{"title":"Crack growth degradation-based diagnosis and design of high pressure liquefied natural gas pipe via designable data-augmented anomaly detection","authors":"Dabin Yang, Sanghoon Lee, Jongsoo Lee","doi":"10.1093/jcde/qwad065","DOIUrl":null,"url":null,"abstract":"\n A new approach to anomaly detection termed “anomaly detection with designable generative adversarial network (Ano-DGAN)” is proposed, which is a series connection of a designable generative adversarial network and anomaly detection with a generative adversarial network. The proposed Ano-DGAN, based on a deep neural network, overcomes the limitations of abnormal data collection when performing anomaly detection. In addition, it can perform statistical diagnosis by identifying the healthy range of each design variable without a massive amount of initial data. A model was constructed to simulate a high-pressure liquefied natural gas pipeline for data collection and the determination of the critical design variables. The simulation model was validated and compared with the failure mode and effect analysis of a real pipeline, which showed that stress was concentrated in the weld joints of the branch pipe. A crack-growth degradation factor was applied to the weld, and anomaly detection was performed. The performance of the proposed model was highly accurate compared with that of other anomaly detection models, such as support vector machine (SVM), one-dimensional convolutional neural network (1D CNN), and long short term memory (LSTM). The results provided a statistical estimate of the design variable ranges and were validated statistically, indicating that the diagnosis was acceptable.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"15 1","pages":"1531-1546"},"PeriodicalIF":4.8000,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Design and Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1093/jcde/qwad065","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
A new approach to anomaly detection termed “anomaly detection with designable generative adversarial network (Ano-DGAN)” is proposed, which is a series connection of a designable generative adversarial network and anomaly detection with a generative adversarial network. The proposed Ano-DGAN, based on a deep neural network, overcomes the limitations of abnormal data collection when performing anomaly detection. In addition, it can perform statistical diagnosis by identifying the healthy range of each design variable without a massive amount of initial data. A model was constructed to simulate a high-pressure liquefied natural gas pipeline for data collection and the determination of the critical design variables. The simulation model was validated and compared with the failure mode and effect analysis of a real pipeline, which showed that stress was concentrated in the weld joints of the branch pipe. A crack-growth degradation factor was applied to the weld, and anomaly detection was performed. The performance of the proposed model was highly accurate compared with that of other anomaly detection models, such as support vector machine (SVM), one-dimensional convolutional neural network (1D CNN), and long short term memory (LSTM). The results provided a statistical estimate of the design variable ranges and were validated statistically, indicating that the diagnosis was acceptable.
期刊介绍:
Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering:
• Theory and its progress in computational advancement for design and engineering
• Development of computational framework to support large scale design and engineering
• Interaction issues among human, designed artifacts, and systems
• Knowledge-intensive technologies for intelligent and sustainable systems
• Emerging technology and convergence of technology fields presented with convincing design examples
• Educational issues for academia, practitioners, and future generation
• Proposal on new research directions as well as survey and retrospectives on mature field.