An improved plant-wide fault detection scheme based on PCA and adaptive threshold for reliable process monitoring: Application on the new revised model of Tennessee Eastman process

2017 ◽  
Vol 32 (5) ◽  
pp. e2978 ◽  
Author(s):  
Azzeddine Bakdi ◽  
Abdelmalek Kouadri
Keyword(s):  
Fault Detection ◽  
Process Monitoring ◽  
Adaptive Threshold ◽  
Tennessee Eastman Process ◽  
Detection Scheme ◽  
Monitoring Application

Adaptive Dynamics Learning for Small Fault Detection of Discrete-Time Nonlinear Uncertain Systems

2019 ◽  
Author(s):  
Jingting Zhang ◽  
Chengzhi Yuan ◽  
Paolo Stegagno
Keyword(s):  
Fault Detection ◽  
Discrete Time ◽  
Uncertain Systems ◽  
Adaptive Dynamics ◽  
Adaptive Threshold ◽  
Detection Methods ◽  
Detection Scheme ◽  
Uncertain Dynamics ◽  
Small Fault ◽  
Nonlinear Uncertain Systems

Abstract This paper addresses the problem of small fault detection for discrete-time nonlinear uncertain systems. The problem is challenging due to (i) the considered system is subject to unstructured nonlinear uncertain dynamics; and (ii) the faults are considered to be “small” in the sense that system states and control inputs in faulty mode remain close to those in normal mode. To overcome these challenges, a novel adaptive dynamics learning based fault detection scheme is proposed. Specifically, an adaptive dynamics learning approach is first proposed to achieve the locally-accurate approximation of the system uncertain dynamics. Then, based on the learned knowledge, a novel residual system is designed by using the absolute measures of the change of the system dynamics resulting from the fault effect. An adaptive threshold is developed based on the residual system for real-time decision making, i.e., the fault is claimed to be detected when the associated residual signal becomes larger than the adaptive threshold. Rigorous analysis is performed to deduce the small fault detectability condition, which is shown to be significantly relaxed compared to those of existing fault detection methods. Extensive simulations have also been conducted to demonstrate the effectiveness and advantages of the proposed approach.

scholarly journals Temporal-Spatial Neighborhood Enhanced Sparse Autoencoder for Nonlinear Dynamic Process Monitoring

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1079
Author(s):  
Nanxi Li ◽  
Hongbo Shi ◽  
Bing Song ◽  
Yang Tao
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Process Monitoring ◽  
Dynamic Process ◽  
Process Data ◽  
Detection Scheme ◽  
Current Time ◽  
Neighborhood Information ◽  
Sparse Autoencoder ◽  
Current Sample

Data-based process monitoring methods have received tremendous attention in recent years, and modern industrial process data often exhibit dynamic and nonlinear characteristics. Traditional autoencoders, such as stacked denoising autoencoders (SDAEs), have excellent nonlinear feature extraction capabilities, but they ignore the dynamic correlation between sample data. Feature extraction based on manifold learning using spatial or temporal neighbors has been widely used in dynamic process monitoring in recent years, but most of them use linear features and do not take into account the complex nonlinearities of industrial processes. Therefore, a fault detection scheme based on temporal-spatial neighborhood enhanced sparse autoencoder is proposed in this paper. Firstly, it selects the temporal neighborhood and spatial neighborhood of the sample at the current time within the time window with a certain length, the spatial similarity and time serial correlation are used for weighted reconstruction, and the reconstruction combines the current sample as the input of the sparse stack autoencoder (SSAE) to extract the correlation features between the current sample and the neighborhood information. Two statistics are constructed for fault detection. Considering that both types of neighborhood information contain spatial-temporal structural features, Bayesian fusion strategy is used to integrate the two parts of the detection results. Finally, the superiority of the method in this paper is illustrated by a numerical example and the Tennessee Eastman process.

scholarly journals Online monitoring scheme using principal component analysis through Kullback-Leibler divergence analysis technique for fault detection

2019 ◽  
Vol 42 (6) ◽  
pp. 1225-1238 ◽  
Author(s):  
Wahiba Bounoua ◽  
Amina B Benkara ◽  
Abdelmalek Kouadri ◽  
Azzeddine Bakdi
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Process Monitoring ◽  
Control Charts ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Process ◽  
Multivariate Gaussian Distribution ◽  
Tennessee Eastman Process ◽  
Leibler Divergence

Principal component analysis (PCA) is a common tool in the literature and widely used for process monitoring and fault detection. Traditional PCA is associated with the two well-known control charts, the Hotelling’s T2 and the squared prediction error (SPE), as monitoring statistics. This paper develops the use of new measures based on a distribution dissimilarity technique named Kullback-Leibler divergence (KLD) through PCA by measuring the difference between online estimated and offline reference density functions. For processes with PCA scores following a multivariate Gaussian distribution, KLD is computed on both principal and residual subspaces defined by PCA in a moving window to extract the local disparity information. The potentials of the proposed algorithm are afterwards demonstrated through an application on two well-known processes in chemical industries; the Tennessee Eastman process as a reference benchmark and three tank system as an experimental validation. The monitoring performance was compared to recent results from other multivariate statistical process monitoring (MSPM) techniques. The proposed method showed superior robustness and effectiveness recording the lowest average missed detection rate and false alarm rates in process fault detection.

scholarly journals Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 227
Author(s):  
Jinlin Zhu ◽  
Muyun Jiang ◽  
Zhong Liu
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Process Monitoring ◽  
Industrial Process ◽  
Fault Detection And Diagnosis ◽  
Propagation Mechanism ◽  
Tennessee Eastman Process ◽  
Variational Autoencoder ◽  
Detection And Diagnosis ◽  
Detection Schemes

This work considers industrial process monitoring using a variational autoencoder (VAE). As a powerful deep generative model, the variational autoencoder and its variants have become popular for process monitoring. However, its monitoring ability, especially its fault diagnosis ability, has not been well investigated. In this paper, the process modeling and monitoring capabilities of several VAE variants are comprehensively studied. First, fault detection schemes are defined in three distinct ways, considering latent, residual, and the combined domains. Afterwards, to conduct the fault diagnosis, we first define the deep contribution plot, and then a deep reconstruction-based contribution diagram is proposed for deep domains under the fault propagation mechanism. In a case study, the performance of the process monitoring capability of four deep VAE models, namely, the static VAE model, the dynamic VAE model, and the recurrent VAE models (LSTM-VAE and GRU-VAE), has been comparatively evaluated on the industrial benchmark Tennessee Eastman process. Results show that recurrent VAEs with a deep reconstruction-based diagnosis mechanism are recommended for industrial process monitoring tasks.

scholarly journals Online process monitoring and fault-detection approach based on adaptive neighborhood preserving embedding

2019 ◽  
Vol 52 (5-6) ◽  
pp. 387-398
Author(s):  
Lei Tan ◽  
Peng Li ◽  
Aimin Miao ◽  
Yong Chen
Keyword(s):  
Fault Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Process Monitoring ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Monitoring Method ◽  
Tennessee Eastman Process ◽  
Detection Approach ◽  
Adaptive Neighborhood

This study aims to solve the problem involving the high false alarm rate experienced during the detection process when using the traditional multivariate statistical process monitoring method. In addition, the existing model cannot be updated according to the actual situation. This article proposes a novel adaptive neighborhood preserving embedding algorithm as well as an online fault-detection approach based on adaptive neighborhood preserving embedding. This approach combines the approximate linear dependence condition with neighborhood preserving embedding. According to the newly proposed update strategy, the algorithm can achieve an adaptive update model that realizes the online fault detection of processes. The effectiveness and feasibility of the proposed approach are verified by experiments of the Tennessee Eastman process. Theoretical analysis and application experiment of Tennessee Eastman process demonstrate that in this article proposed fault-detection method based on adaptive neighborhood preserving embedding can effectively reduce the false alarm rate and improve the fault-detection performance.

scholarly journals A distributed expectation maximization-principal component analysis monitoring scheme for the large-scale industrial process with incomplete information

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988549
Author(s):  
Xuanyue Wang ◽  
Xu Yang ◽  
Jian Huang ◽  
Xianzhong Chen
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Process Monitoring ◽  
Expectation Maximization ◽  
Large Scale ◽  
Principal Component ◽  
Component Analysis ◽  
Missing Information ◽  
Tennessee Eastman Process ◽  
Analysis Scheme

Large-scale process monitoring has become a challenging issue due to the integration of sub-systems or subprocesses, leading to numerous variables with complex relationship and potential missing information in modern industrial processes. To avoid this, a distributed expectation maximization-principal component analysis scheme is proposed in this paper, where the process variables are first divided into several sub-blocks using two-layer process decomposition method, based on knowledge and generalized Dice’s coefficient. Then, the missing information of variables is estimated by expectation maximization algorithm in the principal component analysis framework, then the expectation maximization-principal component analysis method is applied for fault detection to each sub-block. Finally, the process monitoring and fault detection results are fused by Bayesian inference technique. Case studies on the Tennessee Eastman process is applied to show the effectiveness and performance of our proposed approach.

scholarly journals Utilization of Classical Scaling Technique in Sustaining Fault Detection Performance in Process Monitoring

2020 ◽  
Vol 6 (1) ◽  
pp. 1-11
Author(s):  
Mohd Yusri Mohd Yunus ◽  
Jie Zhang ◽  
Sajjad K Al-Amshawee
Keyword(s):  
Fault Detection ◽  
Process Monitoring ◽  
Industrial Process ◽  
Principal Component ◽  
Detection Performance ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Tennessee Eastman Process ◽  
Operating Modes ◽  
Main Challenge

Multivariate Statistical Process Monitoring (MSPM) fundamentally adopts the conventional Principal Component Analysis (cPCA) as the main platform for data compression. The main challenge though, the association nature of most industrial process variables are highly non-linear. As a result, the risks of applying the conventional approach of MSPM within this context may include sluggish or failed in detection, misinterpretation of signals, incorrect fault diagnosis and also inflexible as well as insensitive to changing of operating modes. In addressing the issue, this paper introduces new sets of monitoring parameters i.e. Sm2, Sr2 and Sr3, which have been derived within the frameworks of Classical Scaling (CMDS) and Procusters Analysis (PA) methods. The overall fault detection performance that applied based on the Tennessee Eastman Process (TEP) cases show that the Sr3 can detect the faults particularly for abnormal events number 3, 9, 15 and 19 in higher rate compared to the cPCA-MSPM system. This proves that the new monitoring statistics work effectively in avoiding missed detection during monitoring which cannot be addressed effectively by the traditional monitoring system.

New online kernel method with the Tabu search algorithm for process monitoring

2018 ◽  
Vol 41 (10) ◽  
pp. 2687-2698 ◽  
Author(s):  
Hajer Lahdhiri ◽  
Khaoula Ben Abdellafou ◽  
Okba Taouali ◽  
Majdi Mansouri ◽  
Ouajdi Korbaa
Keyword(s):  
Fault Detection ◽  
Process Monitoring ◽  
Reference Model ◽  
Kernel Method ◽  
Search Algorithm ◽  
Synthetic Data ◽  
Operating Conditions ◽  
Training Data ◽  
Adaptive Model ◽  
Tennessee Eastman Process

Process monitoring is an integral part of chemical process, required higher product quality and safety operation. Therefore, the objective of this paper is to ensure the suitable functioning and to improve the fault detection performance of conventional kernel Principal Components Analysis (KPCA). Thus, an online Reduced Rank KPCA (OnRR-KPCA) with adaptive model has been developed to monitor a dynamic nonlinear process. The developed method is proposed. Firstly, to extract the useful observations, from large amount of training data registered in normal operating conditions, in order to construct the reduced reference model. Secondly, to monitor the process online and update the reference model if a new useful observation is available and satisfies the condition of independencies between variables in feature space. To demonstrate the effectiveness of the OnRR-KPCA with adaptive model over the conventional KPCA and the RR-KPCA, the fault detection performances are illustrated through two examples: one using synthetic data, the second using a simulated Tennessee Eastman Process (TEP) data.

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