Research On Information Extraction Of Weak Characteristics And Fault Diagnosis Of Planetary Gear Train Under Strong Noise Background

The large rotating machinery like large power shearer usually works in low speed,heavy load and strong noise background environment.It results that the vibration signals are submerged in the strong noise background and the signal to noise ratio is very low in the process of the fault diagnosis.So,it seriously affects the accuracy of diagnosis.Furthermore,for the low speed level planetary gear in the large power shearer rocker arm transmission system,it has lower fault frequency.When the fault occurs,the useful characteristic information appears even weaker.Therefore,how to improve the signal to noise ratio of the weak fault signal under extreme conditions has become one of the key issues in the field of fault diagnosis.So,this paper makes the planetary gear train as the research object and does research on weak feature extraction and fault diagnosis under strong noise background.Details are as follows:1)The dynamic characteristics of the planetary gear train with normal and fault condition are compared and analyzed.In the comparison and analysis,UG software is used to build the three-dimensional model of the two stage planetary gear train and ADAMS software is used to analyze and study the dynamic characteristics of the two stage planetary gear train.The analysis results show that the impact phenomenon is generated in the time domain waveform and the amplitude of the side band increases in the frequency domain for the fault state.Meanwhile,the more serious the fault is,the more obvious the phenomenon is.2)An adaptive bistable stochastic resonance method is proposed based on the bistable stochastic resonance theory.In the new method,the frequency-shifted and re-scaling method is used to preprocess the large parameter signal,which makes it meet the requirements of stochastic resonance.In adittion,to seek the global optimal value,the improved fish swarm algorithm is used to optimize the system parameters simultaneously.In the process of optimization,the improved signal-to-noise ratio is set as the optimization goal.In order to verify the validity of the new method,the cosine simulation signal and dynamics simulation data of the planetary gear train is used.The analysis results show that the method proposed can transfer the noise energy to the weak characteristic signal,and improve the ISNR.Moreover,it is superior to the EEMD and the wavelet threshold denoising method.3)In order to study the effect of the potential function of stochastic resonance on the system output and find a more efficient way to extract the weak feature,an adaptive multistable stochastic resonance method is proposed based on the multistable stochastic resonance theory.Then,the cosine simulation signal and dynamics simulation data of the planetary gear train is also used to verify the validity of the new method.The analysis results show that the multistable system is superior to the bistable system,and it is more helpful to the extraction of weak characteristic information.4)The experimental data is used to verify the effectiveness of the two methods.The data collected under the mine with the normal condition is used to determine the working environment.Failure data collected in the laboratory is used to verify the proposed methods.The analysis results show that the proposed methods have certain ability in engineering application.However,for extremely low signal to noise ratio environment,the multistable system has a stronger ability.5)The summarizations of the research and expectation of the related technology development are presented at the end of this thesis.