基于SSA和双稳随机共振的车削颤振微弱特征提取

摘要
图/表
参考文献(20)
相关文章 (15)

全文: PDF (2509 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要围绕车削颤振激发过程，针对车削颤振过渡阶段特征微弱且存在噪声和转频干扰的问题，提出一种基于麻雀优化算法(Sparrow search algorithm，SSA)和双稳随机共振(Bistable Stochastic resonance，BSR)系统的车削颤振微弱特征提取方法。该方法利用SSA优良的寻优特性，以信噪比作为优化指标确定最佳双稳随机共振(BSR)系统参数，用优化后的参数对车削颤振信号进行滤波处理，提取颤振特征频率。仿真实验表明SSA-BSR方法可以实现对强噪声背景下微弱特征信号的提取与增强，同时兼顾寻优速度快和获得全局最优解概率高的优点。开展车削颤振检测实验，颤振激发过程的加速度信号图谱分析结果验证了车削颤振特征频率幅值与颤振激发程度之间的相关性；不同模型在车削颤振过渡阶段特征提取的对比结果验证了SSA-BSR模型的有效性和优越性，并且能够满足车削颤振检测对实时性的要求，实现在进入剧烈颤振阶段前发出预警，为量化车削颤振和车削颤振在线监测提供一种新思路。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	吴飞
	栾天宇
	农皓业

关键词 ：车削颤振, 随机共振, 麻雀优化算法, 信号处理

Abstract：A signal processing method based on sparrow search algorithm (SSA) and bistable stochastic resonance (BSR) was proposed for extracting the weak features in the transition phase of turning chatter. This method used signal-to-noise ratio as the optimization index to determine the optimal bistable stochastic resonance system parameters, which were used to filter the turning chatter signal and extract the chatter characteristic frequency. The simulation analysis showed that the SSA-BSR method can extract and enhance the weak feature signal in the strong noise background, while taking into account the advantages of optimization efficiency. The analysis of the chatter acceleration signal verified the correlation between the amplitude of the chatter characteristic frequency and the degree of chatter excitation. The comparison of the feature extraction effect of different models in the transition stage of chatter verified the effectiveness and superiority of the SSA-BSR model, and it can meet the requirement of real-time detection of chatter. This method provided a new idea for quantifying turning chatter and online monitoring of turning chatter.

Key words： turning chatter stochastic resonance sparrow search algorithm signal processing

收稿日期: 2023-04-10 出版日期: 2024-02-28

引用本文:

吴飞，栾天宇，农皓业. 基于SSA和双稳随机共振的车削颤振微弱特征提取[J]. 振动与冲击, 2024, 43(4): 134-141.
WU Fei，LUAN Tianyu，NONG Haoye. Research on weak feature extraction of turning chatter base on SSA and bistochastic resonance. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(4): 134-141.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2024/V43/I4/134

[1] 孙宇昕. 车削颤振的实时辨识与在线抑制系统研究[D]. 上海交通大学,2017. SUN Yuxin. Research on real-time chatter detection and on-line suppression for turning [D]. Shanghai, China: School of Mechanical Engineering, Shanghai Jiao Tong University, 2017. [2] Benzi R, Parisi G, Sutera A, et al. Stochastic resonance in climatic change[J].Tellus A: Dynamic Meteorology and Oceanography,1982, 34 (1). [3] 王慧，张刚，张天骐.改进型双稳随机共振系统及其在轴承故障诊断的应用[J].西安交通大学学报,2020, 第54卷 (第4期): 110-117. WANG Hui, ZHANG Gang, ZHANG Tianqi. Improved bistable stochastic resonance system and its application in bearing fault diagnosis [J]. Journal of Xi'an Jiaotong University, 2020, 54(4): 110-7. [4] 张刚，徐浩，张天骐.二维双阱势系统随机共振机理研究及应用[J].振动与冲击,2021, 第40卷 (第12期): 15-22. ZHANG Gang, XU Hao, ZHANG Tianqi. A study and application of stochastic resonance mechanism in a two-dimensional bistable potential system [J]. Journal of Vibration and Shock, 2020, 40(12) : 15-22. [5] 范志刚，林敏，黄咏梅，等.基于补偿控制的随机共振特性及其应用研究[J].振动与冲击,2020, 第39卷 (第23期): 64-70. FAN Zhigang, LIN Min, HUANG Yongmei, et al. Compensa -tion control-based stochastic resonance characteristics and their application [J]. Journal of Vibration and Shock, 2020, 39(23) : 64-70. [6] 王衡. 基于二阶随机共振的微弱故障特征提取研究[D]. 大连交通大学,2019. WANG Heng. Weak fault feature extraction based on second-order stochastic resonance [D]. Dalian, Liaoning, China: Dalian Jiaotong University, 2019. [7] 张建磊，兰香，杨祎，等.水下激光通信弱光信号的自适应随机共振检测[J].光学精密工程,2022, 第30卷 (第12期): 1383-1393. ZHANG Jianlei, LAN Xiang, YANG Yi, et al. Adaptive stochastic resonance detection of weak light signal in underwater laser communication [J]. Optics and Precision Engineering, 2022, 30(12): 1383-93. [8] 崔伟成，李伟，孟凡磊，等.基于果蝇优化算法的自适应随机共振轴承故障信号检测方法[J].振动与冲击,2016, 第35卷 (第10期): 96-100，114. CUI Weicheng, LI Wei, MENG Fanlei, et al. Adaptive stochastic resonance method for bearing fault detection based on fruit fly optimization algorithm [J]. Journal of Vibration and Shock, 2016, 35(10) : 96-100, 114. [9] 尹进田，唐杰，刘丽，等.参数同步优化随机共振在牵引传动系统早期微弱故障诊断中的应用[J].振动与冲击,2021, 第40卷 (第17期): 234-240，278. YIN Jintian，TANG Jie，LIU Li, et al. Application of parameter synchronous optimization stochastic resonance in early weak fault diagnosis of traction drive system [J]. Journal of Vibration and Shock, 2021, 40(17) : 234-240, 278. [10] 姜增辉，谢峰，王海宁.归一化变尺度随机共振的刀具状态监测[J].机械科学与技术,2020, 第39卷 (第10期): 1520-1525. JIANG Zenghui, XIE Feng, WANG Haining. Condition Monitoring of Tools with Normalized Variable-scale Stochastic Resonance [J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(10) : 1520-1525. [11] 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, 第41卷 (第5期): 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. [12] 张刚，李红威.混合多稳态随机共振的故障信号检测[J].振动与冲击,2019, 第38卷 (第18期): 9-17. ZHANG Gang, LI Hongwei. Hybrid tri-stable stochastic resonance system used for fault signal detection [J]. Journal of Vibration and Shock, 2019, 38(18) : 9-17. [13] Cui H, Guan Y, Chen H, et al.A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection [J]. Applied Sciences,2021, Vol.11 (No.5385): 5385. [14] 经哲，郭利.基于广义相关系数自适应随机共振的液压泵振动信号预处理方法[J].振动与冲击,2016, 第35卷 (第16期): 72-78，85. JING Zhe，GUO Li. Hydraulic pump vibration signal pretreatment based onadaptive stochastic resonance with a general correlation function [J]. Journal of Vibration and Shock, 2016, 35(16): 72-8+85. [15] 高康平，徐信芯，师宁，等.基于SSA和随机共振的旋转机械微弱信号提取[J].噪声与振动控制,2022, 第42卷 (第1期): 112-118. GAO Kangping, XU Xinxin, SHI Ning, et al. Research on Weak Signal Extraction of Rotating Machinery Based on SSA and Stochastic Resonance [J]. Noise and Vibration Control, 2022, 42(01): 112-8. [16] 李国英, 王诗彬, 杨志勃, 等.自适应多尺度噪声调节二阶随机共振增强方法[J].振动与冲击,2022, 41 (12): 8-15. LI Guoying, WANG Shibin, YANG Zhibo, et al. An adaptive multiscale noise tuning second order stochastic resonance enhanced method [J]. Journal of Vibration and Shock, 2022, 41(12): 8-15. [17] 曹衍龙，杨毕玉，杨将新，等.基于变尺度随机共振的冲击信号自适应提取与识别方法[J].振动与冲击,2016, 第35卷 (第5期): 65-69. CAO Yanlong, YANG Biyu, YANG Jiangxin, et al. Impact signal adaptive extraction and recognition based on a scale transformation stochastic resonance system [J]. Journal of Vibration and Shock, 2016, 35(5): 65-69. [18] 王俊. 基于多尺度非线性状态特征增强的旋转机械故障诊断方法研究[D]. 中国科学技术大学,2015. WANG Jun. Methodology of fault diagnosis of rotating machinery via multiscale and nonlinear condition feature enhancement [D]. Hefei, Anhui, China: University of Science and Technology of China, 2015. [19] Xue J, Shen B.A novel swarm intelligence optimization approach: sparrow search algorithm[J].Systems Science & Control Engineering,2020, 8 (1): 22-34. [20] 成珂，张璐，杨力人，等.灰色关联与麻雀优化算法预测光伏发电量研究[J].电源技术,2022, 第46卷 (第7期): 797-801. CHENG Ke, ZHANG Lu, YANG Liren, et al. Research on prediction of photovoltaic power generation by grey correlation and sparrow optimization algorithm [J]. Chinese Journal of Power Sources, 2022, 46(7): 797-801.