Fault Diagnosis For Nonlinear Systems Research Articles

Active Fault Diagnosis for Nonlinear Systems with Probabilistic Uncertainties

Stringent requirements on safety and availability of high-performance systems necessitate reliable fault detection and isolation in the event of system failures. This paper investigates active fault diagnosis of nonlinear systems with probabilistic, time-invariant uncertainties of the parameters and initial conditions. A probabilistic model-based approach is presented for the design of auxiliary input signals enhancing fault diagnosability by separation of multiple nonlinear models pertaining to nominal and faulty system operations in the presence of the probabilistic uncertainties. To obtain a computationally tractable formulation, polynomial chaos expansions are used to propagate the probabilistic uncertainties through the system models. The input design problem is formulated in terms of a metric that characterizes the similarity of arbitrarily shaped distributions of the model outputs. An optimal input sequence is generated while considering hard input and state constraints. The simulation results for active diagnosis of multiple faults in a three-tank system indicate the capability of the presented approach to improve fault detectability and isolability under probabilistic uncertainties of the parameters and initial conditions.

Intelligent Fault Diagnosis in Nonlinear Systems

Fault diagnosis in nonlinear systems is a challenging and very active research area. One of the difficulties to detect and isolate faults in nonlinear systems via observer-based methods is the design of a residual generator. In this work an integrated procedure combining conventional decoupling methods and Fuzzy Takagi-Sugeno observers for fault diagnosis in nonlinear systems is proposed. The advantage of the proposed AI based approach is that the design condition for the observers is relaxed in contrast with conventional approaches. Furthermore the potential use of decoupling techniques is reinforced. The design methodology is shown using a three tank system.

An approach to fault diagnosis of nonlinear systems using neural networks with invariance to Fourier transform

A neural network with Gauss-Hermite polynomial activation functions is used for approximating the nonlinear system’s dynamics out of a set of input-output data. Thus the output of the neural network provides a series expansion that takes the form of a weighted sum of Gauss-Hermite basis functions. Knowing that the Gauss-Hermite basis functions satisfy the orthogonality property and remain unchanged under the Fourier transform, subjected only to a change of scale, one has that the considered neural network provides the spectral analysis of the output of the monitored system. Actually, the squares of the weights of the output layer of the neural network denote the distribution of energy into the associated spectral components for the output signal of the monitored nonlinear system. By observing changes in the amplitude of the aforementioned spectral components one can have also an indication about malfunctioning of the monitored system and can detect the existence of failures. Moreover, since specific faults are associated with amplitude changes of specific spectral components of the system fault isolation can be also performed.

Particle Filter Approach to Fault Detection andIsolation in Nonlinear Systems

This paper introduces the particle-filtering (PF) based framework for fault diagnosis in non-linear systems and noise and disturbances being Gaussian. In this paper, we use the sequential Monte Carlo filtering approach where the complete posterior distributions of the estimates are represented through samples or particles as opposed to the mean and covariance of an approximated Gaussian distribution. We compare the fault detection performance with that using the extended Kalman filtering and investigate the isolation performance on a nonlinear system.

3 계 슬라이딩 모드 관측기 기반 로봇 고장 진단

Abstract: This paper investigates an algorithm for robust fault diagnosis in robot manipulators. The TOSM (Third Order Sliding Mode observer) provides both theoretically exact observation and unknown fault identification without filtration. The EOI (Equivalent Output Injections) of the TOSM observers can be used as residuals for the problem of fault diagnosis and to identify the unknown faults. The obtained fault information can be used for fault detection, isolation as well as fault accommodation to the self-correcting failure system. The computer simulation results for a PUMA 560 robot are shown to verify the effectiveness of the proposed strategy. Keywords: fault detection, fault diagnosis, sliding mode observer, nonlinear model I. INTRODUCTION Various approaches to fault diagnosis in nonlinear systems as well as robot manipulators have been proposed recently. The observer based on normal measurable variables have been approached [1,2]. By using neural network learning, robust fault detection scheme for nonlinear system [3], and for robot manipulators [4,5] have been developed. The basic idea of these methods is to design the robust fault diagnosis by using the model based method, and to use neural network (NN) to approximate the faults involved in the observer design. In [6], a neural-fuzzy model is used to obtain the model based of the unknown dynamic system. One of the best advantages of robust fault diagnoses is that they are not only able to detect the occurrence of a fault, but also can be provided the fault information which is useful for compensating the affect of the faults in the dynamic systems. Due to important feature of the sliding mode in the system uncertainties such as handling disturbances and modeling uncertainties through the concepts of sliding surface design and equivalent control, SM techniques have been studied for observer states by many researchers [7,8]. However, in SM applications, chattering is the major drawback in the practical realization. To avoid chattering, different approaches have been proposed [9-11]. The most widely used in practical applications to eliminate the chattering are using higher order sliding mode [12,13]. Especially, second order sliding mode [14], for instance, sub-optimal algorithm [15], super-twisting algorithm have been proposed for states observer [16,17]. However, in the second order sliding mode approach, the unknown input is constructed from the discontinuous term which provides the undesired chattering. Hence, to reduce the chattering, the filtration is required in these designs to obtain the unknown input. On the other hand, the filtration provides the delay and error that reduce the fault estimation performance. To avoid filtration which is required of second order sliding mode, the third order sliding mode observer is investigated [18,19]. In [20], the third-order sliding mode observer is designed to estimate the velocities and external perturbation. The obtained estimation of an external perturbation is used to design the controller to compensate the effect of external perturbation in the system. This paper extends earlier results of our previous work [19], the third-order sliding mode based robust fault diagnosis scheme is designed. The fault information is constructed directly from the equivalent output injection (EOI) of SM without filtration. The obtained fault estimation is used for fault detection, isolation as well as fault accommodation. To verify the effectiveness of the third order sliding mode to fault diagnosis, the simulation is performed on PUMA 560 robot. The remainder of this paper is organized as follows: in section II, the robot dynamics and faults are investigated and problems are given. In section III, the fault diagnosis scheme is designed. The simulation results for a PUMA 560 robot is described in section IV. Section V includes some conclusions. II. PROBLEM FORMULATION Let consider a robot dynamics is described by

Fault diagnosis for non-linear systems deploying self-organising maps

Fault diagnosis for non-linear systems deploying self-organising maps

Aviation-Engine Lubricant System Fault Diagnosis Based on Wavelet Neural Networks

Fault detection and diagnosis methods for non-linear system based on wavelet neural networks are researched. Wavelet analysis and back propagation neural networks are fused, and the algorithmic is inferred. Method of wavelet neural networks is used in fault diagnosis for the lubricant system of the aero-engine. Results of simulation and tests show that, this method, which is good to fault diagnosis for non-linear systems, does better in convergence speed, and is not sensitive to the inputs of the net.

Design of an unknown input observer for fault diagnosis of non-linear systems with state constraints

The paper deals with the problem of designing observers for a class of discrete-time nonlinear systems with an unknown input. In particular, with the use of the Lyapunov method, a design procedure of an asymptotically convergent observer is proposed. Moreover, the effect of decoupling an unknown input is investigated in the light of an undesirable decoupling of possible faults. As a result, a condition for checking an undesirable fault decoupling is provided. Subsequently, it is shown how to employ the proposed robust fault diagnosis observer for the non-linear systems with state constraints. The final part of the paper shows experimental results that confirm the effectiveness of the proposed approach and the potential profits that can be achieved while applying the proposed approach in the fault diagnosis scheme. Copyright©2012 IFAC

Parameter Estimation for Fault Diagnosis in Nonlinear Systems by ANFIS

This paper presents a fault diagnosis approach to detect and estimate components faults in satellite's attitude control systems (ACSs). The proposed solution provides a framework to detect, isolate, and estimate various faults in system components, using Adaptive Fuzzy Inference Systems Parameter Estimators (ANFISPEs) that are designed and based on parameterizations related to each class of fault. Each ANFISPE estimates the corresponding unknown Fault Parameter (FP) that is further used for fault detection, isolation and identification purposes. Simulation results reveal the effectiveness of the developed FDI scheme of an ACSs actuators of a 3-axis stabilized satellite even in presence of disturbances.

Intelligent Process Fault Diagnosis for Nonlinear Systems with Uncertain Plant Model via Extended State Observer and Soft Computing

There have been many studies on observer-based fault detection and isolation (FDI), such as using unknown input observer and generalized observer. Most of them require a nominal mathematical model of the system. Unlike sensor faults, actuator faults and process faults greatly affect the system dynamics. This paper presents a new process fault diagnosis technique without exact knowledge of the plant model via Extended State Observer (ESO) and soft computing. The ESO’s augmented or extended state is used to compute the system dynamics in real time, thereby provides foundation for real-time process fault detection. Based on the input and output data, the ESO identifies the un-modeled or incorrectly modeled dynamics combined with unknown external disturbances in real time and provides vital information for detecting faults with only partial information of the plant, which cannot be easily accomplished with any existing methods. Another advantage of the ESO is its simplicity in tuning only a single parameter. Without the knowledge of the exact plant model, fuzzy inference was developed to isolate faults. A strongly coupled three-tank nonlinear dynamic system was chosen as a case study. In a typical dynamic system, a process fault such as pipe blockage is likely incipient, which requires degree of fault identification at all time. Neural networks were trained to identify faults and also instantly determine degree of fault. The simulation results indicate that the proposed FDI technique effectively detected and isolated faults and also accurately determine the degree of fault. Soft computing (i.e. fuzzy logic and neural networks) makes fault diagnosis intelligent and fast because it provides intuitive logic to the system and real-time input-output mapping.

Real-time fault diagnosis of nonlinear systems

This paper concerns the development of a real-time fault detection and diagnosis system for a class of electrical machines. Changes in the system dynamics due to a fault are detected using nonlinear models, namely, nonlinear functions of the measurable variables. At the core of the fault detection and diagnosis system are artificial neural networks and a new neural network structure designed to capture temporal information in the input data. Difficulties such as voltage unbalance, measurement noise, and variable loads, commonly found in practice, are overcome by the system addressed in this paper. Because false alarms are significantly reduced and the system is robust to parameter variations, high detection and diagnosis performance are achieved during both, learning and testing phases. Experimental results using actual data are included to show the effectiveness of the real-time fault detection system developed.

Robust Fault Diagnosis of Non-linear Systems using Constraints Satisfaction

In this paper, the robust fault diagnosis problem for non-linear systems considering both bounded parametric modelling errors and noises is addressed using constraints satisfaction. Combining available measurements with the model of the monitored system, a set of analytical redundancy relations (ARR), relating only known variables, can be derived. These relations will be used in the fault diagnosis procedure to check the consistency between the observed and the predicted system behaviour. When some inconsistency is detected, the fault isolation mechanism will be activated in order to provide an explanation of the possible cause. Finally, a fault estimation procedure is used to estimate the fault size. To illustrate the usefulness of the proposed approach, a case study based on a well-known four tanks system benchmark is used.

Fault diagnosis for Takagi-Sugeno nonlinear systems

This paper addresses a new scheme for fault diagnosis in nonlinear systems described by Takagi-Sugeno multiple models. Two cases are considered, the first one concerns the T-S models with known premise variables (the input or the output of the system). For the second case it is supposed that the weighting functions depend on unmeasurable premise variables (state of the system). The approach is based on the design of observer-based residual generator by minimization of the disturbances effect and maximizing the effects of the faults. The synthesis is based on the L2 formalism developed for linear systems. The convergence conditions are given in LMI formulation.

Online Fault Diagnosis in Nonlinear Systems Using the Multiple Operating Regime Approach

Many chemical processes exhibit highly nonlinear dynamic behavior when operated over a wide operating range. The fault diagnosis schemes based on a linear perturbation model often prove to be inadequate, with regard to addressing fault diagnosis problems in such systems. In this work, a novel multiple-operating-regimes-based technique is proposed for performing online fault diagnosis in nonlinear systems. A Bayesian approach is used to identify the combination of linear perturbation models in different operating regimes that best-represents the plant dynamics at the current operating point. Nonlinear versions of the generalized likelihood ratio (GLR) method that use multiple linear models for fault identification are proposed. The proposed multimodel based fault diagnosis approaches are computationally efficient and exploit the linearity of each submodel. To arrest the performance degradation caused by the occurrence of faults, the information provided by the fault diagnosis component is then used for online fault accommodation. The efficacy of the proposed diagnosis schemes is demonstrated by conducting simulation studies on two benchmark continuously stirred tank reactor (CSTR) systems and a high-purity binary distillation column system. Analysis of the simulation results reveals that the proposed multimodel Kalman filter-based fault diagnosis schemes outperform the linear GLR method when a nonlinear process is in a transient state over a wide operating range.

New nonlinear residual feedback observer for fault diagnosis in nonlinear systems

The increased complexity of plants and the development of sophisticated control systems have necessitated the parallel development of efficient Fault Detection and Isolation (FDI) systems. This paper discusses a model based technique, viz., observers for detecting and isolating parametric and sensor faults. In this paper, a novel diagonal nonlinear residual feedback observer is proposed which is valid for a certain class of nonlinear systems where, subject to other conditions, the state depends nonlinearly on the fault. A number of typical chemical engineering systems can be represented by models of this form. The structure of the observer ensures that the residuals are diagonally affected by the faults. Conditions for exact decoupling of residuals are presented and convergence of the observer in the presence of step faults is proved using Lyapunov like analysis. Multiple observers and a decision logic module are used for FDI when there are un-monitored faults. Results are presented from numerical simulations of an illustrative example and a typical chemical engineering system: a counter-current heat exchanger.

Fault diagnosis of nonlinear systems based on hybrid PSOSA optimization algorithm

Fault diagnosis of nonlinear systems is of great importance in theory and practice, and the parameter estimation method is an effective strategy. Based on the framework of moving horizon estimation, fault parameters are identified by a proposed intelligent optimization algorithm called PSOSA, which could avoid premature convergence of standard particle swarm optimization (PSO) by introducing the probabilistic jumping property of simulated annealing (SA). Simulations on a three-tank system show the effectiveness of this optimization based fault diagnosis strategy.

Fault diagnosis of nonlinear systems using structured augmented state models

This paper presents an internal model approach for modeling and diagnostic functionality design for nonlinear systems operating subject to single-and multiple-faults. We therefore provide the framework of structured augmented state models. Fault characteristics are considered to be generated by dynamical exosystems that are switched via equality constraints to overcome the augmented state observability limiting the number of diagnosable faults. Based on the proposed model, the fault diagnosis problem is specified as an optimal hybrid augmented state estimation problem. Sub-optimal solutions are motivated and exemplified for the fault diagnosis of the well-known three-tank benchmark. As the considered class of fault diagnosis problems is large, the suggested approach is not only of theoretical interest but also of high practical relevance.

Unscented particle filter and log likelihood ratio based fault diagnosis of nonlinear system in non-Gaussian noises

As for the problem of fault diagnosis of nonlinear system in non-Gaussian noises,a new method based on the unscented particle filter(UPF)is proposed,concerning of the shortcoming of degeneracy and estimation precision of generic particle filter.Firstly,normal/abnormal UPF models are estab- lished separately,and the calculation method of likelihood probability density function and log likelihood ratio are de- ducted.Then,the fault detection and diagnosis rule are given, which can forecast both the happening time and type of the fault.At last,some experiments of nonlinear actuator loop of helicopter are carded out,which can demonstrate the validity and superiority of the proposed method.

Fault diagnosis of nonlinear system based on generalized observer

The fault model of the nonlinear system and the generalized nonlinear observer are presented in this paper. The algorithm compensates the influence of faults on the remnant order by means of adjusting the gain matrix of the observer to track the fault parameters of the system on line. It is proved that the nonlinear observer can realize fault diagnosis. In fact, here the problem of fault diagnosis is converted to the problem of model matching. The fault estimation can be exactly and timely. At last, experiments of three-tank system illustrate the effectiveness of the proposed algorithm.

Neuro-Fuzzy Methods for Fault Diagnosis of Nonlinear Systems

Neuro-Fuzzy Methods for Fault Diagnosis of Nonlinear Systems