Academic Journal of Engineering and Technology Science, 2026, 9(1); doi: 10.25236/AJETS.2026.090101.
Menghuan Kuang1, Lifeng Chen1, Zhao Xiao1, Muyao Chen2, Yanbo Zhao3
1School of Mechanical and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China
2School of Mechanical and Electrical Engineering, China University of Mining and Technology, Beijing, China
3School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
Strong noise interference during acoustic signal acquisition and modal aliasing during signal processing complicate feature extraction. This paper proposes a novel acoustic signal denoising method that integrates optimized Variational Mode Decomposition (VMD) with wavelet fuzzy entropy. First, the Whale Optimization Algorithm (WOA) is employed to optimize the VMD's mode component K and penalty factor α, thereby suppressing mode aliasing. Subsequently, the minimum envelope entropy is used as the fitness function to select the optimal Intrinsic Mode Function (IMF) component. Finally, fuzzy entropy values are computed for each IMF to further filter noise components. For IMF components dominated by noise, wavelet transform denoising is applied. These denoised components are then reconstructed with the initially filtered useful signal IMF components to obtain the denoised signal. Through noise reduction analysis of acoustic signals collected via a self-built test bench, this method was validated as superior to fixed-parameter VMD decomposition and wavelet threshold denoising algorithms. It effectively removes noise, providing new insights for planetary gear acoustic signal denoising.
acoustic signal, WOA-VMD, fuzzy entropy, planetary gear, wavelet threshold denoising
Menghuan Kuang, Lifeng Chen, Zhao Xiao, Muyao Chen, Yanbo Zhao. A Wavelet-Based Noise Reduction Method for Acoustic Signals Combining WOA-VMD Enhancement and Fuzzy Entropy Thresholding. Academic Journal of Engineering and Technology Science (2026), Vol. 9, Issue 1: 1-12. https://doi.org/10.25236/AJETS.2026.090101.
[1] Wan A ,Zhu Z ,Bukhaiti A K , et al.Fault diagnosis of helicopter accessory gearbox under multiple operating conditions based on feature mode decomposition and multi-scale convolutional neural networks[J].Applied Soft Computing,2025,113403-113403.
[2] Zhang J ,Zhang S ,Dong Y , et al.Enhanced gear fault diagnosis via heterodyne downconversion: Theoretical verification and optimized ultrasonic signal acquisition[J]. Measurement, 2025, 256(PB):118215-118215.
[3] Wang Lijin. Research on Fault Diagnosis of Transmission Gearboxes Based on Acoustic Signals [D]. Shijiazhuang University of Railways, 2022. DOI: 10.27334/d.cnki.gstdy.2022.000711.
[4] Wu H ,Zhi S ,Fang Q , et al.Adaptive matching squeezing chirplet transform for fault diagnosis of wind turbine planetary gearbox under non-stationary conditions.[J].ISA transactions,2025,
[5] Meng G ,An Y ,Zhang D , et al.Fault diagnosis of gearboxin wind turbine based on EMD-DCGAN[J].EAI Endorsed Transactions on Energy Web,2024,11(1).
[6] Cao J ,Zhang X ,Wang H , et al.A novel approach of fault diagnosis for gearbox based on VMD optimized by SSA and improved RCMDE[J].Journal of Vibration and Control,2025,31(15-16):3282-3294.DOI:10.1177/10775463241272983.
[7] Sun Kang, Jin Jiangtao, Li Chun, et al. Fault Diagnosis of Wind Turbine Gearboxes Based on Improved Empirical Wavelet Transform and Fractal Feature Set [J]. Journal of Solar Energy, 2023, 44(05): 310-319.
[8] Xie Fengyun, Wang Gan, Shang Jiandong, et al. Gearbox Fault Diagnosis Based on Adaptive Variational Modal Decomposition [J]. Propulsion Technology, 2024, 45(09): 223-232.
[9] Guo Qinghui, Li Yuan, Xing Zuoxia. Signal Denoising Method Based on Optimized Variational Modal Decomposition Combined with Wavelet Thresholding [J]. Systems Science and Mathematics, 2025, 45(06): 1687-1700.
[10] Zhang Weiping, Fu Min, Zhang Haiyan, et al. Application of an Improved WOA-VMD Algorithm in Underwater Acoustic Signal Denoising [J]. Journal of Ocean University of China (Natural Science Edition), 2023, 53(01): 138-146.
[11] He Chengbing, Che Qixiang, Xu Zhenhua, et al. Signal Denoising Method Based on Parameter Self-Optimizing Variational Modal Decomposition [J]. Vibration and Shock, 2023, 42(19): 283-293.
[12] Tang Jun, Lei Wensheng, Lin Ling, et al. An Optimized VMD-IWTD Algorithm for Underwater Robot Acoustic Signal Denoising [J/OL]. Machinery Science and Technology, 1-9 [2025-12-01].
[13] Zheng Yang, Zhang Yi, Deng Ruiji, et al. Bridge Monitoring Signal Denoising Method Combining CPO-VMD with Improved Wavelet Threshold [J/OL]. Vibration and Shock, 1-11 [2025-12-02].
[14] Zhang Liyao, Lu Kaiting, Jiang Haiyan, et al. Research on WOA-VMD-Based Signal Decomposition and Reconstruction Method for Rotor-Bearing Systems [J]. Thermal Power Engineering, 2025, 40(02): 158-166.
[15] Zhu Yuyao, Li Shuangjiang, Xin Jingzhou, et al. Noise Reduction Method for Bridge Monitoring Data Based on GWO-VMD and Correlation Coefficient Threshold [J]. Noise and Vibration Control, 2025, 45(04): 116-122+196.
[16] Guo Qinghui, Li Yuan, Xing Zuoxia. Signal Denoising Method Based on Optimized Variational Modal Decomposition Combined with Wavelet Thresholding [J]. Systems Science and Mathematics, 2025, 45(06): 1687-1700.
[17] Liu Kui, Zhang Dongmei, Yu Guang, et al. Experimental Study on Wavelet Threshold Filtering for Air-Coupled Ultrasonic Signals [J]. Transactions of the Chinese Society for Mechanical Engineering, 2015, 51(20): 61-66.