Seperation of Vessels and Sea Dynamics

I have these sets of rolling data on a boat. I want to determine the natural frequency of the boat from the heave and roll motions. My ultimate goal is to identify the frequency that is directly related to the boat’s motion and separate it from other sea dynamics such as waves and wind. I am a bit new to data analysis, any help will be greatly appreciated.


Simplifying the Problem : Imagine the boat is like a playground swing. If you push it a little, it swings back and forth at a certain speed, that’s its natural swinging frequency. Boats also have a natural way of rocking back and forth (heave) and tilting side to side (roll) depending on their size and shape.

You have data that shows how much the boat heaved and rolled, kind of like how fast the swing goes back and forth. What you want to find is the boat’s own natural rocking speed, separate from the bumps caused by waves (like someone pushing the swing) or wind gusts (like a little kid jumping on the swing).

This plot displays the frequency components of the roll and heave data. Peaks in the plot represent dominant frequencies. To further analyze these results, we can identify the specific peaks and filter around these frequencies to isolate the boat’s natural motion from other sea dynamics.
The peak frequencies identified from the FFT analysis for both roll and heave motions are as follows:

  • Roll Peak Frequencies (Hz): Ranges from very low frequencies up to approximately 5 Hz, with numerous peaks throughout this range.
  • Heave Peak Frequencies (Hz): Also spans from very low frequencies up to about 5 Hz, with multiple peaks detected.

Given the broad range of frequencies and numerous peaks, it’s important to focus on the most significant peaks that are likely related to the boat’s natural motion. We can apply a band-pass filter around these significant peaks to isolate the boat’s motion from other sea dynamics.

Hope you get the answer!


Thanks for sharing Mahmed. Your tip about simplifying the problem is really helpful!

Many thanks @rahul for reading

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