Zhizheng Zhang, Hui Gao, Wenxu Sun, Wen Song, Qiqiang Li
{"title":"基于双通道变压器条件 GAN 的多变量时间序列生成,用于工业剩余使用寿命预测","authors":"Zhizheng Zhang, Hui Gao, Wenxu Sun, Wen Song, Qiqiang Li","doi":"10.1016/j.knosys.2024.112749","DOIUrl":null,"url":null,"abstract":"<div><div>Remaining useful life (RUL) prediction is a key enabler of predictive maintenance. While deep learning based prediction methods have made great progress, the data imbalance issue caused by limited run-to-failure data severely undermines their performance. Some recent works employ generative adversarial network (GAN) to tackle this issue. However, most GAN-based generative methods have difficulties in simultaneously extracting correlations of different time steps and sensors. In this paper, we propose dual-channel Transformer conditional GAN (DCTC-GAN), a novel multivariate time series (MTS) generation framework, to generate high-quality MTS to enhance deep learning based RUL prediction models. We design a novel dual-channel Transformer architecture to construct the generator and discriminator, which consists of a temporal encoder and a spatial encoder that work in parallel to automatically pay different attention to different time steps and sensors. Based on this, DCTC-GAN can directly extract the long-distance temporal relations of different time steps while capturing the spatial correlations of different sensors to synthesize high-quality MTS data. Experimental analysis on widely used turbofan engine dataset and FEMTO bearing dataset demonstrates that our DCTC-GAN significantly enhances the performance of existing deep learning models for RUL prediction, without changing its structure, and exceeds the capabilities of current representative generative methods.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"308 ","pages":"Article 112749"},"PeriodicalIF":7.2000,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multivariate time series generation based on dual-channel Transformer conditional GAN for industrial remaining useful life prediction\",\"authors\":\"Zhizheng Zhang, Hui Gao, Wenxu Sun, Wen Song, Qiqiang Li\",\"doi\":\"10.1016/j.knosys.2024.112749\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Remaining useful life (RUL) prediction is a key enabler of predictive maintenance. While deep learning based prediction methods have made great progress, the data imbalance issue caused by limited run-to-failure data severely undermines their performance. Some recent works employ generative adversarial network (GAN) to tackle this issue. However, most GAN-based generative methods have difficulties in simultaneously extracting correlations of different time steps and sensors. In this paper, we propose dual-channel Transformer conditional GAN (DCTC-GAN), a novel multivariate time series (MTS) generation framework, to generate high-quality MTS to enhance deep learning based RUL prediction models. We design a novel dual-channel Transformer architecture to construct the generator and discriminator, which consists of a temporal encoder and a spatial encoder that work in parallel to automatically pay different attention to different time steps and sensors. Based on this, DCTC-GAN can directly extract the long-distance temporal relations of different time steps while capturing the spatial correlations of different sensors to synthesize high-quality MTS data. Experimental analysis on widely used turbofan engine dataset and FEMTO bearing dataset demonstrates that our DCTC-GAN significantly enhances the performance of existing deep learning models for RUL prediction, without changing its structure, and exceeds the capabilities of current representative generative methods.</div></div>\",\"PeriodicalId\":49939,\"journal\":{\"name\":\"Knowledge-Based Systems\",\"volume\":\"308 \",\"pages\":\"Article 112749\"},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2024-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knowledge-Based Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0950705124013832\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705124013832","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Multivariate time series generation based on dual-channel Transformer conditional GAN for industrial remaining useful life prediction
Remaining useful life (RUL) prediction is a key enabler of predictive maintenance. While deep learning based prediction methods have made great progress, the data imbalance issue caused by limited run-to-failure data severely undermines their performance. Some recent works employ generative adversarial network (GAN) to tackle this issue. However, most GAN-based generative methods have difficulties in simultaneously extracting correlations of different time steps and sensors. In this paper, we propose dual-channel Transformer conditional GAN (DCTC-GAN), a novel multivariate time series (MTS) generation framework, to generate high-quality MTS to enhance deep learning based RUL prediction models. We design a novel dual-channel Transformer architecture to construct the generator and discriminator, which consists of a temporal encoder and a spatial encoder that work in parallel to automatically pay different attention to different time steps and sensors. Based on this, DCTC-GAN can directly extract the long-distance temporal relations of different time steps while capturing the spatial correlations of different sensors to synthesize high-quality MTS data. Experimental analysis on widely used turbofan engine dataset and FEMTO bearing dataset demonstrates that our DCTC-GAN significantly enhances the performance of existing deep learning models for RUL prediction, without changing its structure, and exceeds the capabilities of current representative generative methods.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.