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Determination of Standard Deviation and Means square Error
조회 수: 1 (최근 30일)
이전 댓글 표시
Can any one look in to the error I am getting from extracting the standard deviation and Mean Square Error of Matlab DTW of extracted features for fault signal and Steady state signals
댓글 수: 14
Florian Rössing
2022년 11월 24일
Hey, not wanting to be rude, but you provided a lot of files, could you elaborate how we can reproduce your error? Or better give a minimal example to reproduce the error?
john amoo otoo
2022년 11월 24일
Florian, thanks for the response the files you actually need is the getmswtfeat.m and Processdata.m. The data file you need is John_FaultData0km0ohm.mat. Once you run it that shoud reproduce the error Standard Deviation and Variance attached
Steven Lord
2022년 11월 24일
Can you post the full and exact text of those warning and/or error messages (all the text displayed in orange and/or red in the Command Window) that you receive? Knowing that may make determining what's going on and how to avoid the warning and/or error much easier and quicker.
Jeffrey Clark
2022년 11월 29일
@john amoo otoo, @Florian Rössing, @Steven Lord the error is the same exact one you posted in Matrix Dimensional Error Index error. Please only post once for any given issue.
john amoo otoo
2022년 11월 30일
이동: John D'Errico
2022년 12월 5일
% GETMSWTFEAT Gets the Multiscale Wavelet Transform features, these
% include: Energy, Variance, Standard Deviation, and Waveform Length
% feat = getmswtfeat(x,winsize,wininc,SF)
% ------------------------------------------------------------------
% The signals in x are divided into multiple windows of size
% "winsize" and the windows are spaced "wininc" apart.
% Inputs
% ------
% signals: columns of signals
% winsize: window size (length of x)
% wininc: spacing of the windows (winsize)
% SF: sampling frequency
%
% Outputs
% -------
% =========================================================================
function feature_out = getmswtfeat(signals,winsize,wininc,SF)
if nargin < 4
if nargin < 3
if nargin < 2
error('A sliding window approach requires the window size (winsize) as input')
end
error('A sliding window approach requires the window increment (wininc) as input')
end
error('Please provide the sampling frequency of this signal')
end
%% The number of decomposition levels
decomOption = 1;
if decomOption==1
J=8; % Number of decomposition levels set manually here
elseif decomOption==2
J=wmaxlev(winsize,'sym2'); % Number of decomposition levels set based on window size and wavelet family
else
J=(log(SF/2)/log(2))-1; % Number of decomposition levels set based on sampling frequency (SF)
end
%% make sure you have some parameters pre-defined
% specify the number of samples
datasize = size(signals,1);
% based on the number of samples, winsize, and wininc, how many windows we
% will have? this is "numwin"
numwin = floor((datasize - winsize)/wininc)+1;
% how many signals (electrodes) are we processing
Nsignals = size(signals,2);
% how many features we plan to extract
%%
NF = 8;
% predefine zeros matrix to allocate memory for output features
%feature_out = zeros(numwin,(J+1)*NF*Nsignals);
feature_out = zeros(numwin,(J+1)*Nsignals);
for dims =1:Nsignals
x=signals(:,dims);
%% Chop the signal according to a sliding window approach
% allocate memory
feat = zeros(winsize,numwin);
st = 1;
en = winsize;
for i = 1:numwin
feat(1:winsize,i) = x(st:en,:)-mean(x(st:en,:));
st = st + wininc;
en = en + wininc;
end
%% Multisignal one-dimensional wavelet transform decomposition
dec = mdwtdec('col',feat,J,'sym2');
% Proceed with Multisignal 1-D decomposition energy distribution
if isequal(dec.dirDec,'c')
dim = 1;
end
[cfs,longs] = wdec2cl(dec,'all');
level = length(longs)-2;
if dim==1
cfs = cfs';
longs = longs';
end
numOfSIGs = size(cfs,1);
num_CFS_TOT = size(cfs,2);
absCFS = abs(cfs);
absCFS0 = (cfs);
cfs_POW2 = absCFS.^2;
Energy = sum(cfs_POW2,2);
percentENER = zeros(size(cfs_POW2));
notZER = (Energy>0);
percentENER(notZER,:) = cfs_POW2(notZER,:);%./Energy(notZER,ones(1,num_CFS_TOT));
%% or try this version below and tell us which one is the best on your data
% percentENER(notZER,:) = cfs_POW2(notZER,:);
%% Pre-define and allocate memory
tab_ENER = zeros(numOfSIGs,level+1);
tab_VAR = zeros(numOfSIGs,level+1);
tab_STD = zeros(numOfSIGs,level+1);
tab_WL = zeros(numOfSIGs,level+1);
tab_entropy = zeros(numOfSIGs,level+1);
%% Feature extraction section
st = 1;
for k=1:level+1
nbCFS = longs(k);
en = st+nbCFS-1;
%tab_ENER(:,k) = sum(percentENER(:,st:en),2);% energy per waveform
%tab_VAR(:,k) = var(percentENER(:,st:en),0,2); % variance of coefficients
%tab_STD(:,k) = std(percentENER(:,st:en),[],2); % standard deviation of coefficients
%tab_WL(:,k) = sum(abs(diff(percentENER(:,st:en)')))'; % waveform length
%percentENER(:,st:en) = percentENER(:,st:en)./repmat(sum(percentENER(:,st:en),2),1,size(percentENER(:,st:en),2));
prob = percentENER(:,st:en);%./repmat(sum(percentENER(:,st:en),2),1,longs(k)) + eps;
tab_entropy(:,k) = -sum(prob.*log(prob),2);%./size(percentENER(:,st:en),2);
st = en + 1;
end
%feature_out(:,(1:(NF*(J+1)))+(dims-1)*((J+1)*NF)) =log([tab_ENER tab_VAR tab_STD tab_WL tab_entropy]);
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_entropy;
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_STD;
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_VAR;
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_WL;
end
john amoo otoo
2022년 11월 30일
이동: John D'Errico
2022년 12월 5일
load('John_FaultData0km0ohm.mat')
%% does this signal has any NaNs, if so remove
SteadyStateNoneFaultState = rmmissing(SteadyStateNoneFaultState);
DataPG0km0hmsFaultData = rmmissing(DataPG0km0hmsFaultData);
SSTime = SteadyStateNoneFaultState.Time;
SSNFS = SteadyStateNoneFaultState.SteadyStateNoneFaultState;
DPTime = DataPG0km0hmsFaultData.Time;
DPFD = DataPG0km0hmsFaultData.DataPG0km0hmsFaultData;
%% Define the filtering and inspect
n = 8; %% defines window length
w = [-ones(n,1); ones(n,1)];
SSNFS = filter(w, n, SSNFS);
DPFD = filter(w, n, DPFD);
%% Normalize signals
med_training = prctile(SSNFS,50);
iqr_training = iqr(SSNFS);
SSNFS = (SSNFS-med_training)./iqr_training;
med_fault = prctile(DPFD,50);
iqr_fault = iqr(DPFD);
DPFD = (DPFD-med_fault)./iqr_fault;
% Plot & Observe the data
subplot(2,1,1)
plot(DPTime, DPFD)
title('filtered DataPG0km5hmsFaultData')
subplot(2,1,2)
plot(SSTime, SSNFS)
title('filtered SteadyStateNoneFaultState')
%% Let's observe the FFT power spectrum for differences
feat_fault = getmswtfeat(DPFD,32,16,100000);
feat_Good = getmswtfeat(SSNFS,32,16,100000);
john amoo otoo
2022년 11월 30일
이동: John D'Errico
2022년 12월 5일
Please posted is the script, how do I add teh script Tab standard deviation, tab mean square error and tab variance
john amoo otoo
2022년 12월 6일
편집: Walter Roberson
2022년 12월 6일
%% Feature extraction section
st = 1;
for k=1:level+1
nbCFS = longs(k);
en = st+nbCFS-1;
%tab_ENER(:,k) = sum(percentENER(:,st:en),2);% energy per waveform
%tab_VAR(:,k) = var(percentENER(:,st:en),0,2); % variance of coefficients
%tab_STD(:,k) = std(percentENER(:,st:en),[],2); % standard deviation of coefficients
%tab_WL(:,k) = sum(abs(diff(percentENER(:,st:en)')))'; % waveform length
%percentENER(:,st:en) = percentENER(:,st:en)./repmat(sum(percentENER(:,st:en),2),1,size(percentENER(:,st:en),2));
prob = percentENER(:,st:en);%./repmat(sum(percentENER(:,st:en),2),1,longs(k)) + eps;
tab_entropy(:,k) = -sum(prob.*log(prob),2);%./size(percentENER(:,st:en),2);
tab_VAR(:,k) = var(percentENER(:,st:en),0,2);% variance of coefficients
tab_STD(:,k) = std(percentENER(:,st:en),[],2);% standard deviation of coefficients
tab_WL(:,k) = sum(abs(diff(percentENER(:,st:en)')))'; % waveform length
st = en + 1;
end
%feature_out(:,(1:(NF*(J+1)))+(dims-1)*((J+1)*NF)) =log([tab_ENER tab_VAR tab_STD tab_WL tab_entropy]);
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_entropy;
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_STD;
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_VAR;
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_WL;
end
john amoo otoo
2022년 12월 6일
Please could you look into why Tab STD, tab_VAR values does not show up in the work space. See attached
Walter Roberson
2022년 12월 6일
Either you are looking at the wrong workspace, or else there is something in the code before that point that is ending the code early. Notice that the list of variables does not even include st that you set as the very first posted statement.
Remember that the variables that are set in a function typically become inaccessible after the function returns.
john amoo otoo
2022년 12월 7일
이동: Walter Roberson
2022년 12월 7일
% GETMSWTFEAT Gets the Multiscale Wavelet Transform features, these
% include: Energy, Variance, Standard Deviation, and Waveform Length
% feat = getmswtfeat(x,winsize,wininc,SF)
% ------------------------------------------------------------------
% The signals in x are divided into multiple windows of size
% "winsize" and the windows are spaced "wininc" apart.
% Inputs
% ------
% signals: columns of signals
% winsize: window size (length of x)
% wininc: spacing of the windows (winsize)
% SF: sampling frequency
%
% Outputs
% -------
% =========================================================================
% REFERENCE: MATLAB CODE: Multi Scale Wavelet Decomposition: Dr. Rami Khushaba
% Email: Rami.Khushaba@sydney.edu.au
% URL: www.rami-khushaba.com (Matlab Code Section)
function feature_out = getmswtfeat(signals,winsize,wininc,SF)
if nargin < 4
if nargin < 3
if nargin < 2
error('A sliding window approach requires the window size (winsize) as input')
end
error('A sliding window approach requires the window increment (wininc) as input')
end
error('Please provide the sampling frequency of this signal')
end
%% The number of decomposition levels
decomOption = 1;
if decomOption==1
J=8; % Number of decomposition levels set manually here
elseif decomOption==2
J=wmaxlev(winsize,'db2'); % Number of decomposition levels set based on window size and wavelet family
else
J=(log(SF/2)/log(2))-1; % Number of decomposition levels set based on sampling frequency (SF)
end
%% make sure you have some parameters pre-defined
% specify the number of samples
datasize = size(signals,1);
% based on the number of samples, winsize, and wininc, how many windows we
% will have? this is "numwin"
numwin = floor((datasize - winsize)/wininc)+1;
% how many signals (electrodes) are we processing
Nsignals = size(signals,2);
% how many features we plan to extract
%%
NF = 8;
% predefine zeros matrix to allocate memory for output features
%feature_out = zeros(numwin,(J+1)*NF*Nsignals);
feature_out = zeros(numwin,(J+1)*Nsignals);
for dims =1:Nsignals
x=signals(:,dims);
%% Chop the signal according to a sliding window approach
% allocate memory
feat = zeros(winsize,numwin);
st = 1;
en = winsize;
for i = 1:numwin
feat(1:winsize,i) = x(st:en,:)-mean(x(st:en,:));
st = st + wininc;
en = en + wininc;
end
%% Multisignal one-dimensional wavelet transform decomposition
dec = mdwtdec('col',feat,J,'sym8');
% Proceed with Multisignal 1-D decomposition energy distribution
if isequal(dec.dirDec,'c')
dim = 1;
end
[cfs,longs] = wdec2cl(dec,'all');
level = length(longs)-2;
if dim==1
cfs = cfs';
longs = longs';
end
numOfSIGs = size(cfs,1);
num_CFS_TOT = size(cfs,2);
absCFS = abs(cfs);
absCFS0 = (cfs);
cfs_POW2 = absCFS.^2;
Energy = sum(cfs_POW2,2);
percentENER = zeros(size(cfs_POW2));
notZER = (Energy>0);
percentENER(notZER,:) = cfs_POW2(notZER,:);%./Energy(notZER,ones(1,num_CFS_TOT));
%% or try this version below and tell us which one is the best on your data
% percentENER(notZER,:) = cfs_POW2(notZER,:);
%% Pre-define and allocate memory
tab_ENER = zeros(numOfSIGs,level+1);
tab_VAR = zeros(numOfSIGs,level+1);
tab_STD = zeros(numOfSIGs,level+1);
tab_WL = zeros(numOfSIGs,level+1);
tab_entropy = zeros(numOfSIGs,level+1);
%% Feature extraction section
st = 1;
for k=1:level+1
nbCFS = longs(k);
en = st+nbCFS-1;
%tab_ENER(:,k) = sum(percentENER(:,st:en),2);% energy per waveform
%tab_VAR(:,k) = var(percentENER(:,st:en),0,2); % variance of coefficients
%tab_STD(:,k) = std(percentENER(:,st:en),[],2); % standard deviation of coefficients
%tab_WL(:,k) = sum(abs(diff(percentENER(:,st:en)')))'; % waveform length
%percentENER(:,st:en) = percentENER(:,st:en)./repmat(sum(percentENER(:,st:en),2),1,size(percentENER(:,st:en),2));
prob = percentENER(:,st:en);%./repmat(sum(percentENER(:,st:en),2),1,longs(k)) + eps;
tab_entropy(:,k) = -sum(prob.*log(prob),2);%./size(percentENER(:,st:en),2);
tab_VAR(:,k) = var(percentENER(:,st:en),0,2);% variance of coefficients
tab_STD(:,k) = std(percentENER(:,st:en),[],2);% standard deviation of coefficients
tab_WL(:,k) = sum(abs(diff(percentENER(:,st:en)')))'; % waveform length
st = en + 1;
end
%feature_out(:,(1:(NF*(J+1)))+(dims-1)*((J+1)*NF)) =log([tab_ENER tab_VAR tab_STD tab_WL tab_entropy]);
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_entropy;
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_STD;
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_VAR;
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_WL;
end
Walter Roberson
2022년 12월 7일
The first statement in your function after testing nargin is
decomOption = 1;
but the workspace you showed does not even have a variable decomOption in it. It does, however, have some variables that are not set in the function.
The implication is that the workspace you show is not the workspace for the function you are discussing.
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