An approach to fault diagnosis for non-linear system based on fuzzy cluster analysis

2002 ◽  
Author(s):  
Yiping Liu ◽  
Yi Shen ◽  
Zhiyan Liu
Keyword(s):  
Cluster Analysis ◽  
Fault Diagnosis ◽  
Linear System ◽  
Fuzzy Cluster ◽  
Fuzzy Cluster Analysis ◽  
Non Linear ◽  
Non Linear System

Non-linear system identification and fault diagnosis using a new GUI interpretation tool

2001 ◽  
Vol 54 (6) ◽  
pp. 425-449 ◽  
Author(s):  
Jui-Jung Liu ◽  
Shan-Jen Cheng ◽  
I-Chung Kung ◽  
Hui-Chen Chang ◽  
S.A. Billings
Keyword(s):  
Fault Diagnosis ◽  
System Identification ◽  
Linear System ◽  
Non Linear ◽  
Linear System Identification ◽  
Non Linear System

scholarly journals Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis

2011 ◽  
Vol 18 (1-2) ◽  
pp. 127-137 ◽  
Author(s):  
Lingli Jiang ◽  
Yilun Liu ◽  
Xuejun Li ◽  
Anhua Chen
Keyword(s):  
Cluster Analysis ◽  
Fault Diagnosis ◽  
Roller Bearing ◽  
Fuzzy Cluster ◽  
Ar Model ◽  
Fuzzy Cluster Analysis ◽  
Bearing Fault Diagnosis ◽  
Stationary Signals ◽  
Gaussian Signal ◽  
Non Gaussian

This paper proposes a new approach combining autoregressive (AR) model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signals of a roller bearing are non-stationary and non-Gaussian. Aiming at this problem, the set of parameters of the AR model is estimated based on higher-order cumulants. Consequently, the AR parameters are taken as the feature vectors, and fuzzy cluster analysis is applied to perform classification and pattern recognition. Experiments analysis results show that the proposed method can be used to identify various types and severities of fault bearings. This study is significant for non-stationary and non-Gaussian signal analysis, fault diagnosis and degradation assessment.

Fault diagnosis for multivariable non-linear systems based on non-linear spectrum feature

2016 ◽  
Vol 39 (7) ◽  
pp. 1017-1026 ◽  
Author(s):  
Jialiang Zhang ◽  
Jianfu Cao ◽  
Feng Gao
Keyword(s):  
Fault Diagnosis ◽  
Linear System ◽  
Frequency Domain ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Variable Step Size ◽  
Linear Spectrum ◽  
Non Linear ◽  
Spectrum Feature ◽  
Non Linear System

In this study, a novel fault diagnosis approach based on a non-linear spectrum feature is proposed for a multivariable non-linear system. The non-linear spectrum features are obtained using a non-linear output frequency response function (NOFRF) and kernel principal component analysis (KPCA). In order to improve the real-time performance of obtaining non-linear spectrum features, a frequency domain variable step size normalized least mean square (FVLMS) adaptive algorithm is presented to identify NOFRF. A multi-fault classifier based on the fusion of a support vector machine (SVM) is designed according to different frequency domain scales, and a fusion method by using sub-classifier classification reliability is proposed. A simulation example about a two-input–two-output non-linear system is provided to illustrate the effectiveness and performance of the proposed approach.

A novel LSSVM-L Hammerstein model structure for system identification and nonlinear model predictive control of CSTR servo and regulatory control

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Akshaykumar Naregalkar ◽  
Subbulekshmi Durairaj
Keyword(s):  
Linear System ◽  
Regulatory Control ◽  
Hammerstein Model ◽  
Support Vector ◽  
Model Structure ◽  
Linear Dynamics ◽  
Vector Machines ◽  
Non Linear ◽  
Laguerre Filters ◽  
Non Linear System

Abstract A continuous stirred tank reactor (CSTR) servo and the regulatory control problem are challenging because of their highly non-linear nature, frequent changes in operating points, and frequent disturbances. System identification is one of the important steps in the CSTR model-based control design. In earlier work, a non-linear system model comprises a linear subsystem followed by static nonlinearities and represented with Laguerre filters followed by the LSSVM (least squares support vector machines). This model structure solves linear dynamics first and then associated nonlinearities. Unlike earlier works, the proposed LSSVM-L (least squares support vector machines and Laguerre filters) Hammerstein model structure solves the nonlinearities associated with the non-linear system first and then linear dynamics. Thus, the proposed Hammerstein’s model structure deals with the nonlinearities before affecting the entire system, decreasing the model complexity and providing a simple model structure. This new Hammerstein model is stable, precise, and simple to implement and provides the CSTR model with a good model fit%. Simulation studies illustrate the benefit and effectiveness of the proposed LSSVM-L Hammerstein model and its efficacy as a non-linear model predictive controller for the servo and regulatory control problem.

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