Rolling Bearing Component Fault Diagnosis with Frequency Analysis

Effective diagnosis of rolling bearing component failures requires understanding various fault types including surface distress (pitting/spalling), lubrication issues, and alignment problems. This implementation typically involves calculating characteristic fault frequencies through mathematical models where ball pass frequency (BPF) = (rpm/60) × (ball count/2) × (1 - ball diameter/pitch diameter × cos(contact angle)). The code utilizes signal processing techniques like Fast Fourier Transform (FFT) for frequency domain analysis and envelope detection for impact signature identification. Proper implementation includes time-domain vibration analysis, spectral peak detection algorithms, and automated fault severity assessment through amplitude thresholding. Preventive measures such as lubrication optimization and maintenance scheduling can be integrated through condition monitoring algorithms with real-time alert systems. This comprehensive approach combines theoretical frequency calculations with practical MATLAB implementations using functions like fft(), envelope(), and findpeaks() for accurate fault diagnosis and prevention strategy development.