IF 6.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Yuwen You, Yuan Zhao, Yan Ke, Junhao Tang, Bin Yang
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引用次数: 0

摘要

精确的冷水机故障诊断对于延长设备使用寿命和降低能耗至关重要。目前,数据驱动的冷风机诊断模型表现出令人印象深刻的性能。然而,只有在充足且高质量的数据条件下才能保证出色的性能,也就是说,数据往往是不确定和不充分的。为解决这一问题,本研究提出了一种基于红外热成像(IRT)和运行状态参数的新型多模态协同学习框架。状态参数被称为证据源 1,在由基础分类器进行分类之前,先使用条件瓦瑟斯坦生成式对抗网络(CWGAN)进行数据增强。被称为证据源 2 的 IRT 通过一种称为具有梯度惩罚(SA-BAGAN-GP)的自注意 BAGAN 方法进行增强。编码器层中集成了自注意机制,以捕捉关键特征,生成高质量样本。然后,利用自注意卷积神经网络(SA-CNN)模型对生成的 IRT 样本进行分类。最后,Dempster-Shafer(D-S)证据理论被用于融合两种模式的决策信息。通过同时捕捉和整合来自不同来源的数据,该模型提高了通用性和鲁棒性。在实际冷却器上进行的实验验证表明,四项交叉条件任务的平均准确率为 92.75%,噪声测试准确率在 89.2% 到 99.6% 之间,离群值测试准确率在 98.5% 到 99.4% 之间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel solution for data uncertainty and insufficient in data-driven chiller fault diagnosis based on multi-modal data fusion
Accurate fault diagnosis of chillers is essential for extending equipment lifespan and reducing energy consumption. Currently, data-driven diagnostic models for chillers exhibit impressive performance. However, the outstanding performance is only guaranteed in condition of sufficient and high-quality data, i.e., data is often uncertain and insufficient. To resolve this problem, this study proposes a novel multimodal co-learning framework based on infrared thermography (IRT) and operational state parameters. State parameters, referred as evidence source 1, undergo data augmentation using conditional Wasserstein generative adversarial networks (CWGAN) before classification by a base classifier. IRTs referred as evidence source 2, are enhanced through a method called self-attention BAGAN with gradient penalty (SA-BAGAN-GP). Self-attention mechanisms is integrated in the encoder layer to capture critical features to produce high-quality samples. Then, generated IRT samples are then classified using the self-attention convolutional neural network (SA-CNN) model. Finally, Dempster-Shafer (D-S) evidence theory is utilized for the fusion of decision information from both modalities. By simultaneously capturing and integrating data from diverse sources, the model improves generalization and robustness. Experimental validation conducted on actual chillers demonstrated an average accuracy of 92.75% across four cross-condition tasks, with noise test accuracy ranging from 89.2% to 99.6% and outlier test accuracy between 98.5% and 99.4%.
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来源期刊
Energy and Buildings
Energy and Buildings 工程技术-工程:土木
CiteScore
12.70
自引率
11.90%
发文量
863
审稿时长
38 days
期刊介绍: An international journal devoted to investigations of energy use and efficiency in buildings Energy and Buildings is an international journal publishing articles with explicit links to energy use in buildings. The aim is to present new research results, and new proven practice aimed at reducing the energy needs of a building and improving indoor environment quality.
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