Rotating machinery fault information processing method, processing system, processing terminal, medium

Abstract

The invention belongs to the technical field of state monitoring and fault diagnosis of rotating machinery, discloses a method for processing fault information of rotating machinery, a processing system, a processing terminal, and a medium, and constructs a neural network model including a deep feature extractor, a domain classifier, and a state predictor , using the deep feature extractor to automatically extract the migration fault features from the laboratory simulation data and the rotating part monitoring data in the actual engineering equipment through the neural network model; using the domain classifier to shorten the difference between the two data distributions, using the state prediction The machine and domain adaptation constraints are introduced to form a fault diagnosis model based on the deep domain adaptive confrontation network, and the model is used to realize the intelligent fault diagnosis of rotating machinery. The invention can accurately extract the characteristics of migration faults in laboratory simulation data and actual engineering data, and form a fault migration diagnosis model that can be applied to rotating parts, and achieve ideal effects through actual case utilization.

Description

technical field [0001] The invention belongs to the technical field of state monitoring and fault diagnosis of rotating machinery, and in particular relates to a method for processing fault information of rotating machinery, a processing system, a processing terminal, and a medium, and in particular to a method for processing fault information of rotating machinery based on a deep domain self-adaptive confrontation network . Background technique [0002] At present, rotating machinery, as one of the important components of large-scale industrial equipment systems, plays a vital role in the stable operation of the entire system. Its rotating parts (such as rolling bearings, gearboxes, etc.) often work in harsh environments, and have large rotational kinetic energy during operation, which is prone to failure, reduces system reliability, reduces system life, and even causes industrial production. huge loss. Therefore, it is very necessary to carry out fault diagnosis on rotat...

AI Technical Summary

Problems solved by technology

However, in practical engineering applications, two problems have been encountered: one is that the machine operates in a normal state for a long time, and the failure time only accounts for a very small part of the entire life cycle, which leads to an extreme imbalance between healthy samples and fault samples; The monitoring data during the long-term operation of machinery often does not contain fault information labels, and manual labeling is usually expensive, which leads to a serious shortage of labeled fault data samples

[0005] (1) Most of the traditional rotating machinery fault diagnosis methods are based on the time-frequency domain analysis of the fault mechanism and the experience and professional knowledge of diagnostic experts. The operation is cumbersome, and it is difficult to meet the current rapid analysis and diagnosis requirements for massive monitoring data.

[0006] (2) In the existing intelligent diagnosis technology for rotating machinery, due to the long-term operation of the machine in a normal state, the failure time only accounts for a very small part of the entire life cycle, resulting in an extreme imbalance between healthy samples and fault samples

[0007] (3) The monitoring data of machinery in the long-term operation process often does not contain fault information labels, while manual labeling is usually expensive, which leads to a serious shortage of labeled fault data samples, which is objectively data-driven intelligent fault diagnosis The application of the method to practical engineering presents challenges

[0008] The difficulty of solving the above problems and defects is as follows: In actual engineering case applications, due to the lack of fault samples and the difficulty of obtaining labeled data, the existing data-driven supervised fault diagnosis methods are difficult to solve during the model training process. It is prone to overfitting phenomenon due to lack of fault data, which leads to a decrease in the diagnostic accuracy of the model in the actual application process, and the generalization performance is seriously insufficient

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