Fault detection based on neural networks and independent component analysis

2014 ◽  
Author(s):  
M. J. Fuente ◽  
G. I. Sainz-Palmero
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component

An Approach on MCSA-Based Fault Detection Using Independent Component Analysis and Neural Networks

2019 ◽  
Vol 68 (5) ◽  
pp. 1353-1361 ◽  
Author(s):  
Juan Enrique Garcia-Bracamonte ◽  
Juan Manuel Ramirez-Cortes ◽  
Jose de Jesus Rangel-Magdaleno ◽  
Pilar Gomez-Gil ◽  
Hayde Peregrina-Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component

scholarly journals Improved Process Monitoring Scheme Using Multi-Scale Independent Component Analysis

2021 ◽  
Author(s):  
K Ramakrishna Kini ◽  
Muddu Madakyaru
Keyword(s):  
Fault Detection ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Noisy Environment ◽  
Process Data ◽  
Multi Scale ◽  
Efficient Information ◽  
Non Gaussian

AbstractThe task of fault detection is crucial in modern chemical industries for improved product quality and process safety. In this regard, data-driven fault detection (FD) strategy based on independent component analysis (ICA) has gained attention since it improves monitoring by capturing non-gaussian features in the process data. However, presence of measurement noise in the process data degrades performance of the FD strategy since the noise masks important information. To enhance the monitoring under noisy environment, wavelet-based multi-scale filtering is integrated with the ICA model to yield a novel multi-scale Independent component analysis (MSICA) FD strategy. One of the challenges in multi-scale ICA modeling is to choose the optimum decomposition depth. A novel scheme based on ICA model parameter estimation at each depth is proposed in this paper to achieve this. The effectiveness of the proposed MSICA-based FD strategy is illustrated through three case studies, namely: dynamic multi-variate process, quadruple tank process and distillation column process. In each case study, the performance of the MSICA FD strategy is assessed for different noise levels by comparing it with the conventional FD strategies. The results indicate that the proposed MSICA FD strategy can enhance performance for higher levels of noise in the data since multi-scale wavelet-based filtering is able to de-noise and capture efficient information from noisy process data.

A new fault detection scheme based on canonical independent component analysis with application to hot strip mill process

2020 ◽  
pp. 095965182095821
Author(s):  
Rui-Cheng Zhang ◽  
Yu-Ting Li ◽  
Wei-Zheng Liang ◽  
Wei Xiong
Keyword(s):  
Fault Detection ◽  
Independent Component Analysis ◽  
Canonical Variable ◽  
Component Analysis ◽  
Independent Component ◽  
Hot Strip Mill ◽  
Process Data ◽  
Detection Scheme ◽  
Hot Strip ◽  
Mill Process

Aiming at the problems of inaccurate fault detection and error alarm in the process of hot strip mill process, a fault detection scheme of canonical independent component analysis is proposed. The new scheme first uses canonical variable analysis to calculate the canonical variable matrix of observation data, which effectively solves the problem of autocorrelation and cross-correlation. Then the canonical variable matrix is decomposed by independent component analysis to obtain independent elements. Finally, the data are monitored online through constructing statistics. It is proved that the accuracy of the scheme for identifying fault data is reached to 100%, and the misjudgment rate data are reduced to less than 0.6% through the simulation study of the hot strip mill process data.

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