Error with data processing

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John Amoo-Otoo 2022-7-19

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1763300-error-with-data-processing

评论： John Amoo-Otoo 2022-9-28

采纳的回答： Walter Roberson

processdata.m

I get an error if I run this matlab code of processing data for Discrete Wavelet Transformation analysis

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John Amoo-Otoo 2022-7-21

processdata.m

Attached the file

John Amoo-Otoo 2022-7-21

John_FaultData0km0ohm.mat

Attached the data

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Walter Roberson 2022-7-19

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https://ww2.mathworks.cn/matlabcentral/answers/1763300-error-with-data-processing#answer_1010620

You need getmswtfeat from https://www.mathworks.com/matlabcentral/fileexchange/33146-feature-extraction-using-multisignal-wavelet-packet-decomposition

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Walter Roberson 2022-7-22

在 MATLAB Online 中打开

I do not know what you want to do with the features extracted.

Note that there is a theoretical fault in the code. You remove data that has nan, but you do not take into account that that leaves a hole in the timing. You never use the timing information.

In practice the only nan is at the very last entry in the SteadyState, so removing it makes no timing difference for this data.

%% load the data first
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 DataPG0km0hmsFaultData')
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);