Amirkabir University of Technology Amirkabir Journal of Mechanical Engineering 2008-6032 53 Issue 4 (Special Issue) 2021 06 22 Denoising vibration signals of rotating machines using probability density function, similarity measure and improved thresholding function Denoising vibration signals of rotating machines using probability density function, similarity measure and improved thresholding function 2493 2512 4106 10.22060/mej.2020.18129.6742 FA Saeed Nezamivand Chegini Department of Dynamics, Control and Vibrations, Faculty of Mechanical Engineering, The University of Guilan, Rasht, Iran Zohreh Karimi Rastehkenari Department of Mechanical Engineering, Ahrar Institute of Technology and Higher Education, Rasht, Iran Ahmad Bagheri Department of Dynamics, Control and Vibrations, Faculty of Mechanical Engineering, the University of Guilan, Rasht, Iran Bahman Ahmadi Department of Mechanical Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran Journal Article 2020 03 17 In this paper, a new method for removing the noise from the vibration signals acquired from the rotating machinery for its condition monitoring is presented. Firstly, each signal is decomposed into its modes using the empirical mode decomposition method. Then for distinguishing the noisy modes from the noise-free modes, the similarity measure between the probability density function of the raw signal and its modes is calculated. Finally, the noise-dominate modes are denoised by the improved thresholding function, and the denoised signal is reconstructed. In this study, the proposed method is implemented for denoising the simulated signal and real data corresponding to different bearing conditions. Finally, the kurtosis and the envelope spectrum of the denoised signal are calculated for detecting the fault presence and its type. The results show that the proposed technique can improve the quality of the reconstructed signals so that the sensitivity of the kurtosis factor to the presence of the defect in the inner and outer rings is increased. Also, the defects frequencies appear in the spectrum of the signals denoised, and the fault type can easily be detected. The results indicate that the proposed denoising technique is superior to the conventional empirical mode decomposition-based denoising method. In this paper, a new method for removing the noise from the vibration signals acquired from the rotating machinery for its condition monitoring is presented. Firstly, each signal is decomposed into its modes using the empirical mode decomposition method. Then for distinguishing the noisy modes from the noise-free modes, the similarity measure between the probability density function of the raw signal and its modes is calculated. Finally, the noise-dominate modes are denoised by the improved thresholding function, and the denoised signal is reconstructed. In this study, the proposed method is implemented for denoising the simulated signal and real data corresponding to different bearing conditions. Finally, the kurtosis and the envelope spectrum of the denoised signal are calculated for detecting the fault presence and its type. The results show that the proposed technique can improve the quality of the reconstructed signals so that the sensitivity of the kurtosis factor to the presence of the defect in the inner and outer rings is increased. Also, the defects frequencies appear in the spectrum of the signals denoised, and the fault type can easily be detected. The results indicate that the proposed denoising technique is superior to the conventional empirical mode decomposition-based denoising method. https://mej.aut.ac.ir/article_4106_4ca9764c98c203d7bc747cbce9d9174f.pdf