TY - GEN
T1 - An Investigation into Noise Source Separation and Blind Identification Method for Electric Drive System Based on Single-Channel Noise Sources
AU - Qiu, Zizhen
AU - Zhang, Wei
AU - Kong, Zhiguo
AU - Huang, Xin
AU - Wang, Fang
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - During the analysis of noise source characteristics in electric vehicles, signal processing methods are required for the noise excitation source of the electric drive system (EDS) to obtain its time–frequency domain signal features. This paper proposes a single-channel noise source separation and identification method. Firstly, the single-channel noise source separation and blind identification methods, the complete ensemble EMD with adaptive noise (CEEMDAN) and the improved CEEMDAN (ICEEMDAN), have been established based on the empirical mode decomposition (EMD) algorithm. Comparative simulation and analysis are conducted by using similarity coefficients and residual errors as parameters, also with independent component analysis (ICA) method. Finally, acoustic noise data collection and processing for the EDS under multiple operating conditions are performed, in which the obtained data from steady-state conditions are analyzed using the proposed ICEEMDDAN methods both with ICA. The results show that multiple independent noise signals can be effectively obtained after using the proposed methods. Furthermore, by evaluating the sound pressure level of each independent sound source in the time–frequency domain, the significant contributions of secondary meshing noise and switching frequency noise in the analyzed operating conditions are determined.
AB - During the analysis of noise source characteristics in electric vehicles, signal processing methods are required for the noise excitation source of the electric drive system (EDS) to obtain its time–frequency domain signal features. This paper proposes a single-channel noise source separation and identification method. Firstly, the single-channel noise source separation and blind identification methods, the complete ensemble EMD with adaptive noise (CEEMDAN) and the improved CEEMDAN (ICEEMDAN), have been established based on the empirical mode decomposition (EMD) algorithm. Comparative simulation and analysis are conducted by using similarity coefficients and residual errors as parameters, also with independent component analysis (ICA) method. Finally, acoustic noise data collection and processing for the EDS under multiple operating conditions are performed, in which the obtained data from steady-state conditions are analyzed using the proposed ICEEMDDAN methods both with ICA. The results show that multiple independent noise signals can be effectively obtained after using the proposed methods. Furthermore, by evaluating the sound pressure level of each independent sound source in the time–frequency domain, the significant contributions of secondary meshing noise and switching frequency noise in the analyzed operating conditions are determined.
KW - Electric drive system
KW - Empirical mode decomposition
KW - Independent component analysis
KW - Noise source separation and blind identification
UR - http://www.scopus.com/inward/record.url?scp=85195598951&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-49421-5_3
DO - 10.1007/978-3-031-49421-5_3
M3 - Conference contribution
AN - SCOPUS:85195598951
SN - 9783031494208
T3 - Mechanisms and Machine Science
SP - 21
EP - 39
BT - Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2
A2 - Ball, Andrew D.
A2 - Wang, Zuolu
A2 - Ouyang, Huajiang
A2 - Sinha, Jyoti K.
PB - Springer Science and Business Media B.V.
T2 - UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023
Y2 - 29 August 2023 through 1 September 2023
ER -