基于轴承故障信号特征的自适应冲击字典匹配追踪方法及应用

Journal of Vibration and Shock（2014）

Cited 8|Views5

Abstract

针对滚动轴承故障特征,提出了一种自适应冲击字典匹配追踪方法.根据轴承故障信号的产生机理,将轴承的转速、尺寸等因素引入到字典中,建立了一种基于故障信号特征的新型字典模型.依据字典模型中各个关键参数对分析结果的影响程度,确定冲击位置信息为首要模型参数,提出了逐次改变特性参数的方法建立自适应字典,使得字典中的每一个原子都与被分析信号有很好的相似度,降低了字典的冗余程度,提高了字典的使用效率.同时结合匹配追踪原理建立了自适应冲击字典匹配追踪的方法.仿真信号,实验信号和工程信号分析结果表明,基于自适应冲击字典匹配追踪方法可以对轴承不同位置的故障进行有效诊断.将该方法与遗传算法匹配追踪进行比较,表明该方法的处理效果更佳.

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

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

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper