Research On Fault Diagnosis For Rolling Bearing Based On Improved Resonance Demodulation

Rolling bearings are the most common part of rotating machinery and equipment.For the running parts of locomotives,the normal operation of rolling bearings plays a decisive role in the overall safety of the locomotive.In order to reduce the occurrence of accidents and reduce the major economic losses,the research on fault diagnosis and monitoring technology of rolling bearings has always been an important issue in the field of mechanical equipment fault diagnosis.In view of this,this article has carried out a series of research on rolling bearing as the research object.The main research content is as follows:Firstly,based on theoretical analysis and engineering application,the background,significance of the research and the historical development of the technology are expounded.The research status and development trend of the rolling bearing are expounded comprehensively,and the various faults of the rolling bearing are also discussed.The type,vibration mechanism,and various diagnostic methods are briefly introduced in the application of bearing fault feature extraction.At the same time,it also lists the typical types of bearing faults and the calculation method of the characteristic frequency.Secondly,aiming at the low signal to noise ratio of original vibration signal of rolling bearings and the difficulty of determining the parameter selection of bandpass filter in traditional resonance demodulation and relying on the subjective experience of people,this paper proposes a combination of local mean decomposition and fast spectral schlieren algorithm.The method to improve the traditional resonance demodulation.The method firstly decomposes the vibration signal into multiple components by local mean decomposition algorithm,then obtains the time-frequency diagrams of each component through Hilbert transform,and then uses the fast spectral clincher algorithm to automatically determine the center of the band-pass filter.Frequency and bandwidth,the final signal band-pass filtering and envelope demodulation for fault diagnosis.Digital simulation signal and rolling bearing measured data experiments prove the effectiveness of the method.Thirdly,after reading relevant data,we have a deeper understanding of the real-time system and understand the significance of its research.This project has designed a real-time bearing fault diagnosis platform based on Windows using LabVIEW RT to build a program and develop programs for various types of faults.Bearings were tested to verify the reliability of the real-time fault diagnosis system.This experimental system not only has a clear and beautiful,easy-to-use interface,but also has good performance,smooth operation,and easy expansion.It can effectively complete the real-time monitoring and diagnosis of rolling bearing faults.In the end,the traditional vibration signal analysis is mostly off-line processing,and there is a problem of large system size and poor real-time performance.An embedded fault diagnosis system based on ARM Cortex-M0 core embedded W7500 P chip is designed.The system uses the embedded hardware platform to integrate the signal acquisition,processing,and transmission into an embedded hardware platform for execution,enabling real-time online fault diagnosis.Finally,according to the improvement process of the resonance demodulation algorithm proposed in this paper,the improved MATLAB algorithm program is transplanted into the embedded system for the extraction of bearing fault features.The experimental results show that the application of this method to this embedded system can quickly and effectively complete the fault feature extraction and achieve accurate diagnosis of fault diagnosis.