scholarly journals Brake Noise Reduction Method Based on Monte Carlo Sampling and Particle Swarm Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yihong Gu ◽  
Yucheng Liu ◽  
Congda Lu
Keyword(s):  
Monte Carlo ◽  
Particle Swarm Optimization ◽  
Noise Reduction ◽  
Reduction Method ◽  
Particle Swarm ◽  
Monte Carlo Sampling ◽  
Swarm Optimization ◽  
Brake Lining ◽  
Brake Noise ◽  
Dynamometer Test

Brake noise is one of the principal components of vehicle noise and is also one of the most critical measures of vehicle quality. During the braking process, the occurrence of brake noise has a significant relationship with the working conditions of the brake system. In the present study, dynamometer test data and the finite element method (FEM) were used to analyze the direct and indirect effects of variations in the working parameters on the brake noise, and a brake noise reduction method was developed. With this method, Monte Carlo sampling was used to consider variations in the parameters of the brake lining during the braking procedure, and the particle swarm optimization method was used to calculate the optimal parameter combination for the brake lining. A dynamometer test was carried out to validate the effect of optimization on brake noise mitigation.

scholarly journals Monte Carlo localization algorithm based on particle swarm optimization

Automatika ◽  
2019 ◽  
Vol 60 (4) ◽  
pp. 451-461 ◽  
Author(s):  
Cuiran Li ◽  
Jianli Xie ◽  
Wei Wu ◽  
Haoshan Tian ◽  
Yingxin Liang
Keyword(s):  
Monte Carlo ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Localization Algorithm ◽  
Swarm Optimization ◽  
Monte Carlo Localization

Particle Swarm Optimization Algorithm with Mutation Operator for Particle Filter Noise Reduction in Mechanical Fault Diagnosis

2020 ◽  
Vol 34 (10) ◽  
pp. 2058012 ◽  
Author(s):  
Hanxin Chen ◽  
Dong Liang Fan ◽  
Lu Fang ◽  
Wenjian Huang ◽  
Jinmin Huang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Filter ◽  
Noise Reduction ◽  
Real Time ◽  
Particle Swarm ◽  
Utilization Rate ◽  
Mutation Operator ◽  
Swarm Optimization ◽  
Running Time ◽  
Filter Noise

In this paper, a new particle swarm optimization particle filter (NPSO-PF) algorithm is proposed, which is called particle cluster optimization particle filter algorithm with mutation operator, and is used for real-time filtering and noise reduction of nonlinear vibration signals. Because of its introduction of mutation operator, this algorithm overcomes the problem where by particle swarm optimization (PSO) algorithm easily falls into local optimal value, with a low calculation accuracy. At the same time, the distribution and diversity of particles in the sampling process are improved through the mutation operation. The defect of particle filter (PF) algorithm where the particles are poor and the utilization rate is not high is also solved. The mutation control function makes the particle set optimization process happen in the early and late stages, and improves the convergence speed of the particle set, which greatly reduces the running time of the whole algorithm. Simulation experiments show that compared with PF and PSO-PF algorithms, the proposed NPSO-PF algorithm has lower root mean square error, shorter running time, higher signal-to-noise ratio and more stable filtering performance. It is proved that the algorithm is suitable for real-time filtering and noise reduction processing of nonlinear signals.

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