Finding power figures from graph data

In summary, the conversation revolves around calculating power information from data collected from an Optical Rotary Encoder. The formula used for power assumes a constant rpm, which may not be accurate. A more accurate method would be to calculate the initial and final energy and divide the difference by time to get the average power in a model-independent way. However, due to the uncertainty of when acceleration starts, all methods may yield inaccurate results. It is suggested to remove the first interval and consider the possibility of varying power before calculating the average power.
  • #1
lownlazy
13
0
I need help! Could you please double checking my work: calculating a Power information from data I am collecting. I have been Googling and wiki'ing for weeks now and I think I have it right but I need someone to confirm or correct what i have done..

The graph data comes from and Optical Rotary Encoder, which for the unfamiliar, is used to measure/feedback the position of a shaft. In my case I am using it to measure the acceleration of a shaft spin for this project. I have been working on the project for 2 years now and getting past this math stage would be a huge step towards completion.

also, sorry about the Sudo-notation . :)

punchbot math.jpg
swing arm graph.PNG
 
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  • #2
The formula you use for power assumes a constant rpm, something that is certainly not true.

During the first interval (lasting 1.6ms) the wheel accelerates a lot, assuming a uniform velocity within that timescale (to get an angular velocity) is not a good approximation. This does matter if you want to calculate the acceleration - but that gives larger errors anyway.
I would calculate the initial energy and the final energy, divide the difference by the time and you get the average power in a very model-independent way.
 
  • #3
mfb said:
I would calculate the initial energy and the final energy, divide the difference by the time and you get the average power in a very model-independent way.

I will give this (above quoted) a go for sure.

In you reply you say that the 1.6 micro-second interval " assuming a uniform velocity within that timescale is not a good approximation." are you saying I am working with "bad" data? If so does this mean that i try your solution, or any solution, that will will yield inaccurate results?
 
  • #4
Well no method will give exact results as your data is available in those time steps only.
In particular, you do not know when exactly acceleration started, which gives an uncertainty of ~800µs.
 
  • #5
mfb said:
Well no method will give exact results as your data is available in those time steps only.
In particular, you do not know when exactly acceleration started, which gives an uncertainty of ~800µs.

Yes I should have mentioned this earlier, the acceleration starts in the interval before X1. I didnt include it because it is impossible to know its real angle and is therefore null data. Does this effect what you have replied with so far?
 
  • #6
Then we don't know when the acceleration starts? Well, it increases the uncertainty.
If there is some physical motivation to assume a constant torque or a constant power, a fit to the data might give better results, but I don't think it helps here.
 
  • #7
mfb said:
I would calculate the initial energy and the final energy, divide the difference by the time and you get the average power

I was wondering... do you think it would be more accurate to find the power for each of the 12 intervals and get the average from across those?
 
  • #8
That will give exactly the same result. But it might be interesting to remove the first interval. On the other hand, I would not expect power to be constant, so you have to figure out what exactly you want to calculate anyway.
 

Related to Finding power figures from graph data

What is the purpose of finding power figures from graph data?

The purpose of finding power figures from graph data is to analyze and understand the relationships between variables in a given dataset. This can help identify patterns and trends, make predictions, and inform decision-making in various fields such as economics, social sciences, and engineering.

What are power figures and how are they calculated?

Power figures, also known as power values, are numerical values that represent the strength of the relationship between two variables. They are typically calculated using statistical methods such as regression analysis or correlation coefficients, which measure the degree and direction of the relationship between variables.

What factors can affect the accuracy of power figures?

The accuracy of power figures can be affected by various factors such as the quality and completeness of the data, the assumptions made in the analysis, and the choice of statistical methods. It is important to carefully evaluate and interpret the results to ensure the validity and reliability of the power figures.

How do power figures help in interpreting graph data?

Power figures provide a quantitative measure of the strength of the relationship between variables, which can help in interpreting and understanding the data. They can also be used to compare different datasets and identify significant patterns or trends that may not be visible from the graph alone.

What are some limitations of using power figures in data analysis?

While power figures can provide valuable insights into relationships between variables, they should be interpreted with caution. They do not necessarily imply causation and may be influenced by outliers or other factors that are not captured in the data. Additionally, different statistical methods may yield different power figures, so it is important to use them in conjunction with other analytical techniques.

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