DeepAI AI Chat
Log In Sign Up

Neural Component Analysis for Fault Detection

by   Haitao Zhao, et al.

Principal component analysis (PCA) is largely adopted for chemical process monitoring and numerous PCA-based systems have been developed to solve various fault detection and diagnosis problems. Since PCA-based methods assume that the monitored process is linear, nonlinear PCA models, such as autoencoder models and kernel principal component analysis (KPCA), has been proposed and applied to nonlinear process monitoring. However, KPCA-based methods need to perform eigen-decomposition (ED) on the kernel Gram matrix whose dimensions depend on the number of training data. Moreover, prefixed kernel parameters cannot be most effective for different faults which may need different parameters to maximize their respective detection performances. Autoencoder models lack the consideration of orthogonal constraints which is crucial for PCA-based algorithms. To address these problems, this paper proposes a novel nonlinear method, called neural component analysis (NCA), which intends to train a feedforward neural work with orthogonal constraints such as those used in PCA. NCA can adaptively learn its parameters through backpropagation and the dimensionality of the nonlinear features has no relationship with the number of training samples. Extensive experimental results on the Tennessee Eastman (TE) benchmark process show the superiority of NCA in terms of missed detection rate (MDR) and false alarm rate (FAR). The source code of NCA can be found in


page 1

page 6

page 8

page 9

page 10


Learnable Faster Kernel-PCA for Nonlinear Fault Detection: Deep Autoencoder-Based Realization

Kernel principal component analysis (KPCA) is a well-recognized nonlinea...

Fault Detection via Occupation Kernel Principal Component Analysis

The reliable operation of automatic systems is heavily dependent on the ...

Second-Order Component Analysis for Fault Detection

Process monitoring based on neural networks is getting more and more att...

Bearings Fault Detection Using Hidden Markov Models and Principal Component Analysis Enhanced Features

Asset health monitoring continues to be of increasing importance on prod...

Symmetry-Aware Autoencoders: s-PCA and s-nlPCA

Nonlinear principal component analysis (nlPCA) via autoencoders has attr...

Dynamical Component Analysis (DyCA) and its application on epileptic EEG

Dynamical Component Analysis (DyCA) is a recently-proposed method to det...

Deep-RLS: A Model-Inspired Deep Learning Approach to Nonlinear PCA

In this work, we consider the application of model-based deep learning i...