Aiming at fault detection in industrial processes with nonlinear or high dimensions, a novel method based on locally linear embedding preserve neighborhood for fault detection is proposed in this paper. Locally linear embedding preserve neighborhood is a feature-mapping method that combines Locally linear embedding and Laplacian eigenmaps algorithms. First, two weight matrices are obtained by the Locally linear embedding and Laplacian eigenmaps, respectively. Subsequently, the two weight matrices are combined by a balance factor to obtain the objective function. Locally linear embedding preserve neighborhood method can effectively maintain the characteristics of data in high-dimensional space. The purpose of dimension reduction is to map the high-dimensional data to low-dimensional space by optimizing the objective function. Process monitoring is performed by constructing T2 and Q statistics. To demonstrate its effectiveness and superiority, the proposed locally linear embedding preserve neighborhood for fault detection method is tested under the Swiss Roll dataset and an industrial case study. Compared with traditional fault detection methods, the proposed method in this paper effectively improves the detection rate and reduces the false alarm rate.

中文翻译：

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一种工业过程监控的非线性方法

针对非线性或高维工业过程中的故障检测，提出了一种基于局部线性嵌入保留邻域的故障检测新方法。局部线性嵌入保留邻域是一种结合局部线性嵌入和拉普拉斯特征图算法的特征映射方法。首先，分别通过局部线性嵌入和拉普拉斯特征图获得两个权重矩阵。随后，两个权重矩阵通过一个平衡因子组合得到目标函数。局部线性嵌入保留邻域方法可以有效地保持数据在高维空间的特征。降维的目的是通过优化目标函数，将高维数据映射到低维空间。过程监控是通过构建 T2 和 Q 统计量来执行的。为了证明其有效性和优越性，在 Swiss Roll 数据集和工业案例研究下测试了所提出的局部线性嵌入保留邻域的故障检测方法。与传统的故障检测方法相比，本文提出的方法有效地提高了检测率，降低了误报率。