Query: Fault detection and diagnosis based on modified independent component analysis

Candidate papers:

abstract: A novel multivariate statistical process monitoring (MSPM) method based on modified independent component analysis (ICA) is proposed. ICA is a multivariate statistical tool to extract statistically independent components from observed data, which has drawn considerable attention in research fields such as neural networks, signal processing, and blind source separation. In this article, some drawbacks of the original ICA algorithm are analyzed and a modified ICA algorithm is developed for the purpose of MSPM. The basic idea of the approach is to use the modified ICA to extract some dominant independent components from normal operating process data and to combine them with statistical process monitoring techniques. Variable contribution plots to the monitoring statistics (T2 and SPE) are also developed for fault diagnosis. The proposed monitoring method is applied to fault detection and diagnosis in a wastewater treatment process, the Tennessee Eastman process, and a semiconductor etch process and is compared with conventional PCA monitoring methods. The monitoring results clearly illustrate the superiority of the proposed method. (c) 2006 American Institute of Chemical Engineers AIChE J, 2006
author_names: Jong-Min Lee

S Joe Qin

In-Beum Lee
title: Fault detection and diagnosis based on modified independent component analysis
year: 2006

abstract: In this paper, some drawbacks of both the original independent component analysis (ICA) algorithm and the FastICA algorithm are analyzed as follows: the order of the independent components is difficult to be determined; because of using the Newtonian iteration, FastICA method often leads to local minimum solution, and the suitable source signals are not isolated. To solve these problems, a modified ICA algorithm based on particle swarm optimization (PSO) called PSO ICA is proposed for the purpose of multivariate statistical process monitoring (MSPM) The basic idea of the approach is to use the PSO ICA algorithm to extract some dominant independent components from normal operating process data. The order of independent components is determined according to the role of resumption of the original signal. The proposed monitoring method is applied to fault detection and diagnosis in the Tennessee Eastman process. Applications indicate that PSO ICA effectively captures the independent components.
author_names: Ying-wei Zhang

Yang Zhang
title: Fault detection of non Gaussian processes based on modified independent component analysis
year: 2010

abstract: Statistical fault detection techniques are able to detect fault and diagnose root cause(s) from the monitored process variables. For complex process operations, it is not feasible to screen all the process variables due to monitoring cost and flooding of alarms. Thus, if a fault is originated from a process variable that is not monitored, conventional statistical techniques are incapable of locating the true root cause. To relax this limitation, a two stage fault diagnosis technique is proposed for process operations. In the first stage, the modified independent component analysis is used for fault detection and to identify the faulty monitored variable. In the second stage, a Bayesian Network model is constructed considering the process variables and their dependence obtained from the process flow diagram. Evidence is then generated at the network node corresponding to the faulty variable identified in the first stage. Subsequently, the network is updated and analyzed using deductive and abductive reason.
author_names: Hongyang Yu

F Khan

Vikram Garaniya
title: Modified independent component analysis and Bayesian network based two stage fault diagnosis of process operations
year: 2015

abstract: Abstract Compared to large process faults, the latent and small ones are difficult to be detected. However, the accumulation of these faults may even more harmful to the process. A novel fault detection and diagnosis method is proposed which is based on similarity factor and a variable moving window. The new method is based on the idea that a change of process can be reflected in the distribution of the data, which can be detected more easily by the proposed similarity factor. Meanwhile, it has no Gaussian distribution limitation of the process data, since the mixed similarity factor is introduced. The independent component analysis (ICA) factor and the principal component analysis (PCA) factor are used for similarity comparison for Gaussian and non Gaussian information, respectively. Besides, in order to determine the dynamic step accurately and cut the computation cost, the conventional dynamic method is modified by using autocorrelation analysis. A case study of Tennessee Eastman (TE) benchmark process shows the efficiency of the new proposed method.
author_names: Zhiqiang Ge

Zhi-huan Song
title: Latent and small fault detection and diagnosis for dynamic processes
year: 2008

abstract: In this article, we propose a framework to monitor the chatter phenomenon and to diagnose the cause variables of chatter occurred in the hot strip mill process (HSMP) For monitoring chatter, we develop a chatter index (CI) that quantifies chatter to confirm its occurrence. Based on the data classified as normal by the CI, a multivariate statistical process monitoring model for detecting chatter is constructed using the modified independent component analysis (MICA) method. The monitoring results show that the model based on the MICA outperforms other models based on the principal component analysis and independent component analysis. For the diagnosis of the cause variables of detected chatter, various contribution plots can be used. In this article, we develop a relative contribution plot for a more obvious diagnosis than the existing contribution plot. Using this, we diagnose and analyze the cause variables of the detected chatter in the HSMP.
author_names: Ha-Nui Jo

Byeong Eon Park

Yumi Ji

Dong-Kuk Kim

Jeong Eun Yang

In-Beum Lee
title: Chatter Detection and Diagnosis in Hot Strip Mill Process With a Frequency Based Chatter Index and Modified Independent Component Analysis
venue: IEEE Transactions on Industrial Informatics
year: 2020

abstract: Abstract Dynamic principal component analysis (DPCA) and dynamic independent component analysis (DICA) as the frequently used dimensional reduction methods, have been widely applied to monitor dynamic process. Considering the respective advantages of DPCA and DICA in different data distribution characteristics, this paper proposes a novel process monitoring algorithm named DPCA, DICA and Bayesian Inference (DPCA DICA BI) The main idea of DPCA DICA BI is to put the process variables with same distribution characteristic (Gaussian or non Gaussian) into a block on the basis of the variable normality by Jarque Bera test and then to respectively apply DPCA and DICA in Gaussian and non Gaussian blocks. Finally, to combine the monitoring performance of both blocks, Bayesian inference is employed to make an integrated decision. The DPCA DICA BI as well as PCA, ICA, DPCA, and DICA has been used to a numerical example and Tennessee Eastman process. The simulation results show the superiority of DPCA DICA BI.
author_names: Jian Huang

Xuefeng Yan
title: Dynamic process fault detection and diagnosis based on dynamic principal component analysis, dynamic independent component analysis and Bayesian inference
year: 2015

abstract: Kernel principal component analysis is a technique applied for monitoring nonlinear processes. However, compute control limit based on Gaussian distribution can deteriorate its performance. Kernel density estimation is applied to solve the aforementioned issue. In conventional KPCA, a kernel based model depends on a single Gaussian kernel function selected empirically, which means a single model corresponds to a single Gaussian kernel function. It may be effective for certain kinds of fault but not for others which leads to a poor detection performance. Different Gaussian kernel functions may be needed for each kind of fault. To solve these issue, in this work, a novel ensemble kernel principal component analysis Bayes (EKPCA Bayes) is proposed. The ensemble learning with Bayesian inference strategy were applied into conventional KPCA. At last, the fault diagnosis performance is tested for the first time through contribution plot to find out the root cause variables. The proposed method was tested in the Tennessee Eastman (TE) benchmark process for fault detection and fault diagnosis as well.
author_names: Xintong Li

Rui Felizardo

Feng Xue

Rui Felizardo Mauaie

Li-da Qin

Kai Song
title: Fault detection and diagnosis based on new ensemble kernel principal component analysis
venue: 2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)
year: 2019

abstract: Multivariate statistical process monitoring (MSPM) have been applied to process monitoring for industrial processes. The conventional method for a statistical monitoring model is principal component analysis (PCA) However, this is not sufficient to extract meaningful information in non Gaussian data, which is the property of the process data in many industrial processes. Alternatively, the modified independent component analysis (MICA) method can be used to give meaningful information up to higher order statistics, which improves some drawbacks of independent component analysis (ICA) method. In this paper, we propose a protocol to monitor a chatter phenomenon in a hot strip mill process (HSMP) based on modified independent component analysis (MICA) First, we develop the chatter index (CI) that represent the degree of a chatter numerically. The statistical monitoring model for a chatter detection is constructed by using the chatter free data, which is classified by CI. From the chatter monitoring model, a chatter detection rate of 86.7% is achieved.
author_names: Ha-Nui Jo

Byeong Eon Park

Yumi Ji

Dong-Kuk Kim

Jeong Eun Yang

In-Beum Lee

Jeong Byeol Hong
title: Chatter Detection in Hot Strip Mill Process based on Modified Independent Component Analysis
venue: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN)
year: 2019

abstract: The traditional kernel principal component analysis is popularly used for fault detection, however, it only concentrates on the global structure of data sets and ignores the local structure when it is used to extract the nonlinear features. To solve the problem, a new method named modified kernel principal component analysis is proposed for nonlinear process fault detection and diagnosis. The idea of locality preserving is incorporated into the optimization goal of the traditional kernel principal component analysis, taking the excellence of kernel principal component analysis and manifold learning into account. The new projection space enjoys the similar global structure and the local structure, and thus, more feature information can be extracted. The modified kernel principal component analysis is used to map the data space into the feature space. Next, the feature information is classified through Fisher discriminant analysis. A monitoring statistic is established using the distance of each sample in feature space and its control limit is determined through kernel density estimation. When a fault is detected, the source of performance deterioration can be located by using a diagnosis method based on data set similarity. Finally, the results of Tennessee Eastman simulation experiment show its better effectiveness.
author_names: Han Mi
title: Fault detection and diagnosis method based on modified kernel principal component analysis
year: 2015

abstract: The traditional FastICA method uses the Newton iteration method which often leads to local minimum solution, so that the suitable independent components are not obtained. To solve this problem, a modified ICA algorithm based on adaptive gradient algorithm (Adam) called Adam ICA is proposed for the purpose of multivariate statistical processes monitoring (MSPM) The basic idea of this algorithm is to extract the main independent components from the original data by adaptive low order moment estimation for fault diagnosis and analysis. The proposed monitoring method is applied to fault detection and diagnosis of the vehicle dataset. The results show that the Adam ICA method is more efficient.
author_names: Xinxin Shen

Jianming Zhang
title: Fault Detection and Diagnosis Based on Adam ICA
venue: 2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)
year: 2019