Rotary machinery fault diagnosis method based on zero trial learning and potential space coding

Abstract

The invention discloses a rotating machine fault diagnosis method based on zero trial learning and potential space coding, the zero trial learning gets rid of the dependence of traditional deep learning on a large amount of labeled data, fault diagnosis of unknown samples can be realized by using a small amount of labeled data, and a potential space coding model is a new coding-decoding method. According to the method, visual information and semantic information are interacted in a potential space by applying an encoding-decoding thought, and the potential space is shared. According to the method, a large amount of annotation data is not needed for training, and the cost loss of data annotation is greatly reduced. And learning the similarity between unknown class samples in a test set and known class samples in a training set in a potential space to obtain high-level semantic features of the unknown class of the test set to detect unknown samples. According to the method, the detection performance of the network is improved, the parameter optimization process is reduced, and a relatively good fault diagnosis effect is obtained.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of rotating machinery, in particular to a fault diagnosis method for rotating machinery based on zero-trial learning and latent space coding. Background technique [0002] Since entering the 21st century, the use of machinery and equipment has promoted the development of the national economy and brought great convenience to production and life. At present, mechanical equipment is developing towards intelligence and precision. However, due to the correlation between components, a small failure may cause serious consequences, resulting in huge losses of human and material resources. [0003] In the actual environment, mechanical fault diagnosis faces great challenges. Because mechanical equipment works in harsh environments such as high temperature and high pressure, the mechanical load will change, and there are rich data types, but there are few single data types, which increases the diff...

AI Technical Summary

Problems solved by technology

Because mechanical equipment works in harsh environments such as high temperature and high pressure, the mechanical load will change, and the data types are rich, but there are few single data types, which increases the difficulty of fault detection

At the same time, due to the long equipment monitoring period and many monitoring points, massive data will be obtained, but finding useful data from massive data increases the difficulty of mechanical fault diagnosis

In addition, due to the correlation between the various components in the system, a small fault may cause a chain reaction, resulting in the paralysis of the entire equipment and even casualties. Therefore, timely and accurate diagnosis of potential equipment failures has become an important topic in academia and industry

[0004] In response to the scarcity of fault data, researchers have simulated fault samples in the laboratory, but it is difficult to compare simulated fault samples with real data

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