Fault Diagnosis for Rolling Bearings

This article introduces how to utilize rolling bearing fault diagnosis to identify characteristic frequencies for defect analysis in rolling bearings. During the operation of rolling bearings, various faults such as wear, cracks, and spalling may occur due to multiple factors. To enhance the reliability and safety of rolling bearings, fault diagnosis and analysis have become increasingly critical. The article covers the fundamental principles and procedures of rolling bearing fault diagnosis, along with methodologies for extracting characteristic frequencies through signal processing techniques. Key implementation aspects include vibration signal acquisition using accelerometers, frequency domain transformation through Fast Fourier Transform (FFT) algorithms, and characteristic frequency calculation based on bearing geometry parameters. The diagnostic process typically involves envelope analysis for demodulating high-frequency resonance signals and machine learning classifiers for fault pattern recognition. Through studying this article, readers can gain a comprehensive understanding of the principles and methods behind rolling bearing fault diagnosis, providing practical references for real-world applications. The content demonstrates how to implement bearing defect identification algorithms using spectral analysis techniques and time-domain feature extraction methods.