Research And Application Of Fault Diagnosis Method For Rolling Bearing Based On Modal Decomposition

With the development of industrial modernization,rotary machinery is widely used in the field of industrial production.The stable operation of the rotating machinery can effectively ensure the economic benefits and avoid catastrophic accidents.Rolling bearings in rotating machinery are one of the essential core components.For this purpose,research on rolling bearing faults and fault diagnosis methods can ensure efficient,stable and safe operation of rotating machinery systems.In this paper,the rolling bearing is taken as the research object,and the modal decomposition theory is combined with the Compact-RIO technology to study the method.At the same time,the research method is validated and applied in the newly developed system.Firstly,the failure mechanism of rolling bearing is studied to determine the fault impact characteristic expression,and then the modulation signal characteristics of the faulty rolling bearing are highlighted,and the characteristics of the impact period are extracted as the research standard.Taking this standard as the research content,we try to find such signal features from the complex rolling bearing modulation vibration signal,and extract effective fault signal components according to this feature.Secondly,this paper elaborates on the process of building a complete acquisition and analysis experimental platform using Compact-RIO technology.This includes the acquisition hardware selection and the software development environment based on the selected hardware structure.Because this paper takes rolling bearing as the research object,the development background of Compact-RIO technology.Therefore,according to the functional requirements,this paper will correlate the Compact-RIONI9012 chassis,NI9234 vibration signal acquisition card and LabVIEW2014 and Real-Tiem modules.Its purpose is to lay the foundation for the development of rolling bearing fault feature extraction system.Then,based on the working characteristics of the rolling bearing in a strong noise environment.In order to effectively solve the problem of serious false components caused by the traditional static adaptive decomposition method,this paper will carry out research and optimization through two schemes.The first is to explore the application of graphical programming to achieve dynamic EMD signal decomposition,by combining the advantages of graphical programming and signal dynamic decomposition display process to visually determine the fault component.The second is to optimize the K-value of the non-recursive decomposition method VMD,and then propose the optimization VMD decomposition combined with the MED filtering function to extract the feature of the fault signal.Based on this,a method of applying MED to filter and optimize the modal component filtering is proposed.Finally,through the laboratory failure test bench and other related equipment,after a number of experiments,the experimental test of the inner ring fault and rolling element failure of the rolling bearing was completed.Decomposition and feature extraction of fault rolling bearing vibration signals are carried out by different modal decomposition methods.The experimental results show that the system constructed by combining different modal decomposition methods and Compact-RIO technology is stable and reliable,and has good applicability.