Solving an Integration Error with Acceleration Data

In summary: Assuming constant acceleration during a time interval Δt, the displacement during Δt is d=(v(initial)+v(final))/2 *Δt.
  • #1
mark2468
12
0
Hi.

I have a set of data representing acceleration. To get the distance I integrate twice using a summation method and looks like:

accelleration: 0, 40, 30, -50,-80, -40.
velocity: 0, 40, 70, 20, -60, -100.
distance: 0, 40, 110, 130, 70, -30.

The distance should be zero as the movement goes back to the original point so there clearly is an error. Is there a better way to get the distance. I have heard runge kutta is good for this sort of stuff but am not sure where and how to put the data in. Any suggestions to how i can sort out this error.

Thanks.

Mark.
 
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  • #2
mark2468 said:
Hi.

I have a set of data representing acceleration. To get the distance I integrate twice using a summation method and looks like:

accelleration: 0, 40, 30, -50,-80, -40.
velocity: 0, 40, 70, 20, -60, -100.
distance: 0, 40, 110, 130, 70, -30.

The distance should be zero as the movement goes back to the original point so there clearly is an error. Is there a better way to get the distance. I have heard runge kutta is good for this sort of stuff but am not sure where and how to put the data in. Any suggestions to how i can sort out this error.

Thanks.

Mark.

Can you include time information in your dataset? What are the time intervals?
 
  • #3
time intervals are 0.333 seconds.
 
  • #4
mark2468 said:
time intervals are 0.333 seconds.

If your acceleration is 40m/s^2 for 1/3 of a second, you are not going 40m/s at the end of that time interval...
 
  • #5
mark2468 said:
time intervals are 0.333 seconds.
And is the acceleration in units per second^2? If so, haven't you got the wrong values for velocity, as the time intervals were less than a second?

EDIT: missed berkeman's post, sorry
 
  • #6
Apart from the length of the time interval, the displacement during a time interval Δt is the average velocity multiplied by Δt. In the first interval it is 20 (Δt)2, and the total displacement is the sum of all the individual displacements.

ehild
 
  • #7
So what is the best way to synchronise them. Is there a formula or just divide by that ammout. e.g. 40/3, 30/3 etc or whatever the sample interval is. Any links with examples of this available?

Thanks again.
 
  • #8
Assuming constant acceleration during a time interval Δt, the displacement during Δt is

d=(v(initial)+v(final))/2 *Δt.

ehild
 

1. How do I identify an integration error in acceleration data?

An integration error in acceleration data can be identified by looking for any abrupt changes or spikes in the data. These changes may indicate incorrect calculations or measurements, resulting in an error in the integration process.

2. What causes integration errors in acceleration data?

Integration errors in acceleration data can be caused by various factors such as incorrect calibration of sensors, noise interference, human error in data entry, or incorrect mathematical calculations. It is important to carefully analyze the data and identify the root cause of the error.

3. How can I prevent integration errors in acceleration data?

To prevent integration errors in acceleration data, it is important to properly calibrate the sensors used for data collection and ensure that the data is free from any external interference. Using reliable and accurate mathematical equations and double-checking calculations can also help prevent errors.

4. What are some techniques for solving integration errors in acceleration data?

One technique for solving integration errors in acceleration data is to use filtering methods to remove any noise or unwanted data points. Another approach is to use advanced integration techniques such as Kalman filtering or adaptive integration, which can account for changes in acceleration over time.

5. Are there any software tools available for solving integration errors in acceleration data?

Yes, there are various software tools and algorithms available that can help with solving integration errors in acceleration data. Some examples include MATLAB, Python libraries such as SciPy, and specialized software for sensor fusion and data analysis. It is important to carefully choose the appropriate tool for the specific type of integration error and data being analyzed.

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