Research On Health Monitoring Of Vertical Shaft Guide Rail System

As the guide device of the conveyance,guide rail system is an important component of the vertical shaft hoisting equipment,whose function is to restrict the transverse displacement of the conveyance during lifting process as well as to ensure safe and stable operation of the conveyance along the shaft direction.Due to the key position in the mine transportation,the failure of guide rail system will not only excite abnormal vibrations to the hoisting system,the chain reactions will even lead to dangerous situations which threaten the safe production of entire mine.Traditional breakdown maintenance and scheduled maintenance methods generally cause high cost,low efficiency and poor real-time performance,and the strategies are susceptible to subjective factors.Therefore,it is necessary to perform health monitoring for guide rail system,including fault detection,fault diagnosis and remaining useful life prediction of the key components,so as to dynamically and reasonably arrange maintenance operations and ensure the safety and reliability of guide rail system.Vertical shaft guide rail system consists of two parts: guide rail and guide roller.This dissertation concentrates on the health monitoring approach for these two parts through combining the theories of signal processing,feature extraction,pattern recognition and life prediction,so as to propose a vibration signal analysis-based approach including fault detection and diagnosis method for guide rail as well as fault diagnosis and remaining useful life prediction method for guide roller.These research results can provide theoretical supports and technical solutions for ensuring the safe operation of guide rail system.Firstly,the fault characteristic and its impact factors of guide rail are analyzed in detail.On the one hand,the relationship between the vibration characteristic of conveyance and the fault mode of guide rail is studied by mapping the fault response to the lifting process,which lays a foundation for the follow-up fault diagnosis of guide rail.On the other hand,the vibration responses of conveyance to the guide rail faults under different hoisting ropes are compared and the influences of lifting speed,lifting load and fault severity on the vibration responses of conveyance are studied,which provides a basis for the follow-up fault detection of guide rail.Secondly,the study on fault detection and diagnosis of guide rail is carried out.Aiming at the vibration response of conveyance to the guide rail fault,a method based on scale-averaged wavelet power is proposed,and the distribution of fault-related energy with lifting process is obtained,as a result the interference of random noise is weakened.The health monitoring threshold is adaptively selected by Tukey's control chart,which eliminates the influence of working condition on fault detection effect.Through analyzing the vibration characteristics under different guide rail faults,a fault diagnosis method for guide rail based on dynamic time warping is proposed,which removes the influence of lifting parameters and achieves effective diagnosis of guide rail fault under different working conditions.Finally,the study on fault diagnosis and remaining useful life prediction of guide roller is carried out.In order to make full use of the fault vibration data of guide roller and reduce the fluctuation of fault features at different operation times,a feature extraction method based on random average algorithm is proposed.Combining ensemble empirical mode decomposition and entropy theory,the improved permutation entropy is used as the fault feature to represent fault information of guide roller.Given the advantages in classification,support vector machine is applied to realize effective fault diagnosis of guide roller.Based on the analysis of the frequency spectrum evolution during the whole life cycle of guide roller,the power value on sensitive frequency band is constructed as degradation index.Aiming at the fluctuations caused by noise and random disturbance,the degradation curve is fitted using the generalized Weibull failure rate function to reach strict agreement with the degradation process of the guide roller.The remaining useful life of the guide roller is accurately predicted by serving the RBF neural network as prediction model,the fitted power values as inputs,and the life percentage as output.There are 122 figures,23 tables and 225 references in the dissertation.