Adaptive direct fast iterative filtering based rolling bearing fault diagnosis

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (14) : 20-29.

DING Wenhai1,2，ZHENG Jinde1,2，PAN Haiyang1,2，MENG Rui1,2，NIU Limin1,2

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Abstract

Direct fast iterative filtering (DFIF) is a recently proposed nonlinear and non-stationary signal analysis method. Aiming at the problems that the DFIF method needs to preset the adjustment parameter of filtering interval, which lacks adaptability in the process of iterative calculation. In this paper, adaptive direct fast iterative filtering method is proposed based on the instantaneous frequency fluctuation energy difference criterion, which can adaptively determine the optimal filter interval adjustment parameters in the iterative screening process of each layer in the outer loop of DFIF algorithm. The ADFIF method can adaptively decompose a given nonlinear and non-stationary signal into the sum of several approximately narrow band signals whose instantaneous frequency has physical significance and a trend term. Through the simulated and measured signal analysis of rolling bearings, the proposed ADFIF method is compared with original DFIF, the adaptive local iterative filtering, variational mode decomposition, and empirical mode decomposition and the results show that the proposed ADFF method has certain advantages in suppressing mode mixing and noise resistance, and can extract more fault characteristic information of rolling bearing.

Key words

fast iterative filtering / adaptive local iterative filtering / rolling bearing / fault diagnosis

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DING Wenhai1,2，ZHENG Jinde1,2，PAN Haiyang1,2，MENG Rui1,2，NIU Limin1,2. Adaptive direct fast iterative filtering based rolling bearing fault diagnosis[J]. Journal of Vibration and Shock, 2023, 42(14): 20-29

References

[1] ZHENG J D, CAO S J, PAN H Y, et al. Spectral envelope-based adaptive empirical Fourier decomposition method and its application to rolling bearing fault diagnosis[J]. ISA transactions, 2022.
[2] YING W M, ZHENG J D, PAN H Y, et al. Permutation entropy-based improved uniform phase empirical mode decomposition for mechanical fault diagnosis[J]. Digital Signal Processing, 2021, 117.
[3] 孙岩. 非平稳状态滚动轴承故障诊断方法研究[D]. 哈尔滨工业大学,2020.
SUN Yan. Research on fault diagnosis method of non-stationary rolling bearing[D], Harbin Institute of Technology, 2020.
[4] 张贤达. 现代信号处理[M]. 北京：清华大学出版社，1995.
ZHANG Xianda. Modern signal processing[M], Beijing: Tsinghua University Press, 1995.
[5] RIOUL O., VETTERLI M.. Wavelets and signal processing[J]. IEEE Signal Processing Magazine, 1991 ,8(4).
[6] 李恒，张氢，秦仙蓉. 基于短时傅里叶变换和卷积神经网络的轴承故障诊断方法[J]. 振动与冲击，2018,37(19)：124-131.
LI Huan, ZHANG Qing, QIN Xianrong. Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neural network[J]. Journal of Vibration and Shock, 2018, 37(19)：124-131.
[7] 温竹鹏，陈捷，刘连华. 基于小波变换和优化CNN的风电齿轮箱故障诊断[J]. 浙江大学学报(工学版），2022,56(06)：1212-1219.
WEN Zhupeng, CHEN Jie, LIU Lianhua. Fault diagnosis of wind power gearbox based on wavelet transform and improved CNN[J], Journal of Zhejiang University (Engineering Science), 2022, 56(06)：1212-1219.
[8] 林京，屈梁生. 基于连续小波变换的信号检测技术与故障诊断[J]. 机械工程学报，2000(12)：95-100.
LIN Jing, QU Liangsheng. Feature detection and fault diagnosis based on continuous wavelet transform[J]. Journal of Mechanical Engineering, 2000,36(12)：95-100.
[9] 何正嘉，陈进，王太勇等. 机械故障诊断理论及应用[M]. 北京：高等教育出版社，2010.
HE Zhengjia, CHEN Jin, WANG Taiyong, et al. Theories and applications of machinery fault diagnostics[M]. Beijing: Higher Education Press, 2010.
[10] HUANG N E, SHEN Z, LONG S R, et al. The Empirical 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).
[11] Dragomiretskiy K, ZOSSO D. Variational Mode Decomposition.[J]. IEEE Transactions on Signal Processing: A publication of the IEEE Signal Processing Society, 2014, 62(3).
[12] LIN L, WANG Y, ZHOU H M. Iterative filtering as an alternative algorithm for empirical mode decomposition.[J]. Advances in Adaptive Data Analysis, 2009, 1(4)
[13] PIERSANTI M, MATERASSI M, Cicone A, et al.. Adaptive Local Iterative Filtering: A Promising Technique for the Analysis of Nonstationary Signals[J]. Journal of Geophysical Research: Space Physics, 2018, 123(1).
[14] 陈保家，汪新波，赵春华等. 基于自适应局部迭代滤波和能量算子解调的滚动轴承故障特征提取[J]. 南京理工大学学报，2018. 42(04)：445-452.
CHEN Baojia, WANG Xinbo, ZHAO Chunhua, et al. Fault feature extraction of rolling bearing based on ALIF and energy operator demodulation[J]. Journal of Nanjing University of Science and Technology, 2018,42(04)：445-452.
[15] Cicone A, Zhou H M. Numerical analysis for iterative filtering with new efficient implementations based on FFT[J]. Numerische Mathematik, 2021.
[16] XU Y B, FAN F, JIANG X K. A fast iterative filtering decomposition and symmetric difference analytic energy operator for bearing fault extraction[J]. ISA Transactions, 2020.
[17] Cicone A. Iterative filtering as a direct method for the decomposition of nonstationary signals[J]. Numerical Algorithms, 2020.
[18] 郑近德,程军圣.改进的希尔伯特-黄变换及其在滚动轴承故障诊断中的应用[J]. 机械工程学报,2015,51(01)：138-145.
ZHENG Jinde, CHENG Junsheng. Improved Hilbert-Huang Transform and its applications to rolling bearing fault diagnosis[J]. Journal of Mechanical Engineering, 2015, 51(01)：138-145.
[19] 郑近德，程军圣，曾鸣等. 广义经验模态分解性能分析与应用[J]. 振动与冲击，2015,34(03):123-128+155.
ZHENG Jinde, CHENG Junsheng, ZENG Ming, et al.Performance analysis and application of generalized empirical mode decomposition[J]. Journal of Vibration and Shock, 2015, 34(03); 123-128+155.
[20] XUAN B, XIE Q W,PENG S L. EMD Sifting Based on Bandwidth[J]. IEEE Signal Processing Letters, 2001, 14(8).
[21] CHEN B J, CHEN X L, CHEN F F, et al. Integrated early fault diagnosis method based on direct fast iterative filtering decomposition and effective weighted sparseness kurtosis to rolling bearings[J]. Mechanical Systems and Signal Processing, 2022, 171.
[22] WANG X L, ZHENG J D, ZHANG J. A novel optimal demodulation frequency band extraction method of fault bearing based on power spectrum screening combination-gram[J]. Mechanical Systems and Signal Processing, 2022, 174.
[23] LU S L, HE Q B, WANG J. A review of stochastic resonance in rotating machine fault detection[J]. Mechanical Systems and Signal Processing, 2019, 116.