Data driving threshold value noise-reduction method for rotary machine vibration signals

Abstract

The invention discloses a data driving threshold value noise-reduction method for rotary machine vibration signals. According to the method, firstly, wavelet transformation is carried out on collected vibration acceleration signals, and the signals are decomposed into different frequency bands. Secondly, noise estimation is carried out on the signals at each frequency band, and data driving threshold values adapting to the signals are obtained. Thirdly, the signals are segmented through a sliding window technology. Finally, threshold value noise reduction is carried out on each segment of signals by utilizing the data driving threshold values, the signals are reconstructed, and time-domain signals after noise reduction are obtained. The data driving threshold values are from noise estimation of the signals, and the threshold values can be set in a self-adaptive mode according to the intensity of the noise. Compared with traditional threshold values, the data driving threshold values adapt to the signals, the threshold values are set more accurately, and weak fault signals are reserved while noise is reduced. The data driving threshold value noise-reduction method for the rotary machine vibration signals combines the advantages of wavelet transformation, the noise estimation algorithm, the sliding window technology and the 3sigma principle, can effectively extract failure characteristics, and achieves failure diagnosis of mechanical equipment.

Description

technical field [0001] The invention relates to a noise reduction method, in particular to a data-driven threshold value noise reduction method for vibration signals of rotating machinery. Background technique [0002] The mechanical vibration signals collected in engineering often contain strong background noise, so the fault feature information is submerged in the strong background noise, making it difficult to detect early failures of mechanical equipment in time, resulting in major casualties and economic loss accidents. Wavelet denoising is an effective method to extract fault features from strong background noise. The collected vibration signal is decomposed into different frequency bands after undergoing wavelet transformation. Thresholds are set for each frequency band, components smaller than the threshold are set to zero, and components greater than the threshold are retained. The components larger than the threshold are usually shock signals, which reflect the fa...

AI Technical Summary

Problems solved by technology

However, it is difficult to set the threshold accurately. If the threshold is too large, important fault features will be eliminated, and if the threshold is too small, a large noise component will be retained.

When the threshold is set unreasonably, fault features cannot be extracted effectively

Therefore, study the method of setting the threshold, accurately set the appropriate threshold, can retain fault information while eliminating noise, effectively extract the fault characteristics of mechanical equipment, arrange reasonable scheduled spare parts, shutdown replacement time, and arrange equipment maintenance before equipment failure work, effectively reduce sudden downtime, prevent serious damage to equipment, and prolong its service life. It is of great significance to ensure production safety and equipment reliability, but the existing technology cannot prepare a reasonable setting

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