I am a newbie in matlab programming. Actually I have asked this question in mathwork website, but still I did not get the answer, so maybe I can get it here.
I am trying to do peak analysis to find the peak flow of storm water flow. Here is my code :
%% Peak flow analysis
% define data which are used for analysis
Date=finalCSVnew{:,1};
Flow=finalCSVnew{:,7};
figure(2);
[pks,locs]=findpeaks(Flow,Date,'MinPeakProminence',1,'MinPeakDistance',1);
findpeaks(Flow,Date,'MinPeakProminence',1,'MinPeakDistance',1);
text(locs+.02,pks,num2str((1:numel(pks))'));
xlabel('Date and Time');
ylabel('Flow [m3/h]');
title('Find All Peak Flows');
datacursormode on
I managed to plot the peak flow, and find the details about pks and locs. Here, each event should contain one peak flow. So in my case (based on attached picture) I should have 16 events. However, there is duplicate value in event 1 and event 2 which I want to delete one of them, but I am confused about how to do it. Also, I try to find the tutorial for calculating the duration of each event in the website, but I found nothing. I want to know about how to calculate the duration (probably in minutes) based on the peak flow data I got and to delete the peak value in the plot and in pks data which contain duplicates. Is it possible to do that? Could you please help me? Thank you very much for your help.peak flow events
For duplicate values, you can use the unique function to find values which are the same and remove them.
C = unique(pks) % find any unique values and output values without repetitions
https://au.mathworks.com/help/matlab/ref/unique.html
Provide more details about the duration you want to measure. Do you want to measure the duration of just the peak flow? Or of the entire curve leading up to the peak?
Related
I am processing an ocean wave data, where I have a timeseries of the Peak Wave Period (Tp (s)). The typical values for Tp ranges from 2s-15s for this location. However, it may reach higher values above 15s during extreme events such as a storm. Hence, removing data based on a threshold value is not suitable.
As you can see in the figure below, there are multiple values that are outliers. The high values occurred for a small duration and then dropped down. An extreme event would last for hours.
I have tried the functions filloutlier and medfilt1, but they are not successful in removing the outlier, which I presume is because multiple consecutive outlier data points exists.
Is there a built-in Matlab function exist to handle such situation?
Else, if I need to write my own function to filter such signals, could you provide some guidance.
Attaching a small data sample here as well: Download Data
Dataset plot (Only the segment in the provided data above)
Zoomed in plot at one of the outliers.
If we know that we need the values to be in the range of (2,15), we can clip the values > 15 to 15.
Another way is to use the value of a high percentile (say 95) of the observations and clip values about it.
filloutlier, medfilt1 methods are not removing values like 18 because they are not treating them as outliers. 18 is not very far away from the typical range of (2, 15).
I've recently started a new job and having never used MATLAB before, I'm a little stuck. Any help would be much appreciated.
Here is the story. I have been given the velocities of fragmentation pellets at time intervals of 0.001 milliseconds. There are 28 gauges (i.e 28 sets of data) and each gauge has 20,000 readings. I have created a matrix consisting of all this data.
My objective to take that matrix and create 2 more matrices with the corresponding displacement and acceleration values of each reading. The next step is to export the time and acceleration values to an excel spreadsheet.
I am at a loss as to how to do this. I have tried to integrate and differentiate but I cant seem to get it right. Is it possible to create a function that takes the velocity data and automatically calculates acceleration/displacement? (This would make things easier as people in the future could use that same code)
Any help on how to solve any part of this problem would be much appreciated. I've only been using the software 3 days.
Many thanks.
My data looks like this:
I am trying to find the following info about my data:
Rate of rise on the "transient" portion
time to steady state and steady state average
I think that stepinfo is my best bet to do this, but it seems to want take the final value as the steady state value which isn't giving me the best result. And I cannot find the average value of the steady time until I know when it is.... Is there a way to set some bounds on the steady state search? On the picture i linked it could be data within +/- 0.25 for 50 data points is steady state?
What you can do is:
Decide what the slope of the curve should be in the intersection between transient and steady state
Smoothen you signal
Find the difference between each point on the graph
Find the first place where the difference between points is lower then the value you selected in
To do this, keep in mind:
The difference in the beginning is on average zero, thus you have to skip these values.
One way to do this is simply: x(x < 0.1 * max(x)) = []; That way you remove the entire start of the curve. You won't need it for this part anyway. Remember to keep a backup of x.
A simple way to smoothen the signal is: smooth_x = arrayfun(#(t) mean(x(t:t+k)), 1:numel(x-k)). You need to find an appropriate value for k.
Even a smoothed curve will have "bumps", thus you might want to compare points that are not adjacent, for instance check diff(x(k),x(k+10)). If the average incline between those two points are lower than the value you selected in 1, you're happy. A combination of find and diff should do the trick here.
Once you have smoothed it, you can use diff to find the average inclination for both the transient and the stable part.
There is no way for me to tell where the curve goes from transient to stable. That's a decision you need to make. It could for instance be less than 0.2 l/min per 10 seconds.
I am trying to make a fatigue analysis from a load series and I woudl like to extract the equivalent moment for
number cycles=1000000
years=25
I have a time series for one hour that looks like:
Then I have read that Rainflow Analysis is a very good tool to extract the cycles from a time history. Therefore I apply:
%Rainflow moment
dt=time(2)-time(1);
[timeSeriesSig, extt] = sig2ext(timeSeries, dt);
rf = rainflow(timeSeriesSig,extt);
I read that
OUTPUT
rf - rainflow cycles: matrix 3xn or 5xn dependend on input,
rf(1,:) Cycles amplitude,
rf(2,:) Cycles mean value,
rf(3,:) Number of cycles (0.5 or 1.0),
rf(4,:) Begining time (when input includes dt or extt data),
rf(5,:) Cycle period (when input includes dt or extt data),
If I am interested in the number of cycles what does the term rf(3,:) mean? It is only containing 0.5 and 1 in the vector. I want to obtain an histogram with the number of cycles per bin amplitude. Thanks
If you are using code from the File Exchange or another source, it helps to link to where you obtained it.
Here, rf(3,:) is showing you whether the amplitudes and other outputs refer to half or full cycles. The rainflow algorithm finds half-cycles first, and then matches tensile and compressive half-cycles of equal amplitude to find full cycles. There are normally some residual half-cycles.
If you examine the rfhist function contained within that same File Exchange submission, you will see how they handle full and half amplitudes. Basically, two half-cycles at any given load amplitude will be counted as one full cycle when creating the histogram.
Suppose that I have a data set that contains a cyclical event and I am identifying a threshold (peaks) to separate each event (to eventually find the coefficient of variation).
I have multiple trials of this data - the speed of these events is sometimes significantly faster than others. This data is also a bit noisy, so some 'false local maximas' are sometimes picked up if I don't set the 'minpeakdistance' constraint within the 'findpeaks' function.
I am trying to find a way to ensure that regardless of speed, I am finding 'true local maximas'. I have been visually inspecting each trial to ensure that I have identified only true peaks - if I have also identified false peaks, I have been adjusted the mpd value for that specific trial - but this is literally going to take days.
Any suggestions?
Example:
For most trials of my collection, the following line of code only identifies true maximas:
mpd = 'minpeakdistance';
eval(['[t' num2str(a) '.Mspine.pks(:,1),t' num2str(a) '.Mspine.locs] = findpeaks(t' num2str(a) '.Mspine.xyz(:,1), mpd,25);']);
But, for trial 11, they are moving much faster, so the mpd has to be adjusted to 9; however, if I apply an mpd value of 9 to all of the trials, it will pick up false local maximas.
theI would go over to the frequency domain to find this "cyclical event". Specifically, if you know the rate at which data is sampled/generated, using a FFT will indicate the relative strengths of all periodic events in your data. Have a look at: http://www.mathworks.se/help/matlab/ref/fft.html