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A Deep Nonnegative Matrix Factorization Approach via Autoencoder for Nonlinear Fault Detection

机译：AutoEndoder用于非线性故障检测的深度非负矩阵分解方法

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摘要

In the era of big data, data-driven fault detection is vital for modern industrial systems. This article considers the potential complexity of fault detection and proposes a novel nonlinear method based on nonnegative matrix factorization (NMF). Motivated by an autoencoder, in this article we first utilize the input data to learn an appropriate nonlinear mapping function, which transforms the original space into a high-dimensional feature space. Then, according to the decomposition rule of NMF, we divide the learned feature space into two subspaces, and two statistics in these subspaces are designed appropriately for nonlinear fault detection. The established method, i.e., deep nonnegative matrix factorization (DNMF), is implemented by three parts: an encoder module, an NMF module, and a decoder module. Unlike conventional NMF-based nonlinear methods using implicit and predetermined kernels, DNMF provides a new nonlinear scheme applied to NMF via a deep autoencoder framework and realizes nonlinear mapping for input data automatically. Our proposed nonlinear framework can be further generalized to other linear methods. Besides, DNMF greatly expands the NMF application scope by breaking through the limitation of nonnegative input. The Tennessee Eastman process as an industrial benchmark is employed to verify the effectiveness of the proposed method.
机译：在大数据的时代，数据驱动的故障检测对于现代工业系统至关重要。本文考虑了故障检测的潜在复杂性，并提出了一种基于非负矩阵分解（NMF）的新型非线性方法。由AutoEncoder激励，在本文中，我们首先利用输入数据来学习适当的非线性映射函数，该映射函数将原始空间转换为高维特征空间。然后，根据NMF的分解规则，我们将学习的特征空间划分为两个子空间，并且这些子空间中的两个统计数据适当地设计用于非线性故障检测。建立的方法，即深非环境矩阵分解（DNMF）由三个部分实现：编码器模块，NMF模块和解码器模块。与使用隐式和预定内核的传统的基于NMF的非线性方法不同，DNMF通过深度自动化器框架提供应用于NMF的新非线性方案，并自动实现输入数据的非线性映射。我们所提出的非线性框架可以进一步推广到其他线性方法。此外，DNMF通过突破非负输入的限制，大大扩展了NMF应用范围。田纳西州伊斯坦德作为工业基准的进程被用于验证所提出的方法的有效性。

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