Bearing Rolling Element Fault Diagnosis

Bearing rolling element fault diagnosis is a critical component in mechanical equipment health monitoring. By analyzing bearing vibration signals, surface damages on rolling elements such as spalling or cracks can be detected, thereby preventing unexpected equipment failures. In code implementation, this typically involves acquiring vibration data through sensors like accelerometers and processing the signals using libraries such as Python's SciPy or MATLAB's Signal Processing Toolbox.

The core of diagnosis lies in feature extraction from vibration signals and calculation of fault frequencies. Rolling element faults typically generate specific impulse components in vibration signals, with frequencies related to the bearing's geometric parameters and rotational speed. Algorithmically, this requires computing characteristic frequencies using formulas like the Ball Pass Frequency Outer Race (BPFO) or Ball Pass Frequency Inner Race (BPFI), which depend on bearing dimensions and shaft speed.

Common diagnostic procedures include: First, collecting bearing vibration signals and performing preprocessing to remove noise using techniques like band-pass filtering or wavelet denoising. Then, time-frequency analysis methods such as Short-Time Fourier Transform (STFT) or Continuous Wavelet Transform (CWT) are applied for feature extraction, followed by calculation of rolling element fault characteristic frequencies. The frequency computation typically involves bearing geometry parameters (pitch diameter, ball diameter, contact angle) and rotational speed. Finally, spectrum analysis techniques like FFT are used to identify fault characteristic frequencies in the frequency domain, determining whether rolling element damage exists. In programming, this can be implemented using FFT algorithms from libraries like NumPy or MATLAB's fft function.

For more precise diagnosis, envelope analysis techniques can be employed to enhance impulse components, often implemented using Hilbert transform-based envelope detection. Alternatively, machine learning algorithms such as support vector machines (SVM) or convolutional neural networks (CNN) can be integrated for pattern recognition, where feature vectors extracted from vibration signals are used to train classification models. These methods significantly improve the accuracy of bearing fault diagnosis systems.