Physical meaning of autocorrelation

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SUMMARY

The discussion centers on the physical meaning of autocorrelation in vibration-test data analysis. The autocorrelation function (ACF) indicates the correlation of acceleration values over time delays (tau). ACF that does not decay quickly suggests significant periodic elements in the signal, indicating less randomness and better predictability of future values. The presence of low-frequency components in the data may also indicate detector drift, impacting the interpretation of the results.

PREREQUISITES
  • Understanding of autocorrelation functions in signal processing
  • Familiarity with vibration-test data analysis techniques
  • Knowledge of deterministic versus stochastic systems
  • Experience with dynamic light scattering and interpreting autocorrelation curves
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  • Explore the mathematical foundations of autocorrelation functions
  • Learn about the impact of noise frequency on signal analysis
  • Investigate methods for detecting and correcting for detector drift in vibration data
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Researchers and engineers involved in signal processing, vibration analysis, and those interpreting autocorrelation functions in physical systems.

jollage
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Hi All,

I was in a process of processing my vibration-test data. I now generated a plot of the autocorrelation function of the object acceleration. Please see the attachements (the second attachment is the close-up for small tau's).

The x-axis in the plot is the time delay tau. You can see from the second attachment that, at tau=0,the autocorrelation reaches its maximum.

My question is, as for the signal like this, in the long time delay (tau=60s, 70s), the autocorrelation still does not decay too much. What could this behavior imply? In terms of the noise frequency, what can we say?

Any comments will be welcome. Thanks in advance.

Mz
 

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The autocorrelation function gives the amount of correlation of values of the original function at times delayed by ##\tau##. If the autocorrelation function vanishes quickly, it means that the value of the function at some time cannot be predicted with success from neighboring values.

If it does not vanish quickly, it means that the behavior of the function is less random; from the value at time 0 one can estimate possible values at future times with better success. For example, if the function is periodic, after one wave one can make reasonable estimate what the next values will be, and the autocorrelation function comes out also periodic. But if the function is position of Brownian particle, the next values are hard to predict successfully, and also the autocorrelation function decays very fast (exponentially).
 
If the autocorrelation does not die away then there must be some significant periodic elements in the signalbecause the delayed signal correlates well with an earlier version of itself.
 
jollage said:
Hi All,

I was in a process of processing my vibration-test data. I now generated a plot of the autocorrelation function of the object acceleration. Please see the attachements (the second attachment is the close-up for small tau's).

Any comments will be welcome. Thanks in advance.

Mz

A few comments:

I think of the autocorrelation as a measure of how deterministic the system is- a perfectly deterministic system will always have a normalized autocrrelation of '1', while a perfectly random system will have have an autocorrelation that looks like a delta function. For real systems with dissipation present, the autocorrelation function will generally follow a decaying exponential, and the width of the exponential is a measure of the (de-)correlation time (or length, if we extend the analysis to cover spatially random systems such as reflection from a rough surface). Multiple dissipation processes result in multi-exponential autocorrelation functions- the dynamic light scattering field is a good place to get familiar with interpreting autocorrelation curves.

Without knowing any details about what generated the data (what is vibrating? How was it excited?) it's tough to parse your data. However, when we see a low-frequency component like your first graph has, it's due to drift in our detector.
 

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