Changdong Wang , Jingli Yang , Huamin Jie , Bowen Tian , Zhenyu Zhao , Yongqi Chang
{"title":"An uncertainty perception metric network for machinery fault diagnosis under limited noisy source domain and scarce noisy unknown domain","authors":"Changdong Wang , Jingli Yang , Huamin Jie , Bowen Tian , Zhenyu Zhao , Yongqi Chang","doi":"10.1016/j.aei.2024.102682","DOIUrl":null,"url":null,"abstract":"<div><p>Deep learning has made notable advances in intelligent fault diagnosis. However, industrial application of deep learning models faces challenges due to noise interference and scarce labeled samples. Targeting the above problems, this paper proposes a metric network-based diagnostic method. For the problem of noise, a multi-scale cross feature extraction module (MSCM) is constructed to mine key classification information under noise interference to improve fault identifiability. Different from the current approach to metric learning, this paper models the uncertainty of the similarity between ‘query sample-class prototype’, and develops corresponding loss function for more effective perception, thereby better improving the fault recognition ability of the model under limited noisy source domain and scarce noisy unknown domain. Meanwhile, to visualize the decision-making process of the model under uncertainty and improve interpretability, this paper develops a novel colony-based class activation mapping (Colony-CAM) tool, which is more reliable and focused. The proposed method is compared with five baselines across three datasets. It achieved leading diagnostic accuracies of 98.55% and 94.33% with 70 and 40 noisy training samples, respectively.</p></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":null,"pages":null},"PeriodicalIF":8.0000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Engineering Informatics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1474034624003306","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Deep learning has made notable advances in intelligent fault diagnosis. However, industrial application of deep learning models faces challenges due to noise interference and scarce labeled samples. Targeting the above problems, this paper proposes a metric network-based diagnostic method. For the problem of noise, a multi-scale cross feature extraction module (MSCM) is constructed to mine key classification information under noise interference to improve fault identifiability. Different from the current approach to metric learning, this paper models the uncertainty of the similarity between ‘query sample-class prototype’, and develops corresponding loss function for more effective perception, thereby better improving the fault recognition ability of the model under limited noisy source domain and scarce noisy unknown domain. Meanwhile, to visualize the decision-making process of the model under uncertainty and improve interpretability, this paper develops a novel colony-based class activation mapping (Colony-CAM) tool, which is more reliable and focused. The proposed method is compared with five baselines across three datasets. It achieved leading diagnostic accuracies of 98.55% and 94.33% with 70 and 40 noisy training samples, respectively.
期刊介绍:
Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.