Second-Order Synchroextracting Transform With Application to Fault Diagnosis

被引：51

作者：

Bao, Wenjie ^{[1
]}

Li, Fucai ^{[2
]}

Tu, Xiaotong ^{[1
]}

Hu, Yue ^{[2
]}

He, Zhoujie ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Shanghai, Peoples R China

[2] Shanghai Jiao Tong Univ, Shanghai, Peoples R China

来源：

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | 2021年 / 70卷

关键词：

Transforms; Time-frequency analysis; Fault diagnosis; Vibrations; Fourier transforms; Analytical models; Tools; instantaneous frequency (IF); nonstationary signal; synchroextracting transform; synchrosqueezing transform (SST); time-frequency (TF) analysis;

D O I：

10.1109/TIM.2020.3045841

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Synchrosqueezing transform (SST) is a currently proposed novel postprocessing time-frequency (TF) analysis tool. It has been widely shown that SST is able to improve TF representation. However, so far, how to improve the TF resolution while ensuring the accuracy of signal reconstruction is still an open question, particularly for the vibration signal with time-varying instantaneous frequency (IF) characteristics, due to the fact that the vibration signals of mechanical equipment usually contain many types of noise generated by harsh operating conditions, and the SST will mix these noise into the real signal. Our first contribution is using the Gaussian modulated linear chirp (GMLC) signal model to represent the general nonstationary signals. The GMLC signal model can more accurately represent the time-varying nonstationary signal, compared with the SST signal model composed of linear phase function and constant amplitude. Our second contribution in this work is proposing a method to improve the TF resolution and reconstruction accuracy for nonstationary signals with time-varying IF, which we coined the second-order synchroextracting transform (SET2). In SET2, we apply the GMLC to deduce the nonstationary signal model and then only use the energy at the IF to characterize the TF distribution, which improves the TF while reducing the impact of noise on the real signal.

引用

页码：12 / 12

页数：1

共 47 条

[1]

Lataire J., Louarroudi E., Pintelon R., Detecting a time-varying behavior in frequency response function measurements, IEEE Trans. Instrum. Meas., 61, 8, pp. 2132-2143, (2012)

[2]

Zhang S., Et al., An adaptive CEEMDAN thresholding denoising method optimized by nonlocal means algorithm, IEEE Trans. Instrum. Meas., 69, 9, pp. 6891-6903

[3]

Kuo C., Lin W.S., Dressel C.A., Chiu A.W.L., Ensemble empirical mode decomposition, Source, 55, 4, pp. 6281-6284, (2011)

[4]

Yan R., Gao R.X., Hilbert–Huang transform-based vibration signal analysis for machine health monitoring, IEEE Trans. Instrum. Meas., 55, 6, pp. 2320-2329, (2006)

[5]

An X., Jiang D., Li S., Zhao M., Application of the ensemble empirical mode decomposition and Hilbert transform to pedestal looseness study of direct-drive wind turbine, Energy, 36, 9, pp. 5508-5520, (2011)

[6]

Ai S., Li H., Zhang Y., Condition monitoring for bearing using envelope spectrum of EEMD, Proc. Int. Conf. Measuring Technol. Mechtron. Autom., pp. 190-193, (2009)

[7]

Guo W., Tse P.W., Enhancing the ability of ensemble empirical mode decomposition in machine fault diagnosis, Proc. Prognostics Syst. Health Manage. Conf., pp. 1-7, (2010)

[8]

Lu X., Wang J., Bearing fault diagnosis based on redundant second generation wavelet denoising and EEMD, Proc. Int. Conf. Con-Sum. Electron., Commun. Netw. (CECNet), pp. 1090-1093, (2011)

[9]

Ai S., Li H., Gear fault detection based on ensemble empirical mode decomposition and Hilbert-Huang transform, Proc. 5th Int. Conf. Fuzzy Syst. Knowl. Discovery, pp. 173-177, (2008)

[10]

Zhou Y., Tao T., Mei X., Jiang G., Sun N., Feed-axis gearbox condition monitoring using built-in position sensors and EEMD method, Robot. Comput. Integr. Manuf., 27, 4, pp. 785-793, (2011)

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