Abstract
gear transmission system is a complex non-stationary and nonlinear time-varying coupling system. When faults occur on gear system, it is difficult to extract the fault feature. This paper researches the threshold principle in the process of using the wavelet transform to de-noise the system, and combines EMD (empirical mode decomposition) with wavelet threshold de-noising to solve the problem. The wavelet threshold de-noising is acts on the high-frequency IMF (Intrinsic Mode Function) component of the signal, and does overcome the defect by simply highlighting the fault feature. On this basis, the pre-processed signal is analyzed by the method of time-frequency analysis to extract the feature of the signal. The result shows that the SNR (signal-noise ratio) of the signal was largely improved, and the fault feature of the signal can therefore be effectively extracted. Combined with time-frequency analyses in the different running conditions (300 rpm, 900 rpm), various faults such as tooth root crack, tooth wear and multi-fault can be identified effectively. Based on this theory and combining the merits of MATLAB and VC++, a multi-functional gear fault diagnosis software system is successfully exploited.
Highlights
As a complex flexibility system, a gear transmission system will vibrate in internal and external incentives
Due to the influence of the nonlinear time-varying characteristics of gear mesh stiffness and the nonlinear stiffness of a supporting system, such as a bearing or gearbox, a complex coupling nonlinear vibration will occur in the system [1, 2]
This paper proposes a gear feature extraction and fault diagnosis method, which combines the time-frequency analysis with the wavelet threshold de-noising based on empirical mode decomposition (EMD)
Summary
As a complex flexibility system, a gear transmission system will vibrate in internal and external incentives. This paper proposes a gear feature extraction and fault diagnosis method, which combines the time-frequency analysis with the wavelet threshold de-noising based on EMD. Signals will be decomposed into six levels by EMD (combining the high frequency component de-noised by wavelet threshold with the low frequency component without noise) to successfully reconstruct the original signal, and give the reconstructed signal a time frequency analysis to identify the different faults of the gear system under different running conditions. Thereby, it provides a reliable and effective method for dynamic property identification and damage detection of a complex mechanical system. Using VC++ to design the main interface and achieve sub-interface calls, the software system of gear fault analysis and diagnosis is developed
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
