国外文献 > 1.轴承 > 4. 设计与分析 > 设计方法
关键词搜索：
| 标 题： |
A general sequential Monte Carlo method based optimal wavelet filter: A Bayesian approach for extracting bearing fault features
基于最优小波滤波器的一般顺序蒙特卡洛方法：提取轴承故障特征的贝叶斯方法 |
| 作 者： |
Wang, Dong
Sun, Shilong
Tse, Peter W. |
| 关键词： |
Bearing [轴承]
Fault diagnosis [ 故障诊断]
Monte Carlo methods [ 蒙特卡洛方法]
Optimization [ 优化]
Wavelet transforms [ 小波变换]
Vibrations [ 振动] |
| 出版年： |
2015 |
| 期刊名： |
Mechanical Systems & Signal Processing
机械系统与信号处理 |
| 年卷期： |
2015, vol.52/53, no. |
| 摘 要： |
A general sequential Monte Carlo method, particularly a general particle filter, attracts much attention in prognostics recently because it is able to on-line estimate posterior probability density functions of the state functions used in a state space model without making restrictive assumptions. In this paper, the general particle filter is introduced to optimize a wavelet filter for extracting bearing fault features. The major innovation of this paper is that a joint posterior probability density function of wavelet parameters is represented by a set of random particles with their associated weights, which is seldom reported. Once the joint posterior probability density function of wavelet parameters is derived, the approximately optimal center frequency and bandwidth can be determined and be used to perform an optimal wavelet filtering for extracting bearing fault features. Two case studies are investigated to illustrate the effectiveness of the proposed method. The results show that the proposed method provides a Bayesian approach to extract bearing fault features. Additionally, the proposed method can be generalized by using different wavelet functions and metrics and be applied more widely to any other situation in which the optimal wavelet filtering is required. (C) 2014 Elsevier Ltd. All rights reserved.
一种通用的顺序蒙特卡洛方法，特别是通用粒子滤波器，近年来在预测领域引起了广泛关注，因为它能够在不作任何严格假设的情况下，实时估计状态空间模型中所用状态函数的后验概率密度函数。本文提出将通用粒子滤波器用于优化小波滤波器，以提取轴承故障特征。本文的主要创新在于，通过一组随机粒子及其对应的权重来表示小波参数的联合后验概率密度函数，这一方法鲜有报道。一旦得到小波参数的联合后验概率密度函数，便可确定近似最优的中心频率和带宽，并用于执行最优的小波滤波，从而提取轴承故障特征。本文通过两个案例研究验证了所提方法的有效性。结果表明，该方法为提取轴承故障特征提供了一种贝叶斯方法。此外，所提出的方法可通过使用不同的小波函数和度量进行推广，并可更广泛地应用于任何需要最优小波滤波的其他情况。（C）2014 Elsevier Ltd. 保留所有权利。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
京公网安备 11010202009788