{"title":"Dictionary domain adaptation transformer for cross-machine fault diagnosis of rolling bearings","authors":"","doi":"10.1016/j.engappai.2024.109261","DOIUrl":null,"url":null,"abstract":"<div><p>Domain adaptation (DA) techniques have significantly promoted the fault diagnosis of rolling bearings by leveraging diagnostic knowledge from a labeled source domain to recognize faults in an unlabeled target domain. However, dominant DA models often suffer from inaccurate estimation of distribution discrepancies. This stems from the fact that they perform domain alignment on a batch-by-batch basis, where the distribution discrepancies are evaluated solely using mini-batch data. In this paper, a novel dictionary domain adaptation transformer (DDAT) is proposed to boost cross-machine fault diagnosis of rolling bearings. First, a feature dictionary is constructed to represent domain attributes using multi-batch data, enabling more accurate estimation of the domain gap compared to existing batch-based methods. Second, a novel dictionary adaptation framework is designed to direct the model focus on inter-domain discrepancy instead of intra-domain variations caused by random sampling in data batches. Third, a domain-shared transformer feature extractor is developed to learn domain-invariant representations by leveraging the inherent advantages of multi-head attention in capturing long-range dependencies. The proposed DDAT method conducts domain adaptation at the dictionary level, benefiting from a more accurate estimation of distribution discrepancies by leveraging the abundant and diverse data in the dictionary. Experiments confirm that the proposed DDAT method outperforms the popular deep domain adaptation models in various cross-machine diagnosis tasks of rolling bearings.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197624014192","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Domain adaptation (DA) techniques have significantly promoted the fault diagnosis of rolling bearings by leveraging diagnostic knowledge from a labeled source domain to recognize faults in an unlabeled target domain. However, dominant DA models often suffer from inaccurate estimation of distribution discrepancies. This stems from the fact that they perform domain alignment on a batch-by-batch basis, where the distribution discrepancies are evaluated solely using mini-batch data. In this paper, a novel dictionary domain adaptation transformer (DDAT) is proposed to boost cross-machine fault diagnosis of rolling bearings. First, a feature dictionary is constructed to represent domain attributes using multi-batch data, enabling more accurate estimation of the domain gap compared to existing batch-based methods. Second, a novel dictionary adaptation framework is designed to direct the model focus on inter-domain discrepancy instead of intra-domain variations caused by random sampling in data batches. Third, a domain-shared transformer feature extractor is developed to learn domain-invariant representations by leveraging the inherent advantages of multi-head attention in capturing long-range dependencies. The proposed DDAT method conducts domain adaptation at the dictionary level, benefiting from a more accurate estimation of distribution discrepancies by leveraging the abundant and diverse data in the dictionary. Experiments confirm that the proposed DDAT method outperforms the popular deep domain adaptation models in various cross-machine diagnosis tasks of rolling bearings.
期刊介绍:
Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.