT domain[J]. Journal of the Frankin Institute, 2014, 351(5):2938-2965.
[11] 朱利民,钟秉林,贾民平.振动信号短时分析方法及在机械故障诊断中的应用[J].振动工程学报,2000,13(3):400-405. ZHU Limin, ZHONG Binglin, JIA Minping. Short-time analysis of vibration signal and its application in mechanical fault diagnosis[J]. Journal of Vibration Engineering, 2000, 13(3):400-405.
[12] 林京,屈梁生.基于连续小波变换的信号检测技术与故障诊断[J].机械工程学报,2000,36(12):95-100 LIN Jing, QU Liangsheng. Signal detection technology and fault diagnosis based on continuous wavelet transform[J]. Journal of Mechanical Engineering, 2000, 36(12):95-100.
[13] 冯志鹏,褚福磊.行星齿轮箱齿轮分布式故障振动频谱特征[J].中国电机工程学报,2013,33(2):118-125. FENG Zhipeng, CHU Fulei. Vibration spectrum characteristics of distributed gear faults in planetary gearboxes[J]. Proceedings of the CSEE, 2013, 33(2):118-125.
[14] 康健.基于欠阻尼随机共振势模型的微弱信号检测方法及其应用[D].包头:内蒙古科技大学,2020. KANG Jian. Weak signal detection method based on underdamped stochastic resonance potential model and application[D]. Baotou: Inner Mongolia University of Science & Technology, 2020.
[15] HAN Dongying, SHI Peiming. Study on the mean first-passage time and stochastic resonance of a multi-stable system with colored correlated noises[J]. Chinese Journal of Physics, 2021, 69:98-107.
[16] LI Mengdi, SHI Peiming, ZHANG Wenyue, et al. A novel underdamped continuous unsaturation bistable stochastic resonance method and its application[J]. Chaos, Solitons & Fractals, 2021, 151:111228.
[17] KIM H, TAI W, PARKER J, et al. Self-tuning stochastic resonance energy harvesting for rotating systems under modulated noise and its application to smart tires[J]. Mechanical Systems and Signal Processing, 2019, 122:769-785.
[18] FU Yuxuan, KANG Yanmei, LIU Ruonan. Novel bearing fault diagnosis algorithm based on the method of moments for stochastic resonant systems[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 70:6500610.
[19] MA Jianpeng, LI Chengwei, ZHANG Guangzhu. Adaptive stochastic resonance-based processing of weak magnetic slippage signals of bearings[J]. Entropy, 2022, 24(2):147.
[20] HUANG Dawen, YANG Jianhua, ZHOU Dengji, et al. Novel adaptive search method for bearing fault frequency using stochastic resonance quantified by amplitude-domain index[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 69(1):109-121.
[21] HUANG Dawen, YANG Jianhua, ZHOU Dengji, et al. Recovering an unknown signal completely submerged in strong noise by a new stochastic resonance method[J]. Communications in Nonlinear Science and Numerical Simulation, 2019, 66:156-166.
[22] XU Pengfei, JIN Yanfei. Stochastic resonance in an asymmetric tristable system driven by correlated noises[J]. Applied Mathematical Modelling, 2020, 77:408-425.
[23] ZHANG Gang, LIU Xiaoman, ZHANG Tianqi. Two-dimensional tri-stable stochastic resonance system and its application in bearing fault detection[J]. Physica A: Statistical Mechanics and its Applications, 2022: 126855.
[24] LU Siliang, HE Qingbo, ZHANG Haibin, et al. Enhanced rotating machine fault diagnosis based on time-delayed feedback stochastic resonance[J]. Journal of Vibration and Acoustics, 2015, 137(5):051008.
[25] 贺利芳,朱伟,张天骐.分段非对称随机共振系统微弱信号检测[J].振动与冲击,2022,41(5):114-122. HE Lifang, ZHU Wei, ZHANG Tianqi. Detection of weak signals in piecewise asymmetric stochastic resonance system[J]. Journal of Vibration and Shock, 2022, 41(5):114-122.
[1] LIU Meng-Jun, XIU Li-Meng, LI Jin-Bing-Bang-Chi-Yuan. Crack fault diagnosis based on MEP and frequency contour lines [J]. J4, 2009, 44(9): 22-27.
[2] CHEN Li . The robust fault diagnosis filter design for uncertain singular systems [J]. J4, 2007, 42(7): 62-65 .
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE)
Supported by Beijing Magtech Co.Ltd