Mechanical equipment intelligent fault diagnosis method based on partial migration convolutional network

Abstract

The invention provides a mechanical equipment intelligent fault diagnosis method based on a partial migration convolutional network. The method comprises: collecting operation data of mechanical equipment under different operation working conditions; constituting data sets, taking part of data in the data set X as a source domain training sample set and a target domain test sample set; performingdata standardization on each piece of sample data; training two one-dimensional convolutional neural network models with the same structure and different initialization parameters by using the sourcedomain training sample set, and correcting the two trained convolutional neural network models based on the target domain test sample set to obtain a convolutional neural network mechanical equipmentfault diagnosis model; and performing fault diagnosis on the mechanical equipment based on the real-time operation data by using the fault diagnosis model to output a fault type. The method can be effectively used in more real mechanical fault diagnosis, that is to say, the label-free property of the target domain is considered, so that the trained diagnosis model can better diagnose faults of mechanical equipment.

Description

technical field [0001] The invention relates to the technical field of mechanical fault diagnosis, in particular to an intelligent fault diagnosis method for mechanical equipment based on a partial transfer convolutional network. Background technique [0002] With the rapid development of society, mechanical equipment has become an important pillar of modern industry. However, due to continuous operation and environmental disturbances, mechanical equipment will inevitably degrade and fail. Once a failure occurs, it will cause equipment downtime and economic loss at least, and casualties at worst. Therefore, health monitoring and fault diagnosis of mechanical equipment have attracted more and more attention in the industry and academia. [0003] Over the past many years, various diagnostic methods have been proposed. Among them, the deep neural network, as a dazzling new star, has been gradually studied more and more. At present, the intelligent diagnosis method based on th...

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Problems solved by technology

[0004] In recent years, although methods based on deep neural networks have made some achievements, the above three problems are usually ignored and cannot be solved well.

The existence of these problems will not only cause misdiagnosis and missed diagnosis, but also make the accuracy of fault diagnosis lower, and may even hinder the development of intelligent diagnosis.

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