Rotary machinery fault diagnosis method based on zero trial learning and feature extraction

Abstract

The invention discloses a rotating machinery fault diagnosis method based on zero trial learning and feature extraction, which belongs to the field of deep learning and fault diagnosis, and comprises a feature refining module which mainly solves the problem of cross-dataset deviation existing in most existing methods and integrates semantic visual mapping into a unified generative model, so that the fault diagnosis efficiency is improved. In order to refine visual features of visible and invisible class samples, adaptive edge center loss is introduced to explicitly encourage intra-class compactness and inter-class separability, and the adaptive edge center loss is combined with semantic cycle consistency constraints, so that a feature refinement module can learn more distinct feature representations related to classes and semantics, and the robustness of the features is improved. According to the method, the problem of cross-dataset deviation is effectively solved, low efficiency and over-fitting risks of fine tuning are avoided, and the method has remarkable performance gain.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of rotating machinery, in particular to a fault diagnosis method of rotating machinery based on zero-trial learning and feature extraction. 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 diffic...

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

[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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