A Fault Classification Method for Vibration Data Based on Depth Domain Adaptation

Abstract

The invention discloses a vibration data fault classification method based on depth domain self-adaption, which includes: constructing a source domain containing a large amount of sample data and a target domain containing a small amount of sample data; using the sample data in the source domain to construct a classifier; Construct paired samples from sample data in different fields in the source domain and the target domain; construct a twin network, input the paired samples as training samples into the twin network for domain adaptation, and obtain the final loss function of the paired samples; The final loss function is optimized, and the fault classification model after training is obtained. The invention solves the problem of poor diagnostic effect of the existing deep network model under the condition of insufficient fault sample data, combines the domain self-adaptive method in deep learning and transfer learning, maximizes the use of existing data, and improves the accuracy of the model generalization ability, resulting in better classification accuracy.

Description

technical field [0001] The invention relates to the technical field of vibration signal processing and fault classification, in particular to a vibration data fault classification method based on depth field self-adaptation. Background technique [0002] Major mechanical equipment such as wind power generation equipment, aeroengines, and high-end CNC machine tools are developing in the direction of large-scale, complex, high-speed and high-precision equipment. Once an accident occurs in the equipment, it will bring huge economic losses and casualties. Therefore, it is of great significance to study advanced mechanical fault diagnosis methods to ensure the safe operation of equipment. [0003] Traditional bearing fault diagnosis methods can usually be divided into two parts: feature extraction and classifier classification. Among them, feature extraction based on signal processing technology is often aimed at specific problems, requiring diagnostic experts to deeply understa...

AI Technical Summary

Problems solved by technology

[0008] In order to overcome the defects in the above-mentioned prior art, the present invention provides a vibration data fault classification method based on depth field self-adaptation, which solves the problem of poor diagnosis effect of the existing deep network model under the condition of insufficient fault sample data. The combination of learning and domain adaptive methods in transfer learning maximizes the use of existing data, improves the generalization ability of the model, and obtains better classification accuracy

Images