Fault Diagnosis For Nonlinear Systems Research Articles

Auto-extracting technique of dynamic chaos features for nonlinear time series

The main purpose of nonlinear time series analysis is based on the rebuilding theory of phase space, and to study how to transform the response signal to rebuilt phase space in order to extract dynamic feature information, and to provide effective approach for nonlinear signal analysis and fault diagnosis of nonlinear dynamic system. Now, it has already formed an important offset of nonlinear science. But, traditional method cannot extract chaos features automatically, and it needs man's participation in the whole process. A new method is put forward, which can implement auto-extracting of chaos features for nonlinear time series. Firstly, to confirm time delayτby autocorrelation method; Secondly, to compute embedded dimension m and correlation dimension D; Thirdly, to compute the maximum Lyapunov indexλmax; Finally, to calculate the chaos degree Dch of Poincare map, and the non-circle degree Dnc and non-order degree Dno of quasi-phase orbit. Chaos features extracting has important meaning to fault diagnosis of nonlinear system based on nonlinear chaos features. Examples show validity of the proposed method.

Fast and robust fault diagnosis for a class of nonlinear systems: detectability analysis

In recent years, robust fault diagnosis of nonlinear systems has received much more attention due to the universal existence of nonlinearities and model uncertainties in practice. By introducing a new adaptive law and sliding mode observers with boundary layer control into Polycarpou's online approximator, we propose a fast and robust fault diagnosis strategy for a class of nonlinear systems in this article. The robustness and stability are proved theoretically by the Lyapunov method and the detectability conditions as well as the upper bound of detection time are given, which demonstrate that the detection time of our strategy is much shorter than that of Polycarpou's approach. Simulation results on the three-tank system “DTS200” show the effectiveness and fastness of the proposed strategy.

Nonlinear system fault diagnosis based on adaptive estimation*1

An approach to fault diagnosis for a class of nonlinear systems is proposed in this paper. It is based on a new adaptive estimation algorithm for recursive estimation of the parameters related to faults. This algorithm is designed in a constructive manner through a nontrivial combination of a high gain observer and a recently developed linear adaptive observer, without resort to any linearization. Its global exponential convergence is ensured by an easy-to-check persistent excitation condition. A numerical example is presented for illustration.

Nonlinear system fault diagnosis based on adaptive estimation

An approach to fault diagnosis for a class of nonlinear systems is proposed in this paper. It is based on a new adaptive estimation algorithm for recursive estimation of the parameters related to faults. This algorithm is designed in a constructive manner through a nontrivial combination of a high gain observer and a recently developed linear adaptive observer, without resort to any linearization. Its global exponential convergence is ensured by an easy-to-check persistent excitation condition. A numerical example is presented for illustration.

Fault diagnosis for nonlinear systems using a bank of neural estimators

A model-based method to detect faults in nonlinear systems is proposed. Fault diagnosis is accomplished by means of a bank of estimators, which provide estimates of parameters that describe actuator, plant, and sensor faults. These estimators perform according to a receding-horizon strategy and are designed using models of the failures. The problem of designing such estimators for general nonlinear systems is solved by searching for optimal estimation functions. These functions are approximated by feedforward neural networks and the problem is reduced to find the optimal neural weights. The learning can be split into two phases. In the first one, any possible “a priori” knowledge on the statistics of the random variables is used to initialize the neural estimation functions off line. In the second one, the optimization (or training) continues on line. Both off and on line learning rely on stochastic approximation. The performances obtained in the estimation of the fault parameters by the proposed neural estimators and by the extended Kalman filters are compared by means of simulations with an application to underwater robotics.

Robust Analytical Redundancy Relations for Fault Diagnosis In Nonlinear Systems

ABSTRACTThe problem of robust analytical redundancy relations design for fault diagnosis in nonlinear systems is investigated. An approach is proposed to solve this problem through an extension of the system state‐space by means of constant (or slow varying) unknown parameters, followed by designing analytical redundancy relations that are insensitive both to state variables and unknown parameters. To make the design practicable, some problems related to nonlinear transformation of the extended system model into observable one are considered. The quasi‐linear computational form for robust analytical redundancy relations is described. Fault detectability and distinguishability conditions are formulated, and a recommendation for choosing the size of the moving time window is made. The results are demonstrated through an example involving an SISO nonlinear model of an underwater vehicle.

Linearization techniques in fault diagnosis of non-linear systems

In this paper it is shown how the well-known linearization technique can be used to solve the problem of fault diagnosis, for non-linear systems subject to actuator failures. An exponential adaptive observer is designed on the basis of the first approximation approach, achieving locally exponential convergence of observation error as well as unbiased fault diagnosis. A link between linear fault diagnosis and its non-linear counterpart is explored. The effectiveness of the proposed non-linear fault diagnosis scheme is illustrated through a simulation example.

An Approach to Fault Diagnosis for Nonlinear System with Different Operating Points

This paper presents a new approach to fault diagnosis for nonlinear systems with different operating points. The model parameters of a class of nonlinear dynamic systems treated as functions of measured operating point and faults are estimated by radial basis function (RBF) neural networks. The effects caused by occurrence of fault can be differed from the ones caused by variance of operating points precisely with the help of the new concept “reference operating point”. Consequently, the diagnostic result is obtained by means of fuzzy logic. Finally this method is verified in a nonlinear position servo control system.

Nonlinear Adaptive Observer Based Component Fault Diagnosis of Nonlinear Systems in Closed-Loops

Designing Adaptive observers for MIMO nonlinear time-varying deterministic systems is an open problem. In this paper we give a novel solution to this problem by use of a modified strong tracking filter(STF). First, the modified STF is presented, then some technical points of view to use the STF as a nonlinear adaptive observer are discussed. Later on, by combining with a modified Bayes classification algorithm, the proposed nonlinear adaptive observer is adopted to constitute a fault detection and diagnosis algorithm, and is successfully used for the component fault diagnosis of a continuous stirred tank reactor, a nonlinear deterministic system controlled in closedloops. Computer simulation results demonstrate the effectiveness of the proposed schemes