{"title":"A fault diagnosis method with AT-ICNN based on a hybrid attention mechanism and improved convolutional layers","authors":"","doi":"10.1016/j.apacoust.2024.110191","DOIUrl":null,"url":null,"abstract":"<div><p>Fault diagnosis is crucial for mechanical systems, with early diagnosis of bearings playing a key role in ensuring the overall safety and smooth operation of the mechanical system. However, in real industrial environments, traditional diagnostic methods limit the extraction of fault signals from rotating machinery. This study aims to improve the fault diagnosis method for critical mechanical components and proposes a novel deep learning model, the Attention Improved CNN (AT-ICNN) fault diagnosis method. The method combines Convolutional Neural Network (CNN) and attention mechanism to extract key fault feature information from signals, enhancing the model’s ability to highlight fault features and capture global information. This improves the accuracy of fault type identification. The AT-ICNN model enhances traditional CNN models by introducing Improved Convolutional (IMConv) and integrating a hybrid attention mechanism to effectively extract relevant fault information. Experimental results demonstrate superior diagnostic performance of AT-ICNN on the CWRU bearing dataset and laboratory bearing dataset, with accuracy rates of 98.12% and 98.72%, respectively. This represents about 9% improvement over baseline models and other advanced methods. In-depth analysis of experimental results validates the significant advantages of AT-ICNN in the field of fault diagnosis for critical mechanical components.</p></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Acoustics","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0003682X24003426","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 0
Abstract
Fault diagnosis is crucial for mechanical systems, with early diagnosis of bearings playing a key role in ensuring the overall safety and smooth operation of the mechanical system. However, in real industrial environments, traditional diagnostic methods limit the extraction of fault signals from rotating machinery. This study aims to improve the fault diagnosis method for critical mechanical components and proposes a novel deep learning model, the Attention Improved CNN (AT-ICNN) fault diagnosis method. The method combines Convolutional Neural Network (CNN) and attention mechanism to extract key fault feature information from signals, enhancing the model’s ability to highlight fault features and capture global information. This improves the accuracy of fault type identification. The AT-ICNN model enhances traditional CNN models by introducing Improved Convolutional (IMConv) and integrating a hybrid attention mechanism to effectively extract relevant fault information. Experimental results demonstrate superior diagnostic performance of AT-ICNN on the CWRU bearing dataset and laboratory bearing dataset, with accuracy rates of 98.12% and 98.72%, respectively. This represents about 9% improvement over baseline models and other advanced methods. In-depth analysis of experimental results validates the significant advantages of AT-ICNN in the field of fault diagnosis for critical mechanical components.
期刊介绍:
Since its launch in 1968, Applied Acoustics has been publishing high quality research papers providing state-of-the-art coverage of research findings for engineers and scientists involved in applications of acoustics in the widest sense.
Applied Acoustics looks not only at recent developments in the understanding of acoustics but also at ways of exploiting that understanding. The Journal aims to encourage the exchange of practical experience through publication and in so doing creates a fund of technological information that can be used for solving related problems. The presentation of information in graphical or tabular form is especially encouraged. If a report of a mathematical development is a necessary part of a paper it is important to ensure that it is there only as an integral part of a practical solution to a problem and is supported by data. Applied Acoustics encourages the exchange of practical experience in the following ways: • Complete Papers • Short Technical Notes • Review Articles; and thereby provides a wealth of technological information that can be used to solve related problems.
Manuscripts that address all fields of applications of acoustics ranging from medicine and NDT to the environment and buildings are welcome.