Gear And Bearing Fault Detection Of Planetary Transmission System Based On IAS Signal

Planetary transmission system is one of the commonly used transmission modes in mechanical equipment.As the key components of planetary transmission system,the normal and stable operation of gears and rolling element bearing is the premise of safe and effective operation of mechanical equipment.In engineering practice,the working environment of planetary transmission system is usually harsh and the operating conditions are complex,resulting in local faults such as pitting,wear and shedding of gears and rolling element bearing.If the local fault is not found in time,it may lead to the failure of mechanical equipment,which may cause economic losses and casualties.Therefore,it is of great significance to propose an effective fault feature extraction method to ensure the safe operation of mechanical equipment.Fault feature extraction based on vibration signal is one of the main directions of fault diagnosis research.There are some difficulties in local fault feature extraction of planetary transmission system,such as weak fault information,time-varying transmission path and serious signal coupling.The encoder signal has the advantages of no time-varying transmission path,equal angle sampling,and sampling is not affected by the lower limit of frequency,which can solve the problem of extracting fault features from the above vibration signals to a certain extent.Therefore,the research on fault feature extraction based on encoder signal has practical theoretical significance and potential practical application prospects.The optimization of filter length by multipoint optimal minimum entropy deconvolution adjusted technique depends on engineering experience,a sun gear fault detection method based on improved adaptive multi-point optimal minimum entropy deconvolution is proposed.Firstly,based on the advantages of short signal transmission path of encoder and direct correlation with dynamics,combined with transmission parameters,the fault characteristic period is calculated,and the fault cycle search interval and step length are determined.In order to improve the performance of the algorithm,the principle of maximizing spectral negative entropy is proposed as the fitness function,and the length of the multi-point optimal minimum entropy deconvolution optimization filter is adaptively determined,which effectively extracts the fault characteristics of the sun gear root crack of the planetary transmission system.Aiming at the shortcomings of optimizing the selection of demodulation frequency band in the fault extraction of rolling bearing outer ring,a cyclic spectrum correlation optimization demodulation frequency band selection algorithm based on encoder signal diagnostic feature index is proposed.Firstly,the instantaneous angular speed signal of the rolling bearing is obtained by using the forward difference method to estimate the encoder signal,and the bivariate spectrum of the instantaneous angular speed signal is obtained by using the fast spectral correlation analysis.Then,the bivariate spectrum is divided according to the given initial sub-band bandwidth,and the improved envelope spectrum of each sub-band is calculated respectively,and the improved envelope spectrum diagnostic characteristic index of each sub-band is calculated to obtain the diagnostic characteristic curve.Then,the optimized demodulation frequency band is obtained by judging the diagnostic index value and combining the sub-bands.Finally,the fault feature order of rolling bearing is extracted by envelope analysis.Through the analysis of simulation signals and experimental data,the effectiveness of the proposed method is verified,which provides a reference for solving the fault diagnosis of gears and rolling element bearing in vibration detection limited occasions.