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.

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.

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.

Fault Diagnosis for Motor Rotor Based on KPCA-SVM

2011 ◽  
Vol 143-144 ◽  
pp. 680-684 ◽  
Author(s):  
Ping Li ◽  
Xue Jun Li ◽  
Ling Li Jiang ◽  
Da Lian Yang
Keyword(s):  
Fault Diagnosis ◽  
Dimensional Space ◽  
Principal Component ◽  
Vibration Signal ◽  
Training Sample ◽  
Fault Classification ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Nonlinear Properties ◽  
Nonlinear Features

Aimed at the nonlinear properties of motor rotor vibration signal,a fault diagnosis method based on kernel principal component analysis (KPCA) and support vector machines (SVM) was proposed. Initial feature vectors of motor vibration signal were mapped into higher-dimensional space with kernel function. Then the PCA method was used to analyze the data in the high dimensional space to extract the nonlinear features which is used as training sample of SVM fault classifier. Then the rotor fault is identified using the trained classifier. The classification effect of KPCA-SVM is compared with PCA-SVM and SVM. The result shows that the method based on KPCA-SVM can identify motor rotor fault efficiently and fulfill fault classification accurately.

A Winch Fault Classification Algorithm Based on Cluster Kernel Semi-Supervised Support Vector Machine

2012 ◽  
Vol 220-223 ◽  
pp. 452-458
Author(s):  
Xian Xin Shi ◽  
Zhong Xiang Zhao ◽  
Chang Jian Zhu ◽  
Xiao Xiao Kong ◽  
Jun Fei Chai ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Spectral Clustering ◽  
Cluster Algorithm ◽  
Original Data ◽  
Fault Classification ◽  
Support Vector ◽  
The Novel ◽  
High Classification Accuracy ◽  
Novel Approach ◽  
Cluster Assumption

A cluster kernel semi-supervised support vector machine (CKS3VM) based on spectral cluster algorithm is proposed and applied in winch fault classification in this paper. The spectral clustering method is used to re-represent original data samples in an eigenvector space so as to make the data samples in the same cluster gather together much better. Then, a cluster kernel function is constructed upon the eigenvector space. Finally, a cluster kernel S3VM is designed which can satisfy the cluster assumption of semi-supervised study. The experiments on winch fault classification show that the novel approach has high classification accuracy.

A Novel Feature Selection Method for Process Fault Diagnosis

2013 ◽  
Vol 427-429 ◽  
pp. 2045-2049
Author(s):  
Chun Mei Yu ◽  
Sheng Bo Yang
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Sparse Representation ◽  
Selection Process ◽  
Feature Selection Method ◽  
Principal Component ◽  
Selection Method ◽  
Fault Classification ◽  
Process Data ◽  
Bhattacharyya Distance

To increase fault classification performance and reduce computational complexity,the feature selection process has been used for fault diagnosis.In this paper, we proposed a sparse representation based feature selection method and gave detailed procedure of the algorithm. Traditional selecting methods based on wavelet package decomposition and Bhattacharyya distance methods,and sparse methods, including sparse representation classifier, sparsity preserving projection and sparse principal component analysis,were compared to the proposed method.Simulations showed the proposed selecting method gave better performance on fault diagnosis with Tennessee Eastman Process data.

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.

Multifault Diagnosis of Combined Hydraulic and Mechanical Centrifugal Pump Faults Using Continuous Wavelet Transform and Support Vector Machines

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Janani Shruti Rapur ◽  
Rajiv Tiwari
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Strong Association ◽  
Principal Component ◽  
Fault Classification ◽  
Support Vector ◽  
Operating Speed ◽  
Continuous Wavelet ◽  
Centrifugal Pumps ◽  
Multiple Faults

Abstract Centrifugal pumps (CPs) fail due to anomalies in fluid flow patterns and/or due to failure of mechanical subsystems in them. In this work, a technique built on the multiclass support vector machine (MSVM) is developed to identify multiple faults in the CP. In addition, the complex problem of fault combinations and their classification is dealt with in this work. The combination of features from motor line current sensors and accelerometers is used to train the algorithm. To take into account the transient as well as harmonic components of fault signatures, continuous wavelet transform (CWT) analysis is used. Thereafter, the most important information from the CWT coefficients is selected using the two proposed novel methods CWT-based on energy (BE)-MSVM and CWT-principal component analysis (PCA)-MSVM, which are BE as well as PCA, respectively. It is experimentally observed that faults in the CPs have a very strong association with its operating speed. Thus, in order to make the CP versatile in operation, it is important that the fault diagnosis methodology is also efficient at large speed range of CP operation. This work attempts to develop a fault classification methodology, which is independent of the CP operating speed.

Sign in / Sign up

Export Citation Format

Share Document