Do you have noisy signals and you want to increase the quality of the measurements? What if your machine can produce more via software with no additional costs?
We have successfully implemented a novel signal filter that deviates from traditional industry standards.
Through some MATLAB simulations and a dedicated development period in Automation Studio carried out by Raúl García Serrano, we have created a filter that offers, lower settling time than a low pass and better immunity to noise.
Only for B&R controls!
Do you want to test it? I am eager to see the results!
Nice!
I’ve been struggling with a noisy signal from an accelerometer (B&R X20CM4810) that monitors vibrations. I was hoping to be able to use this sensor to detect when a rotating brush is touching a specific position. As soon as the brush is touching this specific position, I want the brush to stop.
I can’t use a RMS value, since that will result in a slow update and my program will not react quick enough to the signal from the vibration sensor. And I haven’t been able to create my own filter. Unfortunately, it looks like your filtered signal is somewhat delayed compared to the original input signal. So I guess your filter will not solve my problem either.
The theory (in the books+papers, we do not have yet real confirmation in real applications)
points that, it has faster settling time, (no delay) and better rejection to the noise.
We were able to reduce the ripple to a similar low pass filter around 1 order of magnitude (10 times ) and at the same time reduce the settling time almost 50%
The ideal scenario of use of this filter is the following “type” signals.
Blue: Data
Green: LP Filter (has been shifted down to campare better vs the red)
Red: Implemented filter
Do you have noisy signals and you want to increase the quality of the measurements?
What if your machine can produce more via software with no additional costs?
) and at the same time reduce the settling time almost 50%
