自适应冗余提升小波降噪分析及轴承故障识别应用

Zhendong yu Chongji/Journal of Vibration and Shock（2013）

Cited 4|Views13

Abstract

为有效识别机械设备中滚动轴承微弱故障信息,提出自适应冗余提升小波降噪方法.据待分解低频尺度系数所含不同特征,应用范数准则自适应选取最匹配该尺度系数特征的小波函数.引入多孔算法,用以通过冗余性保证逐层分解后各尺度系数与小波系数所含丰富信息量.对各层小波系数采用变尺度阈值降噪算法,并对降噪后系数进行重构及包络谱分析,提取滚动轴承故障特征.通过对实验台轴承混合故障信号与现场实际信号分析表明,故障识别较好,从而验证该方法的有效性.

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Key words

Adaptive,Bearing,Denoising,Fault identification,Redundant lifting wavelet

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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