Rolling element bearings are regarded as vital in many rotating types of machinery. These are essential parts of any machine because most malfunctions stem from rotating parts. Their present status may be tracked by the condition monitoring system, which can also forecast their future state. This paper makes use of signal processing techniques like time domain, Fast Fourier Transform (FFT), time–frequency domain analysis (Spectrogram), and envelope analysis (Kurtogram). The bearing vibration datasets are chosen from open-source Case Western Reserve University (CWRU) bearing vibration data, which contains data for healthy bearings, inner race faults, ball faults, and outer race faults with defect sizes of 0.007, 0.014, and 0.021 inches. Signal processing techniques may have a variety of distinctive features or patterns that could indicate a bearing fault which would be challenging to identify through manual observation. For this reason, this paper focuses on the time domain, frequency domain, spectrogram, and envelope analysis for the detection of rotating component faults and aims to analyze the vibration behavior generated by a defective and healthy bearing. The time domain indicators revealed in this study indicated the presence of a fault, with the impulse factor being the most prominent. Moreover, it was observed that the FFT spectrum indicates both the strength and location of the faults and the spectrogram visually depicts the strength of a signal over time at different frequencies. Finally, the envelope analysis using a kurtogram was done to assess the strength, range, and location of the fault.