Gearbox Bearing Outer Race Fault Testing (DRS)

Gearbox Bearing Outer Race Fault Testing (DRS) is a diagnostic method designed to assess the health condition of gearbox bearings, specifically targeting outer race fault detection. This testing approach effectively identifies characteristic frequencies of bearing outer race faults by analyzing specific frequency components in vibration signals combined with envelope analysis techniques.

### Implementation Approach Simulation Signal Validation: In the demo program, simulated signals are first generated to replicate vibration characteristics of bearing outer race faults, validating algorithm effectiveness. The simulation signals typically include periodic impulse components that mimic vibration patterns caused by bearing damage. The MATLAB implementation uses functions like `sin` or `pulse` generators to create these synthetic signals with controlled fault frequencies such as BPFO (Ball Pass Frequency Outer Race). Actual Signal Analysis: Real vibration signals collected from gearboxes are processed using envelope analysis to extract high-frequency resonance components and demodulate fault characteristic frequencies. The code typically involves signal preprocessing (filtering), Hilbert transform for envelope extraction, and FFT analysis to identify dominant frequencies in the envelope spectrum. Fault Frequency Localization: Envelope spectrum analysis effectively suppresses noise interference and highlights characteristic frequencies of bearing outer race faults (e.g., BPFO), enabling engineers to quickly assess bearing health conditions. The algorithm implementation often includes frequency-domain peak detection algorithms to automate fault frequency identification.

### Technical Advantages Efficiency: Automated analysis through MATLAB scripts minimizes manual intervention and improves diagnostic efficiency. Key functions like `envelope` and `fft` are optimized for batch processing of vibration data. Accuracy: Envelope analysis technology effectively extracts weak fault signals and enhances the discernibility of characteristic frequencies. The implementation includes signal enhancement techniques like band-pass filtering around bearing resonance frequencies. Practicality: Validated through real gearbox signal testing, making it suitable for equipment condition monitoring in industrial settings. The code is designed with modular functions for easy integration with existing monitoring systems.

The successful application of this method extends beyond gearbox bearing outer race fault detection and can be adapted for diagnosing other bearing fault types such as inner race and rolling element defects through corresponding characteristic frequency adjustments in the algorithm.