A rolling bearing fault signal denoising algorithm that combines a new adaptive information entropy with a new wavelet threshold function DOI
Min Li, Xuemei Li, Bin Liu

et al.

Engineering Research Express, Journal Year: 2024, Volume and Issue: 6(4), P. 045536 - 045536

Published: Oct. 25, 2024

Abstract Mechanical fault diagnosis is of great significance to industrial automation, and extracting vibration signals one the important tasks in mechanical health monitoring diagnosis. However, due complex working environment rolling bearings, a large amount noise makes it difficult extract signals. Denoising signal bearings can remove interference noise, simplify early identification features, thus improve diagnostic accuracy maintenance efficiency. This paper proposes bearing denoising algorithm, which constructs new feature extraction function. method first decomposes noisy into Intrinsic Mode Functions (IMFs) by Computing Expressive Empirical Decomposition with Adaptive Noise (ICEEMDAN). Secondly, adaptive information entropy threshold function constructed IMFs from it. Then, IMF denoised wavelet Finally, noise-free are reconstructed reconstruct signal. To verify actual performance comparative experiments were conducted on self-collected dataset public dataset, results show that this improves continuity reconstruction various types more effectively accurately, thereby improving detection 2%–9%.

Language: Английский

A WOA-ICEEMDAN joint wavelet threshold function based denoising method for ultrasound signals DOI
Jiaqi Zhang, Songsong Li, Xuan Liu

et al.

Nondestructive Testing And Evaluation, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 36

Published: Feb. 16, 2025

Various interference waves are often interspersed in faulty ultrasonic echo signals and easily eradicated by noise, resulting a low signal-to-noise ratio of the received signals. To address this problem, study proposes joint wavelet threshold function denoising method based on Whale Optimization Algorithm (WOA) optimised Adaptive Complete Ensemble Empirical Decomposition Noise (ICEEMDAN). First, uses WOA to optimise two parameters ICEEMDAN: white noise amplitude weight (Nstd) number additions (NR). The Sample Entropy (SampEn) is combined as fitness function, then WOA-ICEEMDAN decomposition ultrasound signal performed obtain series intrinsic mode functions (IMFs). Second, correlation coefficient applied separate IMF into useful components. multi-scale time-frequency localisation properties algorithm utilised analyse component, extract valuable information from it, reconstruct processed components create final denoised signal. Finally, verified simulation real experiments. Compared with hard soft methods, improves 44.8% 24.9%, while root-mean-square error declines 52.3% 38%, respectively.

Language: Английский

Citations

1
Wind Speed Prediction Method Based on Improved Stochastic Configuration Networks DOI
Yuanhao Yu, Ying Han,

Yu-Beng Leau

et al.

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 345 - 352

Published: Jan. 1, 2025

Language: Английский

Citations

0
A noise reduction method for rolling bearing based on improved Wiener filtering DOI
Mingyue Yu, Jingwen Su, Yan Wang

et al.

Review of Scientific Instruments, Journal Year: 2025, Volume and Issue: 96(2)

Published: Feb. 1, 2025

To accurately identify compound faults of bearings, a new noise reduction method is presented. With the method, input signals and order Wiener filtering are adaptively determined according to feature mode decomposition (FMD), signal evaluation index, Euclidean distance. First, effectively separate frequency components from vibration signals, decomposed into modal based on FMD algorithm; second, kurtosis, root mean square, variance, which sensitive fault information, selected build vectors. Third, distance between vectors component original calculated represent correlation among signals. By acquiring two that have greatest least an actual mixed required by can be determined. Furthermore, with maximum kurtosis as criterion. Finally, features extracted through spectral analysis after type judged that. demonstrate accuracy effectiveness proposed compared classical method. The result comparison shows presented restrict more determine complex bearings accurately.

Language: Английский

Citations

0
A rolling bearing fault signal denoising algorithm that combines a new adaptive information entropy with a new wavelet threshold function DOI
Min Li, Xuemei Li, Bin Liu

et al.

Engineering Research Express, Journal Year: 2024, Volume and Issue: 6(4), P. 045536 - 045536

Published: Oct. 25, 2024

Abstract Mechanical fault diagnosis is of great significance to industrial automation, and extracting vibration signals one the important tasks in mechanical health monitoring diagnosis. However, due complex working environment rolling bearings, a large amount noise makes it difficult extract signals. Denoising signal bearings can remove interference noise, simplify early identification features, thus improve diagnostic accuracy maintenance efficiency. This paper proposes bearing denoising algorithm, which constructs new feature extraction function. method first decomposes noisy into Intrinsic Mode Functions (IMFs) by Computing Expressive Empirical Decomposition with Adaptive Noise (ICEEMDAN). Secondly, adaptive information entropy threshold function constructed IMFs from it. Then, IMF denoised wavelet Finally, noise-free are reconstructed reconstruct signal. To verify actual performance comparative experiments were conducted on self-collected dataset public dataset, results show that this improves continuity reconstruction various types more effectively accurately, thereby improving detection 2%–9%.

Language: Английский

Citations

0