- Load the bearing vibration signal data into MATLAB.
- Preprocess the signal if necessary, such as removing any DC offset or noise using functions like detrend, medfilt1, sgolayfilt.
- Apply a logarithmic transformation to the signal using the log function to compress the dynamic range and emphasize low-frequency components.
- Perform a Fourier transform on the logarithmic transformed signal using the fft function to obtain the frequency spectrum.
- Apply a high-pass filter in the frequency domain using designfilt and filter functions to suppress the low-frequency noise and retain the high-frequency components related to bearing damage.
- Apply an inverse Fourier transform using the ifft function to obtain the filtered signal in the time domain.
- Optionally, post-process the filtered signal by smoothing, thresholding, etc. to enhance the bearing damage features or remove any remaining noise based on the requirements.
- Analyze the filtered signal to detect and characterize the bearing damage.
- https://www.mathworks.com/help/matlab/ref/detrend.html
- https://www.mathworks.com/help/matlab/ref/detrend.html
- https://www.mathworks.com/help/signal/ref/sgolayfilt.html
- https://www.mathworks.com/help/matlab/ref/log.html
- https://www.mathworks.com/help/matlab/ref/fft.html
- https://www.mathworks.com/help/signal/ref/designfilt.html
- https://www.mathworks.com/help/signal/ref/filter.html
- https://www.mathworks.com/help/signal/ref/filter.html