-
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
-
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 — — PHP网站源码吉安网站设计模板价格衢州网页设计多少钱西安企业网站制作推荐大连网站制作设计多少钱平湖网站优化按天计费报价龙岗建设网站多少钱蚌埠网站改版价格宁德关键词按天收费吴忠seo优化价格宣城网站制作价格眉山营销型网站建设公司南联网络推广公司临猗建网站哪家好萍乡英文网站建设推荐重庆网页制作哪家好莱芜模板制作价格坂田SEO按效果付费推荐咸宁外贸网站制作报价长治高端网站设计公司随州百搜标王公司赤峰模板制作济宁网站建设多少钱遵义百姓网标王多少钱廊坊网站定制报价河源SEO按天收费多少钱哈尔滨设计网站公司宜春模板网站建设价格那曲外贸网站设计公司贵港网站定制推荐鹤岗网站优化排名价格歼20紧急升空逼退外机英媒称团队夜以继日筹划王妃复出草木蔓发 春山在望成都发生巨响 当地回应60岁老人炒菠菜未焯水致肾病恶化男子涉嫌走私被判11年却一天牢没坐劳斯莱斯右转逼停直行车网传落水者说“没让你救”系谣言广东通报13岁男孩性侵女童不予立案贵州小伙回应在美国卖三蹦子火了淀粉肠小王子日销售额涨超10倍有个姐真把千机伞做出来了近3万元金手镯仅含足金十克呼北高速交通事故已致14人死亡杨洋拄拐现身医院国产伟哥去年销售近13亿男子给前妻转账 现任妻子起诉要回新基金只募集到26元还是员工自购男孩疑遭霸凌 家长讨说法被踢出群充个话费竟沦为间接洗钱工具新的一天从800个哈欠开始单亲妈妈陷入热恋 14岁儿子报警#春分立蛋大挑战#中国投资客涌入日本东京买房两大学生合买彩票中奖一人不认账新加坡主帅:唯一目标击败中国队月嫂回应掌掴婴儿是在赶虫子19岁小伙救下5人后溺亡 多方发声清明节放假3天调休1天张家界的山上“长”满了韩国人?开封王婆为何火了主播靠辱骂母亲走红被批捕封号代拍被何赛飞拿着魔杖追着打阿根廷将发行1万与2万面值的纸币库克现身上海为江西彩礼“减负”的“试婚人”因自嘲式简历走红的教授更新简介殡仪馆花卉高于市场价3倍还重复用网友称在豆瓣酱里吃出老鼠头315晚会后胖东来又人满为患了网友建议重庆地铁不准乘客携带菜筐特朗普谈“凯特王妃P图照”罗斯否认插足凯特王妃婚姻青海通报栏杆断裂小学生跌落住进ICU恒大被罚41.75亿到底怎么缴湖南一县政协主席疑涉刑案被控制茶百道就改标签日期致歉王树国3次鞠躬告别西交大师生张立群任西安交通大学校长杨倩无缘巴黎奥运
-
[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
计量
- 文章访问数: 11086
- PDF下载量: 256
- 被引次数: 0