Fine-grained multi-class imbalance fault classification method based on knowledge distillation

Abstract

The invention discloses a fine-grained multi-class imbalance fault classification method based on knowledge distillation. The method combines knowledge distillation, hierarchical clustering and other algorithms and is used for solving the problem of multi-class unbalanced fault classification. For a multi-category fault classification problem, firstly, fault classification is carried out by using a knowledge distillation method; aiming at the characteristics of similarity between homogeneous class samples and large difference between heterogeneous class samples in the imbalance problem, a hierarchical clustering method is adopted to cluster all class samples according to a clustering result of class center points so as to obtain a fine-grained cluster. And finally, fine-grained fault classification is performed on each cluster. And for each cluster, a student network is established, finally splicing is performed, and the multiple student networks are optimized together. And under the guidance of teacher network global information, fault classification is carried out in combination with multi-student network fine grit. Compared with other existing methods, the method provided by the invention has good effect and applicability.

Description

technical field [0001] The invention belongs to the field of industrial process monitoring, and in particular relates to a fine-grained multi-category unbalanced fault classification method based on knowledge distillation. Background technique [0002] In machine learning or deep learning classification, the imbalance of the number of class samples is a very common problem, which exists in various fields, such as bioinformatics, smart grid, medical imaging, fault diagnosis. Most existing classification methods are based on the assumption that the underlying distribution of observed data is relatively balanced. However, real industrial datasets usually violate this assumption and present a skewed distribution or even an extremely unbalanced distribution of class sample sizes. For example, data-driven fault classification is an important part of industrial process monitoring, which exhibit unbalanced skewed distributions due to different frequencies of fault occurrence. In t...

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Problems solved by technology

However, real industrial datasets often violate this assumption and exhibit skewed distributions or even extremely unbalanced distributions of class sample sizes

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