Research On Fault Diagnosis Technology Of Large Slewing Bearings Based On Stochastic Resonance

As an important part of significant mechanical equipment manufacturing,large slewing bearings are widely used in various industries of the national economy,such as port machinery,mining equipment,large amusement facilities,metallurgical machinery,etc.In terms of structure and working space,large slewing bearings are different from the original bearings with large size,low running rate roughly between 0.1r/m~10r/m.Besides,the load on it is also complicated,including not only axial and radial forces but also large overturning moments.Due to the particularity of structure and the complexity of working conditions,large slewing bearing is one of the fragile parts in rotating machinery.Whether the running state of large slewing bearings is good or not directly affects the working efficiency of the whole mechanical equipment.Even in severe cases,it will face long-term halting problem.Therefore,in order to reduce the economic losses caused by the halting problem and man-machine safety problems,it is particularly essential to fully master the running mechanism of the large slewing bearings and realize its running state monitoring and early warning.So far,there were few methods for fault diagnosis of large slewing bearings.As a result,the research has become a newly hot topics in comprehensive edged subject.In the view of the importance of the working environment and health maintenance of large slewing bearings above,a method of condition monitoring and fault diagnosis based on stochastic resonance system is proposed in this paper.The main contents are as follows:(1)The research contents,background and significance of this task are discussed.The fault diagnosis methods for parts of large slewing bearings are comprehensively summarized and analyzed.And the data analysis methods,in this paper,using vibration as signal source are described in detail.The monitoring sites of large slewing bearings via temperature,pressure,nondestructive detection and oil detection are so briefly mentioned,on the basis of which it weighted advantages and disadvantages of each method.(2)Some relevant theories and performance impact of between the driving signal feature and intensity of noise toward stochastic resonance system are discussed.The fault diagnosis method based on EEMD and BSR,where the EEMD algorithm is used as the pre-denoisingprocess of BSR system,is proposed and applied to extract the weak impact signal components under the strong noise background successfully.(3)To avoid the human factors for adjusting SR system parameters,an adaptive stochastic resonance(ASR)method is presented.The system regards a novel characteristic parameter——weighted negentropy value as the performance indicator of SR.This indicator can not only prevent signal distortion but also sensitively detect the impact property of periodical impact signal.The gray wolf algorithm with appropriate global optimization is selected as the optimization measure to achieve the purpose of correlation optimization among parameters.The simulation and experimental results show that this method can effectively detect the fault type of the large slewing bearings with a broad application prospects for the condition monitoring of low-frequency impact signal in engineering.(4)The structure of large slewing bearings and the calculation of failure frequency are discussed.And the acquisition of vibration signal is also carried out.Finally,a state monitoring and fault diagnosis system of large slewing bearings is exploited by LabVIEW software.This system mainly includes threes modules of data acquisition,data storage and data analysis,which are practical and easy to operate in line with users' operating habits.