TY - GEN
T1 - An Improved Sparse Mode Decomposition Method for Pulse Signals
AU - Wu, Jialian
AU - Li, Yueyang
AU - Zhao, Dong
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In signal processing field, mode decomposition is one of the most important branches. Many existing decomposition methods are mainly used to deal with narrow-band signals. If the analyzed signal is composed of bandwidth components, such as periodic pulse signals, traditional mode decomposition algorithms may have a performance deterioration. In order to overcome this problem, this paper proposes an improved sparse decomposition based on the group-sparse mode decomposition (GSMD) algorithm. The idea of the algorithm can be divided into two parts. First, the least square curve fitting technique is used to replace the average energy in GSMD algorithm with the fitted signal energy curve. Second, the bandwidth of each mode is adaptively reconstructed by the 3dB bandwidth criterion. The feasibility and superiority of the proposed method are verified by processing a set of actual bearing fault signal data and comparing with some existing methods.
AB - In signal processing field, mode decomposition is one of the most important branches. Many existing decomposition methods are mainly used to deal with narrow-band signals. If the analyzed signal is composed of bandwidth components, such as periodic pulse signals, traditional mode decomposition algorithms may have a performance deterioration. In order to overcome this problem, this paper proposes an improved sparse decomposition based on the group-sparse mode decomposition (GSMD) algorithm. The idea of the algorithm can be divided into two parts. First, the least square curve fitting technique is used to replace the average energy in GSMD algorithm with the fitted signal energy curve. Second, the bandwidth of each mode is adaptively reconstructed by the 3dB bandwidth criterion. The feasibility and superiority of the proposed method are verified by processing a set of actual bearing fault signal data and comparing with some existing methods.
KW - Curve Fitting
KW - Periodic Pulses Signal
KW - Signal Decomposition
KW - Weighted Norm and Penalized Least Squares
UR - https://www.scopus.com/pages/publications/85166029291
U2 - 10.1109/DDCLS58216.2023.10166281
DO - 10.1109/DDCLS58216.2023.10166281
M3 - 会议稿件
AN - SCOPUS:85166029291
T3 - Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023
SP - 362
EP - 367
BT - Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023
Y2 - 12 May 2023 through 14 May 2023
ER -