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Convolutional neural network with Huffman pooling for handling data with insufficient categories: A novel method for anomaly detection and fault diagnosis

The rotating component is an important part of the modern mechanical equipment, and its health status has a great impact on whether the equipment can safely operate. In recent years, convolutional neural network has been widely used to identify the health status of the rotor system. Previous studies...

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Detalles Bibliográficos
Autores principales: Li, Yuqing, Lei, Mingjia, Cheng, Yao, Wang, Rixin, Xu, Minqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10358713/
https://www.ncbi.nlm.nih.gov/pubmed/36344222
http://dx.doi.org/10.1177/00368504221135457
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author Li, Yuqing
Lei, Mingjia
Cheng, Yao
Wang, Rixin
Xu, Minqiang
author_facet Li, Yuqing
Lei, Mingjia
Cheng, Yao
Wang, Rixin
Xu, Minqiang
author_sort Li, Yuqing
collection PubMed
description The rotating component is an important part of the modern mechanical equipment, and its health status has a great impact on whether the equipment can safely operate. In recent years, convolutional neural network has been widely used to identify the health status of the rotor system. Previous studies are mostly based on the premise that training set and testing set have the same categories. However, because the actual operating conditions of mechanical equipment are complex and changeable, the real diagnostic tasks usually have greater diversity than the pre-acquired datasets. The inconsistency of the categories of training set and testing set makes it easy for convolutional neural network to identify the unknown fault data as normal data, which is very fatal to equipment health management. To overcome the above problem, this article proposes a new method, Huffman-convolutional neural network, to improve the generalization ability of the model in detection task with various operating conditions. First, a new Huffman pooling kernel is designed according to the Huffman coding principle and the Huffman pooling layer structure is introduced in the convolutional neural network to enhance the model's ability to extract common features of data under different conditions. Second, a new objective function is proposed based on softmax loss, intra-class loss, and inter-class loss to improve the Huffman-convolutional neural network's ability to distinguish different classes of data and aggregate the same class of data. Third, the proposed method is tested on three different datasets to verify the generalization ability of the Huffman-convolutional neural network in diagnosis tasks with multi-operating conditions. Compared with other traditional methods, the proposed method has better performance and greater potential in multi-condition fault diagnosis and anomaly detection tasks with inconsistent class spaces.
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spelling pubmed-103587132023-08-09 Convolutional neural network with Huffman pooling for handling data with insufficient categories: A novel method for anomaly detection and fault diagnosis Li, Yuqing Lei, Mingjia Cheng, Yao Wang, Rixin Xu, Minqiang Sci Prog Original Article The rotating component is an important part of the modern mechanical equipment, and its health status has a great impact on whether the equipment can safely operate. In recent years, convolutional neural network has been widely used to identify the health status of the rotor system. Previous studies are mostly based on the premise that training set and testing set have the same categories. However, because the actual operating conditions of mechanical equipment are complex and changeable, the real diagnostic tasks usually have greater diversity than the pre-acquired datasets. The inconsistency of the categories of training set and testing set makes it easy for convolutional neural network to identify the unknown fault data as normal data, which is very fatal to equipment health management. To overcome the above problem, this article proposes a new method, Huffman-convolutional neural network, to improve the generalization ability of the model in detection task with various operating conditions. First, a new Huffman pooling kernel is designed according to the Huffman coding principle and the Huffman pooling layer structure is introduced in the convolutional neural network to enhance the model's ability to extract common features of data under different conditions. Second, a new objective function is proposed based on softmax loss, intra-class loss, and inter-class loss to improve the Huffman-convolutional neural network's ability to distinguish different classes of data and aggregate the same class of data. Third, the proposed method is tested on three different datasets to verify the generalization ability of the Huffman-convolutional neural network in diagnosis tasks with multi-operating conditions. Compared with other traditional methods, the proposed method has better performance and greater potential in multi-condition fault diagnosis and anomaly detection tasks with inconsistent class spaces. SAGE Publications 2022-11-07 /pmc/articles/PMC10358713/ /pubmed/36344222 http://dx.doi.org/10.1177/00368504221135457 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Article
Li, Yuqing
Lei, Mingjia
Cheng, Yao
Wang, Rixin
Xu, Minqiang
Convolutional neural network with Huffman pooling for handling data with insufficient categories: A novel method for anomaly detection and fault diagnosis
title Convolutional neural network with Huffman pooling for handling data with insufficient categories: A novel method for anomaly detection and fault diagnosis
title_full Convolutional neural network with Huffman pooling for handling data with insufficient categories: A novel method for anomaly detection and fault diagnosis
title_fullStr Convolutional neural network with Huffman pooling for handling data with insufficient categories: A novel method for anomaly detection and fault diagnosis
title_full_unstemmed Convolutional neural network with Huffman pooling for handling data with insufficient categories: A novel method for anomaly detection and fault diagnosis
title_short Convolutional neural network with Huffman pooling for handling data with insufficient categories: A novel method for anomaly detection and fault diagnosis
title_sort convolutional neural network with huffman pooling for handling data with insufficient categories: a novel method for anomaly detection and fault diagnosis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10358713/
https://www.ncbi.nlm.nih.gov/pubmed/36344222
http://dx.doi.org/10.1177/00368504221135457
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