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📋 📋 📋 本文目录如下: 🎁 🎁 🎁
目录
💥1 概述
📚2 运行结果
🎉3 参考文献
🌈4 Matlab代码实现
💥1 概述
滚动轴承故障检测方法一般包括温度分析、油液分析以及振动信号检测等,通过不同的传感器的信号表现形式可以从不同角度分析轴承故障,通过多种方法的结合运用可以更加准确地判断轴承
故障。
本文可用于在匀速运行的滚动轴承中进行基于振动的故障诊断。
这是一个三步程序:(i)倒谱预白化:
减少其他周期性来源(如齿轮)的贡献。
(ii) 带通滤波:提高信噪比,特别是当对系统共振执行时 (iii) 平方包络频谱:允许检测
(伪)循环稳态贡献,其特征是在特定循环频率下具有大分量
此功能与一个简单的演示一起提供,并且与倍频程完全兼容。
📚2 运行结果
部分代码:
function [xSES,alpha,th] = SES(x,fs,bpf,plotFlag,p,cpswFlag) %% Estimation of the Squared Envelope Spectrum % this function can be used for detecting bearing faults under constant % working speed % % INPUTS % x = input signal % fs = sampling frequency % bpf = band-pass filter frequencies, use a vector as [f lower, f higher] % put and empty vector if band-pass filtering is not needed % bearing fault detection can be improved if performed in a frequency band % wher the SNR is high (typically about a system resonance) % plotFlag = display the SES, 0 -> no (default), 1 -> yes % p = threshold significance level, default p = .999 (99.9%) % cpswFlag = cesptrum pre-whitening, 0 -> no (default), 1 -> yes % bearing fault detection is affected by periodic contribution due to % external sources such as gears. This effect can be reduced by whitening % the signal before SES % % OUTPUTS % SES = squared envelope spectrum % alpha = cyclic frequencies % th = threshold % % REF: Borghesani P. et al, Application of cepstrum pre-whitening for the diagnosis of bearing % faults under variable speed conditions, MSSP, 2013. % % M. Buzzoni % May 2019 if nargin < 4 plotFlag = 0; p = .999; cpswFlag = 0; end if nargin < 5 p = .999; cpswFlag = 0; end if nargin < 6 cpswFlag = 0; end L = length(x); k = (0:L-1); % cepstrum pre-whitening if cpswFlag == 1; x = real(ifft(fft(x) ./ abs(fft(x)))); end % band-pass filtering and ses estimation if isempty(bpf) l = 1 h = floor(L/2)+1; wfilt = zeros(size(x)); wfilt(l:h) = 1; xf = ifft(2 .* fft(x) .* wfilt); % filtered analytic signal else l = floor(bpf(1)*L/fs); % lower freq. index h = floor(bpf(2)*L/fs); % higher freq. index wfilt = zeros(size(x)); wfilt(l:h) = 1; xf = ifft(2 .* fft(x) .* wfilt); % filtered analytic signal end ENV = abs(xf).^2; % squared envelope xSES = abs(1/L .* fft( ENV )) .^ 2; % squared envelope spectrum % threshold S0 = (h - l - k) ./ (2 * (h - l)^2 ) .* (mean(abs(xf).^2)).^2; th = chi2inv(p,2) .* S0; % keep only meaningful cyclic frequencies alpha = k .* fs ./ L; % cyclic frequencies vector alpha = alpha(1:h - l); xSES = xSES(1:h - l); xSES(1) = 0; % put to zero the DC-term of SES in order to th = th(1:h - l); % improve its visualization if plotFlag == 1 % display results tt = k ./ fs; % time vector figure subplot(211) plot(tt,ENV,'k') title('squared envelope') xlabel('time (s)') box off subplot(212) plot(alpha,xSES,'k') title('squared envelope spectrum') hold on, plot(alpha,th,'r') legend('SES',[num2str(p .* 100) '% threhsold' ]) xlabel('cyclic frequency (Hz)') box off end
🎉3 参考文献
部分理论来源于网络,如有侵权请联系删除。
[1]刁宁昆. 滚动轴承故障检测的无监督学习方法研究[D].石家庄铁道大学,2022.DOI:10.27334/d.cnki.gstdy.2022.000368.
[2]Borghesani P. et al, Application of cepstrum pre-whitening for the diagnosis of bearing faults under variable speed conditions, MSSP, 2013.