Multi-wavelet self-adaptive block threshold noise-reducing time domain diagnosis method for damage of gearbox

Abstract

The invention discloses a multi-wavelet self-adaptive block threshold noise-reducing time domain diagnosis method for damage of a gearbox. The method includes taking minimum stein unbiased risk assessment error as a constraint condition according to corelation of multi-wavelet coefficients, and selecting the optimum neighborhood block length and threshold self-adaptively so as to effectively remove interference of noise and extracting signal features accurately; analyzing vibration signals of the transmission gearbox of a tandem mill by the self-adaptive neighborhood block threshold noise-reducing process which is constructed so as to acquire reconstruction signals after noise reducing, and then effectively extracting damage feature of the transmission gearbox of the tandem mill by analyzing periodicity of the time domain impulse waveform. The method has reliable results, is good in real-time performance and high in universality, easy and feasible, and applicable to diagnosis of damage of the transmission gearbox of heavy-load equipment such as the tandem mill.

Description

technical field [0001] The invention relates to mechanical equipment fault diagnosis technology, in particular to a fault diagnosis method for gear box damage. Background technique [0002] The faults initiated during the operation of the gearbox (that is, early faults) have no obvious symptoms and weak characteristic information, and are often overwhelmed by the strong background noise during the operation of mechanical equipment, so that the dynamic monitoring, diagnosis and Failure prediction is increasingly difficult. Therefore, how to denoise the collected vibration signals and highlight or extract useful feature information is a key issue in fault diagnosis and fault prediction. [0003] Based on the characteristic of correlation between adjacent wavelet coefficients, the existing noise reduction processing method is generally block threshold noise reduction, which uses adjacent wavelet coefficients as a whole for threshold processing. Its disadvantages are: (1) The ...

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Problems solved by technology

Its disadvantages are: (1) The block threshold noise reduction method selects the neighborhood block length based on experience, which often produces a large deviation; (2) Each layer of wavelet decomposition uses a global threshold, and the global threshold does not not optimal

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