VMD-SSAE-based rolling bearing fault diagnosis classification method, system and device and storage medium thereof

Abstract

The invention provides a VMD-SSAE-based rolling bearing fault diagnosis classification method. The method comprises the steps of collecting vibration signals of rolling bearings of different fault types; performing time domain, frequency domain and time-frequency domain feature extraction on the vibration signal based on variational mode decomposition; forming a data set by the features, and dividing the data set into a training set, a verification set and a test set; connecting a stack sparse auto-encoder with a Softmax classifier, constructing a VMD-SSAE classification model, and using the training set to train the VMD-SSAE classification model; adopting a grey wolf optimization algorithm and an error back propagation algorithm to optimize the VMD-SSAE classification model, and obtaining an ideal VMD-SSAE classification model; and inputting data in the test set into the ideal VMD-SSAE classification model to obtain a diagnosis classification result. According to the VMD-SSAE-based rolling bearing fault diagnosis classification method provided by the invention, the fault type of the rolling bearing can be diagnosed more accurately.

Description

technical field [0001] The present invention relates to the technical field of rolling bearing fault diagnosis and classification, in particular to a VMD-SSAE-based rolling bearing fault diagnosis and classification method, system, device and storage medium thereof. Background technique [0002] Rolling bearings are widely used in the industrial field and are one of the key components that are easily damaged in rotating machinery. They are prone to fatigue and failures in high-speed, heavy-load and long-term harsh working environments, which will affect the safe and stable operation of the entire equipment. When serious failures occur It will cause huge social and economic losses and even casualties. However, traditional equipment health monitoring requires a lot of manpower and material resources, and it is difficult to achieve real-time monitoring. Therefore, real-time status monitoring and fault diagnosis of rolling bearings are of great significance. [0003] Vibration ...

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Problems solved by technology

[0006] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide a rolling bearing fault diagnosis classification method, system, equipment and storage medium based on VMD-SSAE, which are used to solve the time-varying non-stationary problems of rolling bearings in the prior art. It is difficult to quickly and accurately judge the type of bearing fault under the load and different amount of bearing test data

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