Adaptive redundant lifting wavelet noise reduction analysis-based bearing failure recognition method

Abstract

The invention relates to an adaptive redundant lifting wavelet noise reduction analysis-based bearing failure recognition method, belonging to the technical field of failure recognition of a rolling bearing. The technical scheme is that the method comprises the following steps of: (1) performing adaptive redundant lifting wavelet transformation of a bearing vibration signal; (2) performing variable-size threshold noise reduction processing on a high-frequency detail signal obtained by each decomposition process; (3) performing complete reverse reconstruction on a low-frequency approximation signal obtained by final decomposition and a high-frequency detail signal subjected to wavelet threshold noise reduction; and (4) performing Hilbert demodulation processing on a reconstructed signal to obtain an envelope spectrogram of an initial vibration signal, extracting and recognizing a frequency component in the spectrogram, and judging that a bearing fails if frequency conversion or failure characteristic frequency and even corresponding frequency multiplication occurs. The adaptive redundant lifting wavelet noise reduction analysis-based bearing failure recognition method has the beneficial effects that a threshold can be flexibly selected according to the characteristic of change of noise in a wavelet region, so that noise can be filtered better, and meanwhile, the completeness of a real signal can also be guaranteed as much as possible.

Description

technical field [0001] The invention relates to a bearing fault identification method based on adaptive redundant lifting wavelet noise reduction analysis, and belongs to the technical field of fault identification of rolling bearings. Background technique [0002] In many modern equipments, bearings are one of the most widely used and also the most prone to failure components. The equipment shutdown and production shutdown caused by its failure will cause huge economic losses to the enterprise. In order to ensure normal production and prevent adverse consequences to the greatest extent, it is particularly important and necessary to carry out effective condition monitoring and diagnosis for bearings. When carrying out state monitoring on bearings, collecting its vibration signals and using certain techniques for analysis and processing, and at the same time judging the operating state of bearings based on the fault mechanism is the most commonly used way at present. In man...

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Problems solved by technology

However, for traditional wavelets, due to the constraints of the dual-scale equations, the same wavelet is always used for signal decomposition at different scales, so it is difficult to match the characteristics of signals at different scales.

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