Vibration Monitoring System in Industrial Machinery Using Fast Fourier Transform

This Paper analyses the abnormal vibration patterns in industrial machinery to verify the machine has fault or not. Import the data into MATLAB in column vectors and perform the processes in Signal Processing Toolbox. Create a workspace for each vector, then plot the signals in time domain and to determine the exact fault location. Each of the elements will generate many fault frequencies in the frequency domain based on its characteristics which allowing quick and easy identification. The four possible bearing failing frequencies are: FTF (Fundamental Train Frequency), BPFO (Ball Pass Frequency Outer), BSF (Ball Spin Frequency), BPFI (Ball Pass Frequency Inner) and other overall machine condition faults are Misalignment, Unbalance and Looseness, frequency domain is required. It is used to show each fault location of the faulty machine. This system used to view all the amplitudes of the time waveform which are shown separately. Regarding to Fourier analysis, any vibration signals can be separated into several discrete frequencies or a spectrum of frequencies over a continuous range. Fast Fourier Transform is a system which is used to convert the signals in time domain into the Frequency domain signals. Compute a power spectrum and determine threshold to filter out noise. In this paper we use 1000 RPM, 50 Hz, 415V, 6-pole, 2HP, 1.5KW Induction Motor with 6205 ball bearing. Compare the measured signal with the signals obtained by calculations to determine the fault components.