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摘要
经验模态分解一类的递归算法所产生的模态混淆和端点效应将导致所获物理信息失真, 变分模态分解可改善这些问题. 但因其需预设参数, 对信号分解精度影响显著, 为此, 提出采用目标信号功率谱峰值所对应的频率以初始化变分模态分解所需中心频率, 借鉴经验模态分解递归模型, 基于能量截止法将变分模态分解改进为递归模式算法, 并采用粒子群优化算法对具有带宽约束能力的惩罚因子进行最优取值, 构成优化递归变分模态分解. 通过对比分析经验模态分解, 集成经验模态分解及优化递归变分模态分解在分解信号时的计算精度; 研究传统变分模态分解与优化递归变分模态分解在处理实际振动信号时计算速率. 结果表明: 优化递归变分模态分解在处理目标信号时精度最高, 与原分量相关性达99.9%; 与集成经验模态分解对比, 可由低至高将信号分解至不同频段, 物理意义更加清晰且不产生虚假模态; 处理实际非线性信号时, 优化递归变分模态分解无需预设分解模态个数, 相比于传统变分模态分解, 计算速率高12.5%—18.5%.-
关键词:
- 变分模态分解 /
- 非线性 /
- 信号处理
Abstract
Variational mode decomposition can improve traditional recursive algorithms, such as empirical mode decomposition, resulting modal aliasing and endpoint effects, but it has a significant influence on signal decomposition accuracy due to its pre-set parameters. The frequency corresponding to the peak value of the target signal power spectrum is proposed to initialize the center frequency required for the variational mode decomposition. The empirical mode decomposition and recursive model is used to improve the variational mode decomposition into the recursive mode algorithm based on the energy cutoff method. The group optimization algorithm optimally takes the penalty factor with bandwidth constraint ability to form an optimized recursive variational mode decomposition. By comparing with and analyzing empirical mode decomposition, integrating empirical mode decomposition and optimizing the computational accuracy of recursive variational mode decomposition in decomposing signals; studying traditional variational mode decomposition and optimizing recursive variational mode decomposition in dealing with actual vibration signals calculating rate, the results are obtained, showing that the optimized recursive variational mode decomposition has the highest accuracy when dealing with the target signal, and the correlation with the original component is 99.9%. Comparing with the integrated empirical mode decomposition, the signal can be decomposed into different frequency bands from low to high, and the physical meaning is clearer. No false modality is generated. When the actual nonlinear signal is processed, the optimized recursive variational mode decomposition does not need to preset the number of decomposition modes, and the calculation rate is 12.5%–18.5% higher than thay of the traditional variational mode decomposition.-
Keywords:
- variational mode decomposition /
- nonlinear /
- signal process
作者及机构信息
Authors and contacts
文章全文 : translate this paragraph
参考文献
[1] Ingerman E A, London R A, Heintzmann R, Gustafsson M G L 2019 J. Microsc. 273 11
[2] Banjade T P, Yu S, Ma J 2019 J. Seismol. 5 1
[3] Yang F, Shen X, Wang Z 2018 Entropy 20 8
[4] Lian J J, Zhuo L, Wang H J, Dong X F 2018 Mech. Syst. Sig. Process. 107 53 Google Scholar
[5] Klionskiy D M, Kaplun D I, Geppener V V 2018 Pattern Recognit Image Anal. 28 122 Google Scholar
[6] Chervyakov N, Lyakhov P, Kaplun D, Butusov D, Nagornov N 2018 Electronics 8 135
[7] Qiu X, Ren Y, Suganthan P N, Amaratunga G A J 2017 Appl. Soft Comput. 54 246 Google Scholar
[8] Sweeney K T, Mcloone S F, Ward T E 2013 IEEE Trans. Biomed. Eng. 60 97 Google Scholar
[9] Guo Y, Naik G R, Nguyen H 2017 IEEE Eng. Med. Biol. Soc. 2013 6812
[10] Wang Y, Liu F, Jiang Z S, He S L, Mo Q Y 2017 Mech. Syst. Sig. Process. 86 75 Google Scholar
[11] Xiong T, Bao Y, Zhongyi H U 2014 Neurocomputing 123 174 Google Scholar
[12] Dragomiretskiy K, Zosso D 2014 IEEE Trans. Sig. Process. 62 531
[13] Wang Y X, Markert R, Xiang J W, Zheng W G 2015 Mech. Syst. Sig. Process. 60 243
[14] Yang F R, Bi X, Li C C, Liu C F, Tian T 2019 Measurement 140 1 Google Scholar
[15] 郑小霞, 陈广宁, 任浩翰, 李东东 2019 振动与冲击 38 153
Zheng X X, Chen G N, Ren H H, Li D D 2019 J. Vib. Shock 38 153
[16] 唐贵基, 王晓龙 2015 西安交通大学学报 49 73 Google Scholar
Tang G J, Wang X L 2015 J. Xi'an Jiaotong Univ. 49 73 Google Scholar
[17] 刘备, 胡伟鹏, 邹孝, 丁亚军, 钱盛友 2019 物理学报 68 028702 Google Scholar
Liu B, Hu E P, Zou X, Ding Y J, Qian S Y 2019 Acta Phys. Sin. 68 028702 Google Scholar
[18] Baldini G, Steri G, Dimc F, Giuliani R 2016 Sensors 16 818 Google Scholar
[19] Chen X J, Yang Y M, Cui Z X, Shen J 2019 Energy 174 1110 Google Scholar
[20] Cui J, Yu R Z, Zhao D B, Yang J Y, Ge W C, Zhou X M 2019 Appl. Energy 247 480 Google Scholar
[21] Huang N E, Shen Z, Long S R 1998 Proc. Roy. Soc. A 454 903 Google Scholar
[22] Damerval C, Meignen S, Valerie P 2005 IEEE Signal Process Lett. 12 701 Google Scholar
[23] Cheng J S, Yu D J, Yang Y 2006 Mech. Syst. Sig. Process. 20 817 Google Scholar
[24] Kennedy J, Eberhart R 1995 IEEE Int. Conf. Neural Networks 4 1942
[25] 吕中亮 2016 博士学位论文(重庆: 重庆大学)
Lv Z L 2016 Ph. D. Dissertation (Chongqing: Chongqing University) (in Chinese)
[26] Mcfadden P D, Smith J D 1984 J. Sound Vib. 96 69 Google Scholar
[27] Smith W A, Randall R B 2015 Mech. Syst. Sig. Process. 64–65 100 Google Scholar
[28] Chen F, Shi T, Duan S K, Wang L D, Wu J G 2017 Signal Process. 142 423
[29] Chen F, Li X Y, Duan S K, Wang L D, Wu J G 2018 Digit. Signal Prog. 81 16 Google Scholar
[30] Chen F, Shao X D 2017 Signal Process. 133 213 Google Scholar
[31] Shao X D, Chen F 2019 Signal Process. 160 237 Google Scholar
施引文献
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图 1 递归VMD流程
Fig. 1. The recursive VMD diagram.
图 2 基于PSO优化改进递归VMD参数流程
Fig. 2. The process of using PSO to optimize recursive VMD parameter.
图 3 合成信号及其分量 (a) 分量s1; (b) 分量s2; (c) 分量s3; (d) 合成信号f
Fig. 3. Analog signal and its component waveform: (a) s1; (b) s2; (c) s3; (d) f.
图 4 EMD分解结果 (a) IMF1; (b) IMF2; (c) IMF3; (d) IMF4; (e) IMF5; (f) res
Fig. 4. The results of EMD: (a) IMF1; (b) IMF2; (c) IMF3; (d) IMF4; (e) IMF5; (f) res.
图 5 EEMD分解结果 (a) IMF1; (b) IMF2; (c) IMF3; (d) IMF4; (e) IMF5; (f) IMF6; (g) IMF7; (h) res
Fig. 5. The results of EEMD: (a) IMF1; (b) IMF2; (c) IMF3; (d) IMF4; (e) IMF5; (f) IMF6; (g) IMF7; (h) res.
图 6 ORVMD分解结果 (a) IMF1; (b) IMF2; (c) IMF3
Fig. 6. The results of ORVMD: (a) IMF1; (b) IMF2; (c) IMF3.
图 7 早期轴承内圈故障信号 (a) 时域; (b) 频谱
Fig. 7. Early inner race fault diagnosis signal.
图 8 故障信号EEMD分解结果 (a) IMF1; (b) IMF2; (c) IMF3; (d) IMF4; (e) IMF5; (f) IMF6; (g) IMF7; (h) IMF8; (i) IMF9; (j) IMF10; (k) IMF11; (l) IMF12
Fig. 8. The results of EEMD for fault signal: (a) IMF1; (b) IMF2; (c) IMF3; (d) IMF4; (e) IMF5; (f) IMF6; (g) IMF7; (h) IMF8; (i) IMF9; (j) IMF10; (k) IMF11; (l) IMF12.
图 9 故障信号ORVMD分解结果 (a) IMF1; (b) IMF2; (c) IMF3; (d) IMF4; (e) IMF5; (f) IMF6; (g) IMF7; (h) IMF8; (i) IMF9
Fig. 9. The results of ORVMD for fault diagnosis: (a) IMF1; (b) IMF2; (c) IMF3; (d) IMF4; (e) IMF5; (f) IMF6; (g) IMF7; (h) IMF8; (i) IMF9.
图 10 VMD与ORVMD计算耗时
Fig. 10. The duration of calculation for VMD and ORVMD.
表 1 ORVMD参数组合
Table 1. Parameter set of ORVMD.
编号 K, α 编号 K, α 1 12, 12100 14 12, 12400 2 12, 12900 15 12, 12000 3 12, 11800 16 12, 12000 4 12, 11900 17 13, 11900 5 12, 11800 18 12, 11900 6 12, 12700 19 12, 11400 7 12, 12100 20 12, 11900 8 12, 13100 21 13, 11900 9 12, 12100 22 12, 11900 10 12, 12000 23 12, 12000 11 11, 12000 24 12, 12000 12 11, 12000 25 11, 12100 13 12, 13200 — — 深圳坪山网站建设公司GOOGLE网站优化设计自己建个网站做优化苏州综合网站优化行业天河网站关键词优化价格绵竹网站优化推广南京网站排名优化软件价格没注册的公司网站能优化吗路北区网站优化多少钱廊坊网站搜索引擎优化阿图什网站seo优化平台黎川网站搜索引擎优化修水网站优化渠道省心的珠宝行业网站优化平台密云网站关键字优化我的世界基岩版动作优化网站乐平百度网站优化网站优化推广排名静海商城网站优化马鞍山市网站关键词优化哪家便宜厚街家具网站优化有哪些方法鹤壁优化网站排名费用情况孝南区网站排名优化哪里买银川市网站优化哪家值得信赖东门高端网站优化网站整站优化来看易速达昆明网站首页优化池州市网站建设优化独立网站的优化意见浙江光电网站优化产品介绍巢湖网站优化公司价格香港通过《维护国家安全条例》两大学生合买彩票中奖一人不认账让美丽中国“从细节出发”19岁小伙救下5人后溺亡 多方发声卫健委通报少年有偿捐血浆16次猝死汪小菲曝离婚始末何赛飞追着代拍打雅江山火三名扑火人员牺牲系谣言男子被猫抓伤后确诊“猫抓病”周杰伦一审败诉网易中国拥有亿元资产的家庭达13.3万户315晚会后胖东来又人满为患了高校汽车撞人致3死16伤 司机系学生张家界的山上“长”满了韩国人?张立群任西安交通大学校长手机成瘾是影响睡眠质量重要因素网友洛杉矶偶遇贾玲“重生之我在北大当嫡校长”单亲妈妈陷入热恋 14岁儿子报警倪萍分享减重40斤方法杨倩无缘巴黎奥运考生莫言也上北大硕士复试名单了许家印被限制高消费奥巴马现身唐宁街 黑色着装引猜测专访95后高颜值猪保姆男孩8年未见母亲被告知被遗忘七年后宇文玥被薅头发捞上岸郑州一火锅店爆改成麻辣烫店西双版纳热带植物园回应蜉蝣大爆发沉迷短剧的人就像掉进了杀猪盘当地回应沈阳致3死车祸车主疑毒驾开除党籍5年后 原水城县长再被查凯特王妃现身!外出购物视频曝光初中生遭15人围殴自卫刺伤3人判无罪事业单位女子向同事水杯投不明物质男子被流浪猫绊倒 投喂者赔24万外国人感慨凌晨的中国很安全路边卖淀粉肠阿姨主动出示声明书胖东来员工每周单休无小长假王树国卸任西安交大校长 师生送别小米汽车超级工厂正式揭幕黑马情侣提车了妈妈回应孩子在校撞护栏坠楼校方回应护栏损坏小学生课间坠楼房客欠租失踪 房东直发愁专家建议不必谈骨泥色变老人退休金被冒领16年 金额超20万西藏招商引资投资者子女可当地高考特朗普无法缴纳4.54亿美元罚金浙江一高校内汽车冲撞行人 多人受伤
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[1] Ingerman E A, London R A, Heintzmann R, Gustafsson M G L 2019 J. Microsc. 273 11
[2] Banjade T P, Yu S, Ma J 2019 J. Seismol. 5 1
[3] Yang F, Shen X, Wang Z 2018 Entropy 20 8
[4] Lian J J, Zhuo L, Wang H J, Dong X F 2018 Mech. Syst. Sig. Process. 107 53 Google Scholar
[5] Klionskiy D M, Kaplun D I, Geppener V V 2018 Pattern Recognit Image Anal. 28 122 Google Scholar
[6] Chervyakov N, Lyakhov P, Kaplun D, Butusov D, Nagornov N 2018 Electronics 8 135
[7] Qiu X, Ren Y, Suganthan P N, Amaratunga G A J 2017 Appl. Soft Comput. 54 246 Google Scholar
[8] Sweeney K T, Mcloone S F, Ward T E 2013 IEEE Trans. Biomed. Eng. 60 97 Google Scholar
[9] Guo Y, Naik G R, Nguyen H 2017 IEEE Eng. Med. Biol. Soc. 2013 6812
[10] Wang Y, Liu F, Jiang Z S, He S L, Mo Q Y 2017 Mech. Syst. Sig. Process. 86 75 Google Scholar
[11] Xiong T, Bao Y, Zhongyi H U 2014 Neurocomputing 123 174 Google Scholar
[12] Dragomiretskiy K, Zosso D 2014 IEEE Trans. Sig. Process. 62 531
[13] Wang Y X, Markert R, Xiang J W, Zheng W G 2015 Mech. Syst. Sig. Process. 60 243
[14] Yang F R, Bi X, Li C C, Liu C F, Tian T 2019 Measurement 140 1 Google Scholar
[15] 郑小霞, 陈广宁, 任浩翰, 李东东 2019 振动与冲击 38 153
Zheng X X, Chen G N, Ren H H, Li D D 2019 J. Vib. Shock 38 153
[16] 唐贵基, 王晓龙 2015 西安交通大学学报 49 73 Google Scholar
Tang G J, Wang X L 2015 J. Xi'an Jiaotong Univ. 49 73 Google Scholar
[17] 刘备, 胡伟鹏, 邹孝, 丁亚军, 钱盛友 2019 物理学报 68 028702 Google Scholar
Liu B, Hu E P, Zou X, Ding Y J, Qian S Y 2019 Acta Phys. Sin. 68 028702 Google Scholar
[18] Baldini G, Steri G, Dimc F, Giuliani R 2016 Sensors 16 818 Google Scholar
[19] Chen X J, Yang Y M, Cui Z X, Shen J 2019 Energy 174 1110 Google Scholar
[20] Cui J, Yu R Z, Zhao D B, Yang J Y, Ge W C, Zhou X M 2019 Appl. Energy 247 480 Google Scholar
[21] Huang N E, Shen Z, Long S R 1998 Proc. Roy. Soc. A 454 903 Google Scholar
[22] Damerval C, Meignen S, Valerie P 2005 IEEE Signal Process Lett. 12 701 Google Scholar
[23] Cheng J S, Yu D J, Yang Y 2006 Mech. Syst. Sig. Process. 20 817 Google Scholar
[24] Kennedy J, Eberhart R 1995 IEEE Int. Conf. Neural Networks 4 1942
[25] 吕中亮 2016 博士学位论文(重庆: 重庆大学)
Lv Z L 2016 Ph. D. Dissertation (Chongqing: Chongqing University) (in Chinese)
[26] Mcfadden P D, Smith J D 1984 J. Sound Vib. 96 69 Google Scholar
[27] Smith W A, Randall R B 2015 Mech. Syst. Sig. Process. 64–65 100 Google Scholar
[28] Chen F, Shi T, Duan S K, Wang L D, Wu J G 2017 Signal Process. 142 423
[29] Chen F, Li X Y, Duan S K, Wang L D, Wu J G 2018 Digit. Signal Prog. 81 16 Google Scholar
[30] Chen F, Shao X D 2017 Signal Process. 133 213 Google Scholar
[31] Shao X D, Chen F 2019 Signal Process. 160 237 Google Scholar
目录
- 第68卷,第23期 - 2019年12月05日
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