scholarly journals Fault Detection in Nonlinear Systems Via Linear Methods

2017 ◽  
Vol 27 (2) ◽  
pp. 261-272 ◽  
Author(s):  
Alexey Zhirabok ◽  
Alexey Shumsky ◽  
Sergey Solyanik ◽  
Alexey Suvorov
Keyword(s):  
Nonlinear Systems ◽  
Fault Detection ◽  
Linear Algebra ◽  
The State ◽  
Observer Design ◽  
Dynamic Approach ◽  
State Equations ◽  
Linear And Nonlinear

AbstractThe problem of robust linear and nonlinear diagnostic observer design is considered. A method is suggested to construct the observers that are disturbance decoupled or have minimal sensitivity to the disturbances. The method is based on a logic-dynamic approach which allows us to consider systems with non-differentiable nonlinearities in the state equations by methods of linear algebra.

Machine Diagnostics Using Nonlinear Output Observer and Neural Network Models

2000 ◽  
Author(s):  
Tor Fretheim ◽  
Rahmat Shoureshi ◽  
Tyrone Vincent ◽  
Duane Torgerson ◽  
John Work
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Fault Detection ◽  
Network Models ◽  
Observer Design ◽  
Nonlinear Observer ◽  
Analytical Models ◽  
Neural Network Models ◽  
The Neural Network ◽  
Complex Nonlinear Systems

Abstract Predictive maintenance is rapidly becoming a familiar concept in industrial fault detection. The ability to detect early warning signals in systems in the form of small changes in dynamic behavior is essential to anticipate failures. In general, accurate system models are an essential part of residual based fault detection. However, in complex nonlinear systems, the development of accurate models can be very difficult, thus usually other approaches are often selected. As an alternative to the nonlinear analytical models, neural networks have shown significant potential in accurately representing nonlinear systems. In this paper we show how a system identified by a neural network, and a nonlinear observer can be used to detect changes in system dynamics. The neural network structure and identification have a significant impact on the observer performance. Different methods for observer design, and appropriate neural network structures for fault detection are discussed. The experimental section shows the observer implemented on a thermo fluid system. Several faults are introduced, and the observer prediction is compared to actual data.

Development of a Robust Nonlinear Observer for a Single-Link Flexible Manipulator

2004 ◽  
Author(s):  
Nabil G. Chalhoub ◽  
Giscard A. Kfoury
Keyword(s):  
Sliding Mode ◽  
Initial Conditions ◽  
The State ◽  
Observer Design ◽  
Nonlinear Observer ◽  
Flexible Manipulator ◽  
State Variables ◽  
State Equations ◽  
On Line ◽  
Simulation Results

Accurate measurements of all the state variables of a given system are often not available due to the high cost of sensors, the lack of space to mount the transducers or the hostile environment in which the sensors must be located. The purpose of this study is to design a robust sliding mode observer that is capable of accurately estimating the state variables of the system in the presence of disturbances and model uncertainties. It should be emphasized that the proposed observer design can handle state equations expressed in the general form. The performance of the nonlinear observer is assessed herein by examining its capability of predicting the rigid and flexible motions of a compliant beam that is connected to a revolute joint. The simulation results demonstrate the ability of the observer in accurately estimating the state variables of the system in the presence of structured uncertainties and under different initial conditions between the observer and the plant. Moreover, they illustrate the deterioration in the performance of the observer when subjected to unstructured uncertainties of the system. Furthermore, the nonlinear observer was successfully implemented to provide on-line estimates of the state variables for two model-based controllers. The simulation results show minimal deterioration in the closed-loop response of the system stemming from the usage of estimated rather than exact state variables in the computation of the control signals.

Mathematical modeling of conversion of non-Gaussian random processes, signals and noise in linear and nonlinear systems

2018 ◽  
pp. 66-78
Author(s):  
V. M. Artyushenko ◽  
V. I. Volovach
Keyword(s):  
Mathematical Modeling ◽  
Nonlinear Systems ◽  
Random Processes ◽  
Mathematical Transformation ◽  
Positive And Negative Feedback ◽  
Non Linear Systems ◽  
Linear And Nonlinear ◽  
Non Gaussian ◽  
Linear And Nonlinear Systems ◽  
Inertial Systems

The questions connected with mathematical modeling of transformation of non-Gaussian random processes, signals and noise in linear and nonlinear systems are considered and analyzed. The mathematical transformation of random processes in linear inertial systems consisting of both series and parallel connected links, as well as positive and negative feedback is analyzed. The mathematical transformation of random processes with polygamous density of probability distribution during their passage through such systems is considered. Nonlinear inertial and non-linear systems are analyzed.

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