Dr. Feng Chen Profile

Dr. Feng Chen

SPIE Involvement:

Author

Publications (2)

Proceedings Article | 15 March 2024 Paper

Research on background noise separation of speed reducer based on WOA optimized VMD

Weilin Li, Yangming Jiang, Ting Lin, Xu Cheng, Tianshu Zhao, Ting Liu, Zhongjie Zhang, Feng Chen

Proceedings Volume 13079, 1307916 (2024) https://doi.org/10.1117/12.3015524

Read Abstract +

The NVH (Noise, Vibration, Harshness) of electric vehicles greatly affects driving comfort, and the NVH has become a key indicator for verifying the quality of their products, strengthening the research on noise testing technology can greatly promote the high-quality development of reducers. Given the problem that the noise data acquisition environment of the noise test stand is highly disturbed and difficult to eliminate, this paper proposes a whale optimization algorithm (WOA) with envelope entropy as the fitness function to carry out the improved variational modal decomposition (VMD) to decompose the noise signal of the speed reducer to obtain the IMF component, and to use the signal inter-correlation analysis of the IMF background noise to identify and filter out the noise reduction, and to complete the separation of the noise from the background noise of the speed reducer. The method is based on the iterative optimization of the number of decomposition layers and penalty factor of the VMD by WOA to find out the optimal combination of decomposition parameters and identify and reduce the background noise components based on the signal correlation analysis. Firstly, it is verified that the method has more effective decomposition performance than the traditional VMD algorithm by simulation signal test. Then the method is used for the background noise separation of electric vehicle gearbox, and the results show that the WOA-VMD proposed in this paper correctly separates the background noise signals in the mixed signals, and can effectively carry out the noise separation, and the decomposition filters out the background noise with an average reduction of the sound pressure level of 2.99 dB(A), and the average error of the sound pressure level with the baseline condition is 0.45 dB(A). Also, the noise reduction method using WOA-VMD reduces the sound pressure level by an average of 1.47 dB(A) than that obtained by the VMD algorithm, which has a better noise separation effect. The above conclusion proves that the present method improves the accuracy of noise testing of electric vehicle reducers.

Proceedings Article | 2 November 2022 Paper

Research on the prediction of bearing life based on EMD and PF

Weilin Li, Feng Chen, Hangchao Zhou, Yinda He

Proceedings Volume 12351, 123511O (2022) https://doi.org/10.1117/12.2652707

KEYWORDS: Data modeling, Particles, Particle filters, Failure analysis, Statistical modeling, Systems modeling, Interference (communication), Instrument modeling, Complex systems, Statistical methods

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years