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A popular approach for conditional monitoring of mechanical machines is to embed vibration sensors into a machine and start "listening" to it. The data from the sensors must be stored somewhere. So, the more efficient we compress them, the longer history we can keep. When we deal with sensors we always have noise from them, and when a machine is stopped we have only noise which wastes our storage space. WaveletBuffer was developed to solve this problem by using the wavelet transformation and efficient compression of denoised data. However, a user must know many settings to use it efficiently. In this tutorial, you will learn how to find denoising parameters to get rid of white noise in your data.
For an educational purpose, I brought two samples from a vibration sensor which is maintained on a real machine. You can find them in the directory docs/tutorials. buffer_signal.bin contains a second of the signal when the machine is working. buffer_no_signal.bin has only noise because the machine is stopped. Both files are serialized buffer without any denoising, so they have equal sizes but different amount of the information!
We need to separate white noise and information, so we need the sample with the working machine. Let's deserialize it:
Output:
You can see that the signal has about 48000 points and the size of the file is about 200kB. Let's have a look at the signal inside:
Looks noisy, right? When we've restored the original signal, we can use WaveletBuffer to denoise it. We use some random threshold for the demonstration. Later we learn how to find the optimal parameter. When we pass denoise.Threshold(a=0, b=0.04) in WaveletBuffer.decompose method it set to 0 all values in hi-frequency subbands which less than a*x+b where x is a decomposition step of the current subband.
The denoised signal looks the same, but we actually don't know if we removed only the noise, or we removed some part of the signal. To figure out it, we can take the difference between the denoised and original signals and check if it has some information or only noise. It has some information and means we deleted with denoising.
Continue reading here
Originally published at driftpythonclient.readthedocs.io
