Abstract:In condition monitoring and fault diagnosis of mechanical systems, the preprocessing of measured signal is a necessary link and a basis ensuring subsequent feature extraction and reliability of health assessment. In the article, the results achieved by existing preprocessing methods for measured signals are reviewed. The status of signal conversion methods is described, and the outcomes of signal component extraction methods are introduced. Moreover, the development trend of measured signal preprocessing in mechanical systems is analyzed and prospected. Much work in update of existing traditional time-domain and frequency-domain integration methods and their performance improvement still needs to be exploited in-depth. Intelligent processing methods can obtain complete data source, which reflects the state characteristics of equipment, and are still worthy being investigated further. As the mechanical equipment gradually tends to be intelligent, multi-functional, the efficient and green the preprocessing technology for measured signals in mechanical systems will face new challenges. Advanced signal preprocessing methods should be explored to carry out precise intelligent fault diagnosis and prediction based on information of equipment operating state. Such methods can find extensive applications in scientific research and engineering practice in the future.
汤胜楠,朱勇*,李伟,蔡佳熙. 机械系统实测信号预处理方法研究现状与展望[J]. 排灌机械工程学报, 2019, 37(9): 822-828.
TANG Shengnan, ZHU Yong*, LI Wei, CAI Jiaxi. Status and prospect of research in preprocessing methods for measured signals in mechanical systems. Journal of Drainage and Irrigation Machinery Engin, 2019, 37(9): 822-828.
[1]顾名坤, 吕振华. 基于振动加速度测量的振动速度和位移信号识别方法探讨[J]. 机械科学与技术, 2011, 30(4): 522-526. GU Mingkun, LYU Zhenhua. Identification of a mechanism′s vibration velocity and displacement based on the acceleration measurement [J]. Mechanical science and technology for aerospace engineering, 2011,30(4):522-526.(in Chinese)[2]THONG Y K, WOOLFSON M S, CROWE J A, et al. Numerical double integration of acceleration measure-ments in noise[J]. Measurement, 2004,36(1):73-92.[3]YANG J, LI J B, LIN G. A simple approach to integration of acceleration data for dynamic soil-structure interaction analysis [J]. Soil dynamics & earthquake engineering, 2006,26:725-734.[4]HONG Y H, KIM H K, LEE H S. Reconstruction of dynamic displacement and velocity from measured accele-rations using the variational statement of an inverse problem[J]. Journal of sound and vibration, 2010,329(23):4980-5003.[5]周小祥, 陈尔奎, 吕桂庆, 等. 基于数字积分和LMS的振动加速度信号处理[J]. 自动化仪表, 2006,27(9):51-53. ZHOU Xiaoxiang, CHEN Erkui, LYU Guiqing, et al. The processing of vibration acceleration signal based on numeric integration and LMS[J]. Process automation instrumentation, 2006,27(9):51-53.(in Chinese)[6]张永强, 宋建江, 屠良尧, 等. 软件数值积分误差原因分析及改进办法[J]. 机械强度, 2006,28(3):419-423. ZHANG Yongqiang, SONG Jianjiang, TU Liangyao, et al. Error analysis and improvement method when numerical integration with software[J]. Journal of mechanical strength, 2006,28(3):419-423.(in Chinese)[7]HAN S, CHUNG J W. Retrieving displacement signal from measured acceleration signal[C]//Proceedings of Conference on Structural Dynamics. Los Angeles, USA, 2002,4753(2):1178-1184.[8]HAN S. Retrieving the time history of displacement from measured acceleration signal[J]. KSME international journal, 2003,17(2):197-206.[9]RIBEIRO J G T, DE CASTRO J T P, FREIRE J L F. Using the FFT-DDI method to measure displacements with piezoelectric, resistive and ICP accelerometers[C]//Proceedings of Conference and Exposition on Structural Dynamics, 2003.[10]李强, 王太勇, 胥永刚. 基于频域积分的振动参量转换修正算法[J]. 组合机床与自动化加工技术, 2005(9):60-61,65. LI Qiang, WANG Taiyong, XU Yonggang. The modification of the vibration parameter transform based on frequency domain integration[J]. Modular machine tool & automatic manufacturing technique, 2005(9):60-61,65.(in Chinese)[11]段智育, 贾民平, 许飞云, 等. 振动故障信号的软件积分研究与应用[J]. 机械制造与自动化, 2007, 36(2):76-78,81. DUAN Zhiyu, JIA Minping, XU Feiyun, et al. Resea-rch & application of software integration of vibration fault signal[J]. Machine building & automation, 2007,36(2):76-78,81.(in Chinese)[12]温广瑞, 李杨, 廖与禾, 等. 基于精确信息重构的故障转子系统振动加速度信号积分方法[J]. 机械工程学报, 2013,49(8):1-9. WEN Guangrui, LI Yang, LIAO Yuhe, et al. Faulty rotor system vibration acceleration signal integration method based on precise information reconstruction[J]. Journal of mechanical engineering, 2013,49(8):1-9.(in Chinese)[13]ZHU Yong, JIANG Wanlu, ZHANG Sheng, et al. An accurate calculation method of vibration displacement based on vibration acceleration signal[J]. Journal of information and computational science, 2015,12(1):41-49.[14]ZHU Yong, JIANG Wanlu, KONG Xiangdong, et al. An accurate integral method for vibration signal based on feature information extraction[J]. Shock and vibration, 2015,2015(1):1-13.[15]ZHU Yong, JIANG Wanlu, HU Haosong, et al. A precise frequency-domain integral method of vibration acceleration signal[J]. ICIC express letters, 2015,9(6):1617-1624.[16]朱勇, 姜万录, 郑直, 等. 基于有效信息重构的故障旋转机械振动加速度信号积分方法[J]. 中国机械工程, 2015,26(18):2511-2517. ZHU Yong, JIANG Wanlu, ZHENG Zhi, et al. Useful information reconstruction-based vibration acceleration signal integral method for faulty rotating machinery[J]. China mechanical engineering, 2015,26(18):2511-2517.(in Chinese)[17]梁兵, 汪同庆. 基于HHT的振动信号趋势项提取方法[J]. 电子测量技术, 2013,36(2):119-122. LIANG Bing, WANG Tongqing. Method of vibration signal trend extraction based on HHT[J]. Electronic mea-surement technology, 2013,36(2):119-122.(in Chinese)[18]刘廷宇, 江泉, 李晔, 等. 旋转机械故障的信号处理与诊断方法综述[J]. 能源与环保, 2018,40(1):163-166. LIU Tingyu, JIANG Quan, LI Ye, et al. A review of rotating machinery fault signal processing and diagnosis methods[J]. China energy and environmental pro-tection, 2018,40(1):163-166.(in Chinese)[19]吴志成, 王重阳, 任爱君. 消除信号趋势项时小波基优选方法研究[J]. 北京理工大学学报, 2013,33(8):811-814. WU Zhicheng, WANG Chongyang, REN Aijun. Optimal selection of wavelet base functions for eliminating signal trend based on wavelet analysis[J]. Transactions of Beijing Institute of Technology, 2013,33(8):811-814.(in Chinese)[20]HUANG N E, SHEN Z, LONG S R, et al. The empi-rical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the royal society a: mathematical, physical and engineering sciences, 1998,454(1971):903-995.[21]LEI Yaguo, LIN Jing, HE Zhengjia, et al. A review on empirical mode decomposition in fault diagnosis of rota-ting machinery[J]. Mechanical systems and signal processing, 2013,35(1/2):108-126.[22]张飞, 付婧, 樊玉林, 等. 基于EMD的水电机组甩负荷主轴摆度[J]. 排灌机械工程学报, 2017,35(10):863-868. ZHANG Fei, FU Jing, FAN Yulin, et al. Main shaft run-out research of hydraulic generator unit in load rejection process based on empirical mode decomposition[J]. Journal of drainage and irrigation machinery engineering, 2017,35(10):863-868.(in Chinese)[23]JIA Rong, MA Fuqi, WU Hua, et al. Coupling fault feature extraction method based on bivariate empirical mode decomposition and full spectrum for rotating machinery[J/OL]. Mathematical problems in engineering, 2018,2018:4598706[2018-04-02]. https://doi.org/10.1155/2018/4598706.[24]YUAN Jing, JI Feng, GAO Yuan, et al. Integrated ensemble noise-reconstructed empirical mode decompo-sition for mechanical fault detection[J]. Mechanical systems and signal processing, 2018,104:323-346.[25]王金良, 李宗军. 极点对称模态分解方法——数据分析与科学探索的新途径[M]. 北京: 高等教育出版社, 2015:1-84.[26]SMITH J S. The local mean decomposition and its application to EEG perception data[J]. Journal of the royal society interface, 2005,2(5):443-454.[27]JIANG Wanlu, ZHENG Zhi, ZHU Yong, et al. Demo-dulation for hydraulic pump fault signals based on local mean decomposition and improved adaptive multiscale morphology analysis[J]. Mechanical systems and signal processing, 2015,58/59:179-205.[28]ZHENG Zhi, JIANG Wanlu, WANG Zhenwei, et al. Gear fault diagnosis method based on local mean decomposition and generalized morphological fractal dimensions[J]. Mechanism and machine theory, 2015,91:151-167.[29]LI Yongbo, XU Minqiang, WANG Rixin, et al. A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy[J]. Journal of sound and vibration, 2016,360:277-299.[30]LIU W Y, ZHANG W H, HAN J G, et al. A new wind turbine fault diagnosis method based on the local mean decomposition[J]. Renewable energy, 2012,48(6):411-415.[31]YANG Yu, CHENG Junsheng, ZHANG Kang. An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems[J]. Measurement, 2012,45(3):561-570. [32]WU Zhaohua, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in adaptive data analysis, 2009,1(1):1-41.[33]CHEN Dongyue, LIN Jianhui, LI Yanping. Modified complementary ensemble empirical mode decomposition and intrinsic mode functions evaluation index for high-speed train gearbox fault diagnosis[J]. Journal of sound and vibration, 2018,424:192-207.[34]王金良, 李宗军. 可用于气候数据分析的ESMD方法[J]. 气候变化研究快报, 2014,3(1):1-5. WANG Jinliang, LI Zongjun. The ESMD method for climate data analysis[J]. Climate change research letters, 2014,3(1):1-5.(in Chinese)[35]WANG Y H, YEH C H, YOUNG H W V, et al. On the computational complexity of the empirical mode decomposition algorithm[J]. Physica A: statistical mechanics and its applications, 2014,400(2):159-167.[36]DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE transactions on signal processing, 2014,62(3):531-544.[37]WANG Jinliang, LI Zongjun. Extreme-point symmetric mode decomposition method for data analysis[J]. Advances in adaptive data analysis, 2013,5(3):1350015.[38]LI Huifeng, WANG Jinliang, LI Zongjun. Application of ESMD method to air-sea flux investigation[J]. International journal of geosciences, 2013,4(5):8-11.[39]YANG Wenan, ZHOU Wei, LIAO Wenhe, et al. Identification and quantification of concurrent control chart patterns using extreme-point symmetric mode decomposition and extreme learning machines[J]. Neurocompu-ting, 2015,147(1):260-270.[40]丁佐榕, 李健, 王胜南, 等. 基于ESMD的浓相气力输送颗粒静电信号分析及速度测量[J]. 东南大学学报(自然科学版), 2016,46(5):1032-1037. DING Zuorong, LI Jian, WANG Shengnan, et al. Analysis of particle electrostatic signals and velocity measurement in dense pneumatic conveying system based on ESMD[J]. Journal of Southeast University(natural science edition), 2016,46(5):1032-1037.(in Chinese)[41]TIAN Xiaoying, LI Yongshuai, ZHOU Huan, et al. Electrocardiogram signal denoising using extreme-point symmetric mode decomposition and nonlocal means[J]. Sensors, 2016,16(10):1584.[42]叶卫东, 杨涛. 融合极点对称模态分解与时频分析的单通道振动信号盲分离方法[J]. 计算机应用, 2016,36(10):2933-2939. YE Weidong, YANG Tao. Single-channel vibration signal blind source separation by combining extreme-point symmetric mode decomposition with time-fre-quency analysis[J]. Journal of computer applications, 2016,36(10):2933-2939.(in Chinese)[43]LIN Qingxia, WU Zhiyong, SINGH V P, et al. Correlation between hydrological drought, climatic factors, re-servoir operation, and vegetation cover in the Xijiang Basin, South China[J]. Journal of hydrology, 2017,549:512-524.[44]LIU Xianglei, TANG Yi, LU Zhao, et al. ESMD-based stability analysis in the progressive collapse of a building model: a case study of a reinforced concrete frame-shear wall model [J]. Measurement, 2018,120:34-42.[45]ZHU Yong, JIANG Wanlu, KONG Xiangdong. Adaptive extraction method for trend term of machinery signal based on extreme-point symmetric mode decomposition [J]. Journal of mechanical science and technology, 2017,31(2):493-500.[46]张淑清, 徐剑涛, 姜安琦, 等. 基于极点对称模态分解和概率神经网络的轴承故障诊断[J]. 中国机械工程, 2017,28(4):425-431. ZHANG Shuqing, XU Jiantao, JIANG Anqi, et al. Fault diagnosis of bearings based on extreme-point symmetric mode decomposition and probabilistic neural network[J]. China mechanical engineering, 2017,28(4):425-431.(in Chinese)[47]MATHERON G, SERRA J. The birth of mathematical morphology[C]//Proceedings of International Sympo-sium on Mathematical Morphology. [S.l.]: CSIRO Publishing, 2002.[48]LI Huijian, WANG Runqiu, CAO Siyuan, et al. Weak signal detection using multiscale morphology in microseismic monitoring[J]. Journal of applied geophysics, 2016,133:39-49.[49]胡智勇, 胡杰鑫, 谢里阳,等. 滚动轴承振动信号处理方法综述[J]. 中国工程机械学报, 2016,14(6):525-531. HU Zhiyong, HU Jiexin, XIE Liyang, et al. Review on signal processing for rolling bearing vibrations[J]. Chinese journal of construction machinery, 2016,14(6):525-531.(in Chinese)