How to convert acceleration data to velocity and displacement?

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Rohit Bhoi
Rohit Bhoi 2016 年 3 月 12 日
コメント済み: Ricky Cao 2021 年 12 月 1 日
I have acceleration data and I need to convert it into velocity and displacement.My sampling frequency is 500 Hz. I used the following approach but my result are not accurate. Need suggestion. My approch:
1>Fist took acceleration data
2>Then I filter that data using butter worth high pass filter of frequency hpf=50. I used this command [b,a]=butter(5,Fhp/Fs,'high');
dataa=filter(b,a,dataa);
3>then to get velocity I used 'cumtrapz' to integrate acceleration with code datav=cumtrapz(dataa)./Fs;
4>again I filter this velocity data and again integrate to get displacement.
5>finally I plotted this data.results are not proper.
suggest me some thing.
my code is
csvData=handles.csvData;% Read File
dataa=(csvData(:,2)).*9.81; % Data is converted to m/sqr.s
[b,a]=butter(5,Fhp/Fs,'high'); %Filter data
dataa=filter(b,a,dataa);
time=csvData(:,1); %first row is time data
datav=cumtrapz(dataa)./Fs; %Acceleration to Velocity convertsion using integration
[b,a]=butter(5,Fhp/Fs,'high'); %Filter data
datav=filter(b,a,datav);
datad=cumtrapz(datav)./Fs; %Velocity to displacement conversion using integration
  1 件のコメント
Pooja Hegde
Pooja Hegde 2021 年 6 月 2 日
I'm facing similar issue. How could you fix this problem

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回答 (1 件)

KSSV
KSSV 2016 年 3 月 12 日
Dear friend
You have acceleration data, to convert it into velocity and displacement, you need to differentiate the accl. data twice. You may use numerical differentiation like Trapezoidal rule, Simpsons rule etc. You may also use finite difference time schemes like Newmark beta method, wilson method etc.
  4 件のコメント
Mohammad Saiful Islam
Mohammad Saiful Islam 2016 年 6 月 24 日
Dear Dr. Siva Srinivas Kolukula, Probably you are little wrong with the conversion of acceleration to displacement. You can not get the displacement by double differentiation of acceleration. You can get acceleration from double differentiation of displacement.
Ricky Cao
Ricky Cao 2021 年 12 月 1 日
Na, you are so wrong.

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