On sensor fault diagnosis problem: Compensator design to non linear system in real time

2015 ◽  
Author(s):  
J. M. Orozco Sixtos ◽  
J. Anzurez Marin ◽  
M. Heras Cervantes ◽  
R. Tapia Sanchez
Keyword(s):  
Fault Diagnosis ◽  
Linear System ◽  
Real Time ◽  
Sensor Fault ◽  
Compensator Design ◽  
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 Natural Scene Derived Camera Edge Spatial Frequency Response for Autonomous Vision Systems

2021 ◽  
Vol 2021 (1) ◽  
pp. 88-92
Author(s):  
Oliver van Zwanenberg ◽  
Sophie Triantaphillidou ◽  
Robin B. Jenkin ◽  
Alexandra Psarrou
Keyword(s):  
Linear System ◽  
Spatial Frequency ◽  
Frequency Response ◽  
Real Time ◽  
Natural Scene ◽  
Vision Systems ◽  
Camera System ◽  
Non Linear ◽  
Edge Based ◽  
Non Linear System

The edge-based Spatial Frequency Response (e-SFR) is an established measure for camera system quality performance, traditionally measured under laboratory conditions. With the increasing use of Deep Neural Networks (DNNs) in autonomous vision systems, the input signal quality becomes crucial for optimal operation. This paper proposes a method to estimate the system e-SFR from pictorial natural scene derived SFRs (NSSFRs) as previously presented, laying the foundation for adapting the traditional method to a real-time measure.In this study, the NS-SFR input parameter variations are first investigated to establish suitable ranges that give a stable estimate. Using the NS-SFR framework with the established parameter ranges, the system e-SFR, as per ISO 12233, is estimated. Initial validation of results is obtained from implementing the measuring framework with images from a linear and a non-linear camera system. For the linear system, results closely approximate the ISO 12233 e-SFR measurement. Non-linear system measurements exhibit scene-dependant characteristics expected from edge-based methods. The requirements to implement this method in real-time for autonomous systems are then discussed.

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.

On a non-linear system for shape memory alloys

1990 ◽  
Vol 2 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Ph. B�nilan ◽  
D. Blanchard ◽  
H. Ghidouche
Keyword(s):  
Linear System ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Non Linear ◽  
Non Linear System
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