Cross-category fault diagnosis method and system based on small sample learning, and storage medium

Abstract

The invention discloses a cross-category fault diagnosis method based on small sample learning. The method comprises the following steps: establishing a fault diagnosis model in combination with labeled historical working data of a part A; establishing a support set by combining the labeled historical working data of the part B; obtaining test data of the part B, pairing the test data with the labeled sample data in the support set to form a test sample, judging whether two pieces of data in the test sample belong to the same signal category or not through a fault diagnosis model, and obtaining the labeled sample data which are in the support set and have the same signal category as the test data; and collecting a signal category associated with the labeled sample data as a signal category of the test data. According to the method, the historical working data of the part A is adopted to train the fault diagnosis model, it is guaranteed that sufficient training data are learned in fault diagnosis model training, the part B provides the support set containing a small amount of labeled sample data, and the feasibility of signal category diagnosis on the part B through the fault diagnosis model is guaranteed.

Description

technical field [0001] The present invention relates to the technical field of rolling bearing vibration signal processing, in particular to a small-sample learning-based cross-category fault diagnosis method, system and storage medium. Background technique [0002] The study of advanced mechanical fault diagnosis methods is an important part of ensuring the safety of equipment and personnel. Among them, bearings are one of the most important mechanical parts in rotating machinery. They are widely used in various important fields such as electric power, chemical industry, metallurgy, and aviation. At the same time, bearings are also One of the most easily damaged components, the quality of bearing performance and working conditions will directly affect the performance of the entire machine equipment. Defects in bearing performance and working conditions will cause abnormal vibration and noise of the equipment, and even cause equipment damage. Therefore, it is particularly im...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the above-mentioned defects in the prior art that the fault diagnosis of mechanical parts with difficult data collection is difficult, the present invention proposes a cross-category fault diagnosis method, system and storage medium based on small sample learning

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