Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance

Characterized by small size, light weight and large transmission ratio, planetary gear transmission is widely used in large scale complex mechanical system with low speed and heavy duty. However, due to the influences of operating condition, manufacturing error, assembly error and multi-tooth meshing, the vibration signal of planetary gear exhibits the characteristics of nonlinear and non-stationary. Especially when early gear fault occurs, the weak fault feature information is submerged in interfering signal. A weak fault feature information extraction method of planetary gear based on Ensemble Empirical Mode Decomposition (EEMD) and Adaptive Stochastic Resonance (ASR) is proposed. The original signal is decomposed to the Intrinsic Mode Functions (IMFs) with small modal aliasing by EEMD. The Signal to Noise Ratio (SNR) of fault feature frequency information of each IMF is calculated, and the IMFs with first four higher SNR are reconstructed and selected as the effective IMFs containing main fault feature information. ASR system is built by combining Particle Swarm Optimization (PSO) and Stochastic Resonance (SR). PSO algorithm is used to optimize the critical parameters of SR, and SNR of ASR output signal is defined as an optimization objective. When the signal reconstructed by effective IMFs is inputted into ASR system, the weak fault feature information can be extracted from the output signal of ASR system. The experimental results show that the proposed method can extract the weak fault feature information of normal gear and fault gears successfully. The amplitudes of fault feature frequency and its sidebands generated by planetary gear fault have a significantly increase, and the effects on sideband amplitudes of faults become even greater than that on the amplitude of fault feature frequency. For different gear faults, the amplitude of fault feature frequency has different changes, meanwhile different sidebands are produced. Planetary gear fault diagnosis can be achieved accurately by comparing the extracted weak fault feature information, so it is an effective method of weak fault feature information extraction of planetary gear.