Mechanical pump small sample fault diagnosis method based on WGAN-GP-C and metric learning

Abstract

The invention discloses a mechanical pump small sample fault diagnosis method based on WGAN-GP-C and metric learning, which is based on one-dimensional convolution and time series data design and comprises four parts of data preprocessing, data enhancement, metric learning and model optimization. The data preprocessing realizes data self-adaptive denoising and standardization; in the data enhancement part, network and structure modification is carried out based on WGAN-GP to obtain WGAN-GP-C, so that data expansion according to categories is realized, and edge boundary information of the data is enhanced; a metric network combines a residual idea and a spatial adaptive structure to realize feature mapping, and then a KNN algorithm is utilized to realize state classification; in the model optimization part, the network is optimized by using a weight quantization idea, the KNN is realized by using a Ball-tree, and training data are deleted according to important factors, so that the algorithm performance is improved on the whole. The method has good performance under the condition of rare data, is high in practical value, and can provide thoughts for related workers maintaining mechanical pumps.

Description

technical field [0001] The invention relates to a mechanical pump fault diagnosis method, in particular to a mechanical pump small-sample fault diagnosis method based on WGAN-GP-C and metric learning. Background technique [0002] With the development of science and technology, the structure of ship equipment is becoming more and more complex, the functions are becoming more and more perfect, the degree of automation is getting higher and higher, and the complexity of mechanical equipment is also increasing exponentially. Therefore, when a part of the mechanical equipment fails, it may lead to the failure of the entire system. This failure may not only cause serious economic losses, but may even cause casualties. Therefore, it is urgent to develop and improve the intelligent diagnosis technology that can effectively diagnose the faults of mechanical equipment. [0003] Intelligent diagnosis technology is the combination of fault diagnosis technology and network transmission...

AI Technical Summary

Problems solved by technology

These diagnostic models usually have good results in specific scenarios, but the establishment of most models requires more data, and the data scale has a huge impact on some of them, especially based on deep learning and neural networks and model structures. The impact of the amount of data is exceptionally obvious

But in reality, the fault data of mechanical equipment such as mechanical pumps is very scarce and may be partially missing, which makes it difficult to support an effective training of a common algorithm. The small sample data situation increases the difficulty of data analysis, making it difficult for the algorithm to obtain ideal As a result, the fault diagnosis faces difficulties such as low accuracy rate, false positives and false positives.

[0007] The design of mechanical fault diagnosis algorithms for small sample backgrounds is a hot research topic nowadays. Many strategies have emerged in recent years, but most of them still have problems such as single adaptation to the scene and defects in algorithm performance. This is also the significance of continuing research.

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