基于深度迁移学习的变工况气体泄漏检测
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TP391 TH49

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无锡市社会发展科技示范工程项目(20191008)资助


Gas leak detection for variable conditions based on deep transfer learning
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    摘要:

    压力容器气体泄漏智能检测识别技术易受多种因素干扰,且智能检测模型需要大量的监测数据训练。 而在实际工业环 境中,可用数据特别是数据标签十分稀缺,为了克服多工况干扰和数据缺少标签信息等问题,提出了一种利用迁移学习的无监 督变工况智能检测技术。 首先采集实验室环境下的多种泄漏的样本,选择 3 种不同压力工况下将数据分为有标签的源域和无 标签的目标域;其次设计卷积特征提取器,针对两个域的边缘分布和条件分布,提出一种改进的联合分布适应机制,并进一步改 进了分布差异度量,以增强邻域混淆。 在 6 个迁移学习任务上的实验结果验证了该方法的有效性,对比经典域自适应算法有更 高的准确率。

    Abstract:

    Pressure vessel gas leakage intelligent detection and identification techniques are susceptible to interference from a variety of factors, and intelligent detection models require a large amount of monitoring data training. In the actual industrial environment, available data, especially data labels, are very scarce. To address the problems such as interference from multiple working conditions and lack of labeling information of data, this article proposes an unsupervised variable working condition intelligent detection technique by using transfer learning. Firstly, samples of multiple leaks are collected in laboratory environment and select three different pressure working conditions to divide the data into labeled source domain and unlabeled target domain. Secondly, a convolutional feature extractor is designed to propose an improved joint distribution adaptation mechanism for the edge distribution and conditional distribution of the two domains, and further improve the distribution difference metric to enhance the neighborhood confusion. Experimental results on six transfer learning tasks validate the effectiveness of the method, with higher accuracy than the classical domain adaptive algorithm.

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张立豪,李 鹏,刘轩宇.基于深度迁移学习的变工况气体泄漏检测[J].仪器仪表学报,2023,44(6):177-187

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  • 在线发布日期: 2023-09-20
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