Gearbox Fault Detection Using Real Coded Genetic Algorithm and Novel Shock Response Spectrum Features Extraction

This paper presents an intelligent fault detection method for gearbox. The method uses band-pass and wavelet filtering with real coded genetic algorithm (RCGA) and shock response spectrum (SRS) for features extraction. Vibration data acquired from gearbox are adaptively filtered through a band-pass and wavelet filters optimized by the RCGA. The filtering process unveils the fault pulses buried under huge background noise. Shock response spectrum is used to calculate the amount of shock produced by these pulses over a frequency band of interest for features extraction. The proposed method is a combination of intelligent and conventional search techniques, which shows a high performance and accurate fault detection results. The effectiveness, feasibility, and robustness of the proposed method are demonstrated on experimental data. The RCGA has successfully achieved an average speed up factor of 74 %, as compared to conventional genetic algorithms (GA) while preserving the quality of results.