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首页> 外文期刊>IEEE transactions on industrial informatics >Semisupervised Kernel Learning for FDA Model and its Application for Fault Classification in Industrial Processes
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Semisupervised Kernel Learning for FDA Model and its Application for Fault Classification in Industrial Processes

机译：FDA模型的半监督核学习及其在工业过程故障分类中的应用

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摘要

For fault classification in industrial processes, the performance of the classification model highly depends on the size of labeled dataset. Unfortunately, labeling the fault types of data samples need expert experiences and prior knowledge of the process, which is costly and time consuming. As a result, semisupervised modeling with both labeled and unlabeled data have recently become an interest in industrial processes. In this paper, a kernel-driven semisupervised fisher discriminant analysis (FDA) model is proposed for nonlinear fault classification. Two discriminant analytical strategies are introduced for online fault assignment, namely k-nearest neighborhood and Bayesian inference. Detailed comparative studies are carried out through two industrial benchmark processes between the linear and kernel-driven semisupervised FDA models, in which the best fault classification performance is obtained by the kernel semisupervised model with Bayesian inference as its discriminant strategy.
机译：对于工业过程中的故障分类，分类模型的性能高度取决于标记数据集的大小。不幸的是，标记数据样本的故障类型需要专家经验和对过程的先验知识，这既昂贵又费时。结果，近来具有标签和未标签数据的半监督建模已成为工业过程中的关注点。本文提出了一种核驱动的半监督费舍尔判别分析（FDA）模型，用于非线性故障分类。针对在线故障分配引入了两种判别分析策略，即k最近邻和贝叶斯推理。通过线性和内核驱动的半监督FDA模型之间的两个行业基准测试过程，进行了详细的比较研究，其中以贝叶斯推理为判别策略的内核半监督模型获得了最佳的故障分类性能。

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