Variable-scale self-adaptive denoising method for acoustic differential intelligent bearing and acoustic intelligent bearing

Abstract

The invention provides a variable-scale self-adaptive denoising method for an acoustic differential intelligent bearing and the acoustic intelligent bearing. The method comprises the following steps: acquiring an expected sound signal on a bearing outer ring end face, and acquiring a reference sound signal on an axle box cover; carrying out adaptive variational mode decomposition on the expected sound signal, and determining an optimal decomposition layer number and a bandwidth penalty coefficient; calculating an optimal modal center frequency, designing a Wiener filter bank according to the optimal modal center frequency, and decomposing the expected sound signal and the reference sound signal by adopting the Wiener filter bank to obtain a plurality of decomposed sub-signal pairs; filtering out in-band noise of the sub-signal of the expected sound signal by using the sub-signal of the reference sound signal, outputting all de-noising modes as final modes, and summing all de-noising modes to obtain a reconstructed de-noising signal. The method is high in adaptability, can effectively remove the noise in the collected sound signal of the bearing, and is particularly suitable for improving the signal-to-noise ratio of the train bearing rail side acoustic signal.

Description

technical field [0001] The invention relates to the technical field of mechanical component monitoring and fault diagnosis, in particular to a variable-scale self-adaptive denoising method for an acoustic differential intelligent bearing and an acoustic intelligent bearing. Background technique [0002] Bearings are a critical part of rotating instruments, supporting their weight and rotating at high speeds. Due to the harsh working conditions of heavy load, alternating stress and complex environment for a long time, the bearing structure is prone to deformation and loss, which will lead to equipment failure and shutdown, and even cause economic losses and casualties. Therefore, monitoring and diagnosing the health status of bearings is of great significance to maintaining the normal operation of equipment, and has great economic and social benefits. At present, the intelligentization of equipment is an important way to realize industrial modernization, which has attracted ...

AI Technical Summary

Problems solved by technology

However, VMD relies heavily on predefined parameters. Unreasonable parameter setting will lead to the loss of key fault information and the introduction of redundant noise, which is not conducive to the accurate perception and intelligent diagnosis of weak faults.

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