scholarly journals Fault Detection and Identification for Nonlinear Process Based on Inertia-Based KEPCA and a New Combined Monitoring Index

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Loubna El Fattahi ◽  
El Hassan Sbai
Keyword(s):  
Fault Detection ◽  
Dimensional Space ◽  
Principal Component ◽  
Feature Space ◽  
Nonlinear Process ◽  
Fault Detection And Diagnosis ◽  
High Dimensional ◽  
Tennessee Eastman Process ◽  
Detection And Identification ◽  
Fault Detection And Identification

In the present study, we introduce a new approach for the nonlinear monitoring process based on kernel entropy principal component analysis (KEPCA) and the notion of inertia. KEPCA plays double roles. First, it reduces the data in the high-dimensional space. Second, it constructs the model. Before data reduction, KEPCA transforms input data into high-dimensional feature space based on a nonlinear kernel function and automatically determines the number of principal components (PCs) based on the computation of the inertia. The retained PCs express the maximum inertia entropy of data in the feature space. Then, we use the Parzen window estimator to compute the upper control limit (UCL) for inertia-based KEPCA instead of the Gaussian assumption. Our second contribution concerns a new combined index based on the monitoring indices T2 and SPE in order to simplify the detection task of the fault and prevent any confusion. The proposed approaches have been applied to process fault detection and diagnosis for the well-known benchmark Tennessee Eastman process (TE). Results were performing.

scholarly journals Fault Detection and Diagnosis in Process Data Using Support Vector Machines

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Fang Wu ◽  
Shen Yin ◽  
Hamid Reza Karimi
Keyword(s):  
Dimensional Space ◽  
Industrial Process ◽  
Principal Component ◽  
Original Data ◽  
Fault Detection And Diagnosis ◽  
Fault Classification ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Process Data ◽  
Tennessee Eastman Process

For the complex industrial process, it has become increasingly challenging to effectively diagnose complicated faults. In this paper, a combined measure of the original Support Vector Machine (SVM) and Principal Component Analysis (PCA) is provided to carry out the fault classification, and compare its result with what is based on SVM-RFE (Recursive Feature Elimination) method. RFE is used for feature extraction, and PCA is utilized to project the original data onto a lower dimensional space. PCAT2, SPE statistics, and original SVM are proposed to detect the faults. Some common faults of the Tennessee Eastman Process (TEP) are analyzed in terms of the practical system and reflections of the dataset. PCA-SVM and SVM-RFE can effectively detect and diagnose these common faults. In RFE algorithm, all variables are decreasingly ordered according to their contributions. The classification accuracy rate is improved by choosing a reasonable number of features.

The instrument fault detection and identification based on kernel principal component analysis and coupling analysis in process industry

2020 ◽  
pp. 014233122096024
Author(s):  
Yanjie Liang ◽  
Zhiyong Gao ◽  
Jianmin Gao ◽  
Guangnan Xu ◽  
Rongxi Wang
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Principal Component ◽  
Process Industry ◽  
New Method ◽  
Kernel Principal Component Analysis ◽  
Fault Identification ◽  
Coupling Analysis ◽  
Detection And Identification ◽  
Fault Detection And Identification

This paper investigates the fault detection problem of instruments in process industry. Considering the difficulty of fault identification and the problems of multivariable and large computation complexity based on traditional kernel principal component analysis (KPCA), this paper presents a new method for fault detection and identification, which combines the coupling analysis with kernel principal component for multivariable fault detection and employed the local outlier factor (LOF) for multivariable fault identification. The new method consists of three parts. Firstly, according to nonlinear correlation of multivariable, coupling analysis and module division of variables based on detrended cross-correlation analysis (DCCA) are considered to reduce false alarm rate (FAR) and missed detection rate (MDR) in fault detection and identification. Secondly, KPCA is employed to detect fault in each sub-module of variables. Finally, for the sub-module which has the fault detected in second step, the LOF is adopted to calculate abnormal contribution of each variable in sub-modules to realize fault identification. To prove that the new method has the better capability of processing multivariable fault detection and the more accuracy rate on fault detection and identification than the conventional methods of KPCA, a case study on Tennessee process is carried out at the end.

Fault Detection and Identification in NPP Instruments Using Kernel Principal Component Analysis

2010 ◽  
Author(s):  
Jianping Ma ◽  
Jin Jiang
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Power Plants ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Isolation ◽  
Kernel Principal Component Analysis ◽  
Isolation And Identification ◽  
Detection And Identification ◽  
Fault Detection And Identification

In this paper, kernel principal component analysis (KPCA) is studied for fault detection and identification in the instruments of nuclear power plants. We propose to use mean values of the sensor reconstruction errors of a KPCA model for fault isolation and identification. They provide useful information about the directions and magnitudes of detected faults, which are usually not available from other fault isolation techniques. The performance of the method is demonstrated by applications to real NPP measurements.

Sensor Fault Detection and Identification Method with KPCA in the Process of Aero-Engine Ground Testing

2013 ◽  
Vol 303-306 ◽  
pp. 297-301 ◽  
Author(s):  
Tao Xu
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Sensor Fault ◽  
Ground Testing ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Aero Engine ◽  
Sensor Fault Detection

This paper focuses on Kernel Principal Component Analysis (KPCA) in order to solve the problem of sensor fault detection and identification without linear relationship between sensors. After samples are projected into high-dimensional space, new Principal Component Analysis (PCA) model is established in the kernel principal component eigenvector space. By the contrast with SPE, Hotelling T2 is an appropriate parameter to detect sensor fault because it is more sensitive to sensor failure. The contribution of different sensor to Hotelling T2 is utilized to identify sensor fault for the faulty sensor’s contribution is large than others significantly. Finally, the proposed method is illustrated by the sensors of oil-providing system in the process of aero-engine ground testing. Experiment results show the applicability and effectiveness of the proposed method.

Model for Unanticipated Fault Detection by OCPCA

2012 ◽  
Vol 591-593 ◽  
pp. 2108-2113 ◽  
Author(s):  
Zhang Ming He ◽  
Hai Yin Zhou ◽  
Jiong Qi Wang ◽  
Yuan Yuan Jiao
Keyword(s):  
Fault Detection ◽  
Health Management ◽  
Industrial Process ◽  
Principal Component ◽  
Critical Issue ◽  
Fault Detection And Diagnosis ◽  
Tennessee Eastman Process ◽  
Proposed Model ◽  
Single Pattern ◽  
Detection And Diagnosis

Detection and diagnosis of unanticipated fault has inevitably become a critical issue for PHM (Prognostics and Health Management), especially in the fields of robot, spacecraft and industrial system. It is difficult to overcome this problem since there is lack of history information, prior knowledge and dealing strategy for unanticipated fault. In this paper, a general processing model for unanticipated fault detection and diagnosis is constructed, then, a detection method, named OCPCA (One-class Principal Component Analysis), is proposed. Every OCPCA detector is trained by data from single pattern, and the testing task is to determine whether the testing data is from the very pattern. If the unanticipated fault data is rejected by all OCPCA detectors, then the detection task is accomplished. TEP (Tennessee-Eastman Process), a widely used simulated system based on an actual industrial process, is used to verify the detection of unanticipated fault. The results demonstrate the validity of the proposed model and method.

Gas Turbine Fault Detection and Diagnosis Using Nonlinear Feature Extraction Methods

2005 ◽  
Author(s):  
Joydeb Mukherjee ◽  
Venkataramana B. Kini ◽  
Sunil Menon ◽  
Lalitha Eswara
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Gas Turbine ◽  
Principal Component ◽  
Feature Space ◽  
Component Analysis ◽  
Extraction Methods ◽  
Fault Detection And Diagnosis ◽  
Sensor Data ◽  
Detection And Diagnosis

Accurate gas turbine fault detection and diagnosis (FDD) is essential to improving airline safety as well as in reducing airline costs associated with delays and cancellations. In this paper, we present FDD methods based on feature extraction methods using nonlinear principal component analysis (NLPCA) and curvilinear component analysis (CCA). The underlying principle of both methods is to find the most representative feature space corresponding to gas turbine normal and faulty operations. During operation, new sensor data is located in this feature space and then it is determined whether a particular fault is indicated. NLPCA is an extension of linear PCA methods to the nonlinear domain; therefore, it is intrinsically better suited to nonlinear domains such as the gas turbine engine. The CCA method is another approach to clustering having superior properties for determining cluster manifolds automatically compared to the popular selforganizing map (SOM) method of clustering. The developed methods are tested with snapshot data collected at takeoff, both normal and faulty, from a turbofan gas turbine propulsion engine and the results are presented.

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