Multivariate time series generation based on dual-channel Transformer conditional GAN for industrial remaining useful life prediction

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhizheng Zhang, Hui Gao, Wenxu Sun, Wen Song, Qiqiang Li
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Abstract

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.
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
剩余使用寿命(RUL)预测是预测性维护的关键因素。虽然基于深度学习的预测方法取得了长足进步,但有限的运行到故障数据导致的数据不平衡问题严重影响了这些方法的性能。最近的一些研究采用生成式对抗网络(GAN)来解决这一问题。然而,大多数基于 GAN 的生成方法都难以同时提取不同时间步长和传感器的相关性。本文提出了一种新型多变量时间序列(MTS)生成框架--双通道变换器条件 GAN(DCTC-GAN),以生成高质量的 MTS,从而增强基于深度学习的 RUL 预测模型。我们设计了一种新颖的双通道 Transformer 架构来构建生成器和判别器,它由并行工作的时间编码器和空间编码器组成,可自动对不同的时间步骤和传感器给予不同的关注。在此基础上,DCTC-GAN 可以直接提取不同时间步长的时间关系,同时捕捉不同传感器的空间相关性,从而合成高质量的 MTS 数据。在广泛使用的涡轮风扇发动机数据集和 FEMTO 轴承数据集上进行的实验分析表明,我们的 DCTC-GAN 在不改变现有深度学习模型结构的情况下,显著提高了其在 RUL 预测方面的性能,并超越了当前代表性生成方法的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
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.
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