New fault diagnosis method for rotating machinery based on deep confrontation convolutional neural network

Abstract

The invention discloses a new fault diagnosis method for a rotating machine based on a deep confrontation convolutional neural network. The method comprises the following steps: constructing a source domain sample data set and a target domain sample data set; constructing a deep adversarial convolutional neural network for identifying known faults and new faults, wherein the deep adversarial convolutional neural network comprises a feature extractor G, a label classifier CF, a domain discriminator D and a non-adversarial domain discriminator; in the training stage, data of a source domain and a target domain are mapped into a high-dimensional feature space through a feature extraction module, and data feature distribution is obtained; a weighted discrimination mechanism is designed, the similarity between target domain sample data and source domain data is evaluated, and the mobility of the data is discriminated; and inputting target domain test data into the trained network for testing, judging whether the data belongs to a new fault category or not through a calculated weight value, and outputting a final classification diagnosis result. Through weighted adversarial training and target domain test sample weight threshold selection, the constructed network is enabled to be suitable for known fault and new fault detection under variable working conditions.

Description

technical field [0001] The invention belongs to the field of intelligent fault diagnosis of rotating machinery, and in particular relates to a new fault diagnosis method for rotating machinery based on a deep anti-convolutional neural network. Background technique [0002] With the continuous development of modern industrial technology and the modernization level of mechanical equipment, rotating machinery tends to be more and more complex, large-scale, high-performance, high-efficiency and high-automation development. However, the parts of the equipment will inevitably be damaged during operation, which will lead to equipment failure, disrupt the production rhythm at least, and cause safety accidents at worst. In order to ensure the stable and efficient operation of industrial equipment and grasp the operating status of equipment in real time, mechanical intelligent fault diagnosis technology is playing an increasingly important role. Mechanical equipment health monitoring ...

AI Technical Summary

Problems solved by technology

However, this method can only diagnose the known fault types in the source domain data, lacks the ability to identify new faults, and cannot meet the diagnostic requirements

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