Filtering the Experimental Data

As the vibration of the motor induce vibration on the sensor, it needs to be taken out of the raw data. This will need a high-pass filter.

To remove the vibration of the motor from the experimental data, I use a low pass filter which has a Matlab function of "function y=lowp(x,f1,f3,rp,rs,Fs)". In which x is the input, f1 is the frequency at the start of the pass band and f3 is the end of the band. rp is the cutoff frequency for the point 3 dB point below the passband value and rs being the cutoff frequency for the point 6 dB point below the passband value. Finally, Fs marks the sampling rate of the filter.

I have also involved a high pass filter to remove the shaking of my hand holding the probe, the function is essentially the same, "function y=highp(x,f1,f3,rp,rs,Fs)".

After applying the filters, the experimental data are plotted.

The first figure is increasing frequency and the second is increasing amplitude. It can be seen in both figures that the the early stages where the change is small, the probe could maintain a relatively constant level of force. It could also be observed that the probe performs better when the change is in frequency.