Reduced Mode Decomposition: A New Signal Decomposition Method

被引:17
作者
Cheng, Jian [1 ]
Pan, Haiyang [1 ]
Tong, Jinyu [1 ]
Zheng, Jinde [1 ]
机构
[1] Anhui Univ Technol, Sch Mech Engn, Ma'anshan 243002, Peoples R China
关键词
Fault diagnosis; periodic pulse; reduced mode decomposition (RMD); redundant information; reweighted kurtosis; FAULT-DIAGNOSIS; BEARING; FUSION;
D O I
10.1109/TIM.2024.3378258
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
Although traditional signal processing methods have good decomposition performance in multimodal signals, they lack theoretical research on periodic pulse signals, resulting in insufficient decomposition. Based on this, a reduced mode decomposition (RMD) method is proposed in this article, which can decompose reduced components (RCs) iteratively through the designed finite impulse response (FIR) filter bank. On the one hand, an adaptive index called reweighted kurtosis (RK) is defined as the objective function of filter banks, so as to fully consider the impulsivity and periodicity of signals and make the filter components contain rich state information. On the other hand, FIR is used to constrain various modal signals, so that RMD is not limited by filter bandwidth and center frequency, improves the decomposition ability and ensures the robustness of noise. The verification results of two types of roller bearing fault signals indicate that RMD is a novel multimode signal analysis method.
引用
收藏
页码:1 / 9
页数:9
相关论文
共 30 条
[1]   Ramanujan Fourier Mode Decomposition and Its Application in Gear Fault Diagnosis [J].
Cheng, Jian ;
Yang, Yu ;
Wu, Zhantao ;
Shao, Haidong ;
Pan, Haiyang ;
Cheng, Junsheng .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2022, 18 (09) :6079-6088
[2]   Symplectic geometry packet decomposition and its applications to gear fault diagnosis [J].
Cheng, Jian ;
Yang, Yu ;
Li, Xin ;
Cheng, Junsheng .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2022, 174
[3]   Enhanced periodic mode decomposition and its application to composite fault diagnosis of rolling bearings [J].
Cheng, Jian ;
Yang, Yu ;
Shao, Haidong ;
Pan, Haiyang ;
Zheng, Jinde ;
Cheng, Junsheng .
ISA TRANSACTIONS, 2022, 125 :474-491
[4]   Three-dimensional instantaneous orbit map for rotor-bearing system based on a novel multivariate complex variational mode decomposition algorithm [J].
Cui, Xiaolong ;
Huang, Jie ;
Li, Chaoshun ;
Zhao, Yujie .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2022, 178
[5]   Empirical Wavelet Transform [J].
Gilles, Jerome .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2013, 61 (16) :3999-4010
[6]   Generalized Variational Mode Decomposition: A Multiscale and Fixed-Frequency Decomposition Algorithm [J].
Guo, Yanfei ;
Zhang, Zhousuo .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 70
[7]   Improving the predictive response using ensemble empirical mode decomposition based soft sensors with auto encoder deep neural network [J].
Haleem, Sulaima Lebbe Abdul ;
Sodagudi, Suhasini ;
Althubiti, Sara A. ;
Shukla, Surendra Kumar ;
Ahmed, Mohammed Altaf ;
Chokkalingam, Bharatiraja .
MEASUREMENT, 2022, 199
[8]   Review of Automatic Fault Diagnosis Systems Using Audio and Vibration Signals [J].
Henriquez, Patricia ;
Alonso, Jesus B. ;
Ferrer, Miguel A. ;
Travieso, Carlos M. .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2014, 44 (05) :642-652
[9]   A review on empirical mode decomposition in fault diagnosis of rotating machinery [J].
Lei, Yaguo ;
Lin, Jing ;
He, Zhengjia ;
Zuo, Ming J. .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2013, 35 (1-2) :108-126
[10]   Periodic impulses extraction based on improved adaptive VMD and sparse code shrinkage denoising and its application in rotating machinery fault diagnosis [J].
Li, Jimeng ;
Yao, Xifeng ;
Wang, Hui ;
Zhang, Jinfeng .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2019, 126 :568-589