A STATE OF THE ART REVIEW ON ADVANCES IN BEARING FAULT DIAGNOSIS USING MACHINE LEARNING APPROACH - Patel, Premal; Keval Bhavsar; Parmar, Umang; Bhatt, Pina M. (ShodhKosh: Journal of Visual and Performing Arts, 2023)
Diagnosing bearing issues is crucial because bearings are crucial parts of rotating machinery, supporting and guiding shafts, and because faults can result in lost productivity, damaged equipment, and safety hazards. One common technique for identifying bearing problems is vibration analysis. High-frequency resonances are isolated using sophisticated techniques like envelope analysis, fault frequencies are determined using frequency-domain techniques like the Fast Fourier Transform (FFT), and anomalies in signals are identified using time-domain analysis. Machine learning improves fault classification, while time-frequency methods such as wavelet transformations are employed to handle non-stationary signals. Every technique has its limitations: sophisticated techniques offer precision at the expense of complexity; frequency analysis performs well in steady settings but suffers from speed variations; time-domain analysis is straightforward but may reveal early issues. Accuracy, computational demands, and operational requirements must all be balanced when choosing the best strategy for bearing condition monitoring.