Rolling bearing fault diagnosis method and system based on particle swarm optimization and medium

Abstract

The invention relates to a rolling bearing fault diagnosis method and system based on particle swarm optimization, and a medium. The method comprises: obtaining an initial vibration signal of a rolling bearing in the operation process, and obtaining an initialization parameter of the initial vibration signal by adopting a particle swarm optimization method; performing variation mode decompositionon the initial vibration signal according to the initialization parameters to obtain a plurality of intrinsic mode components; selecting one inherent mode component containing most fault characteristic information from the plurality of inherent mode components as a most sensitive mode component, and carrying out band-pass filtering processing on the most sensitive mode component to obtain a faultcharacteristic vibration signal; and analyzing the fault characteristic vibration signal, extracting the fault characteristic information, and identifying the fault according to the fault characteristic information. The fault diagnosis accuracy and the fault identification precision of the rolling bearing can be effectively improved, the method can be widely applied to the technical field of signal fault diagnosis, and normal operation of mechanical equipment is guaranteed.

Description

Technical field [0001] The present invention relates to the technical field of signal fault diagnosis, in particular to a method, system and medium for fault diagnosis of rolling bearings based on particle swarm optimization. Background technique [0002] Rolling bearings are an important part of rotating machinery and are widely used in various types of machinery and equipment. Therefore, its operating status directly affects the operating status of the entire mechanical equipment. In order to ensure the normal operation of mechanical equipment and reduce the downtime and major economic losses caused by bearing failures, it is very important to conduct state monitoring and fault diagnosis of rolling bearings. Traditional signal processing methods are not suitable for non-stationary and non-linear vibration signals, and changes in the frequency domain cannot be changed in time with changes in the time domain, so that it is difficult to effectively extract feature information and...

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Problems solved by technology

[0004] However, since the vibration signals collected at industrial sites are not only complex in composition but also mixed with a large amount of background noise, the fault characteristic information is often covered up, and it is difficult to obtain good accurate diagnosis results by using VMD alone. Some studies, such as Wang Fei et al. used spectral kurtosis and variational mode decomposition method to solve the fault of rotor misalignment, Ma Zengqiang et al. used variational mode decomposition and spectral kurtosis method to realize the extraction of the characteristic frequency of the faulty bearing , Tang Guiji used VMD and spectral kurtosis methods to detect and diagnose the early faults of bearings, Yi Cancan used the optimized VMD method to extract the characteristic frequency of bearing outer ring faults, An Xueli used VMD and penetration Fault Diagnosis of Fan Bearings Using Entropy Method

The characteristics of the fault information extracted by the above method are not very obvious, the results of fault diagnosis and the accuracy of fault identification need to be improved, and there is no fault diagnosis that can combine particle swarm optimization, variational mode decomposition and spectral kurtosis. method

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