Fahmida N. Chowdhury

Fault estimation and accommodation for linear MIMO discrete-time systems

In this brief, a methodology for detection and accommodation of actuator faults for a class of multi-input-multi-output (MIMO) stochastic systems is presented. First, a new real-time fault estimation module that estimates the actuator effectiveness is developed. The actuator fault diagnosis is based on the estimation of the state vector. Under some conditions, the stochastic system is transformed into two separate subsystems. One of them is not affected by actuator faults, so a reduced order Kalman filter can be used to estimate its states. The other, whose states are measurable, is affected by the faults. Then, the output of the nominal controller is reconfigured to compensate for the loss of actuator effectiveness in the system. Simulation results of a helicopter in vertical plane is presented to demonstrate the performance of the proposed fault-tolerant control scheme.

Parameter fault detection and estimation of a class of nonlinear systems using observers

The focus of this paper is on the detection and estimation of parameter faults in nonlinear systems with nonlinear fault distribution functions. The novelty of this contribution is that it handles the nonlinear fault distribution function; since such a fault distribution function depends not only on the inputs and outputs of the system but also on unmeasured states, it causes additional complexity in fault estimation. The proposed detection and estimation tool is based on the adaptive observer technique. Under the Lipschitz condition, a fault detection observer and adaptive diagnosis observer are proposed. Then, relaxation of the Lipschitz requirement is proposed and the necessary modification to the diagnostic tool is presented. Finally, the example of a one-wheel model with lumped friction is presented to illustrate the applicability of the proposed diagnosis method.

A modular methodology for fast fault detection and classification in power systems

This paper presents a modular yet integrated approach to the problem of fast fault detection and classification. Although the specific application example studied here is a power system, the method would be applicable to arbitrary dynamic systems. The approach is quite flexible in the sense that it can be model-based or model-free. In the model-free case, we emphasize the use of concepts from signal processing and wavelet theory to create fast and sensitive fault indicators. If a model is available then conventionally generated residuals can serve as fault indicators. The indicators can then be analyzed by standard statistical hypothesis testing or by artificial neural networks to create intelligent decision rules. After a detection, the fault indicator is processed by a Kohonen network to classify the fault. The approach described here is expected to be of wide applicability. Results of computer experiments with simulated faulty transmission lines are included.

Simultaneous identification of time-varying parameters and estimation of system states using iterative learning observers

This article presents the design of an iterative learning observer (ILO) for the purpose of estimating system states while simultaneously identifying time-varying parameters. The proposed ILO uses a novel updating mechanism to identify time-varying parameters instead of using integrators which are commonly used in classical adaptive observers to identify constant parameters while estimating system states. The main idea behind the design of the ILO is the use of learning, i.e. previous information is combined into the ILO for identifying time-varying parameters in real time. Stability and convergence of state and parameter estimation errors are established and proven. An illustrative example exhibits the effectiveness of the proposed ILO.

On a New Type of Neural-Network-Based Input-Output Model: The ANARMA Structure

Abstract In this paper, we present the Additive Nonlinear AutoRegressive Moving Average (ANARMA) structure as an excellent choice for neural-networks-based inputoutput models. The advantage of the ANARMA model is that the time-steps in the argument are pair-wise decomposed, which allows the ANARMA model to be realized in state-space, and to linearize the model via dynamic output feedback. Results of recursive training and feedback linearization of such NN-ANARMA models are presented.

A survey of neural networks applications in automatic control

This paper is a survey of recent literature in neural networks applications in the field of automatic control. It is now generally accepted in the field that for nonlinear, imperfectly or partially known, and complicated systems, neural networks offer some of the most effective control techniques. In this paper, no attempt has been made to provide mathematical or algorithmic details of the various approaches that are being proposed in the literature; instead, general outlines of some of the techniques are given. The survey is not presented in a chronological order. It is not an exhaustive survey (but an effort has been made to collect publications from different types of journals). Many authors and groups of authors in the field have numerous publications; for each group, we have cited only one or two representative articles. The goal of this paper is to serve as a resource for new researchers in the field.

Ordinary and neural Chi-squared tests for fault detection in multi-output stochastic systems

In this paper, two variations of the Chi-squared test are proposed for fault detection in multioutput stochastic systems. It is assumed that an optimal online estimation technique (such as the Kalman filter) is available in order to generate a residual sequence. We demonstrate that the ordinary (unweighted) Chi-squared test (which implies testing the squared Euclidean norm of the normalized residual vector) is equivalent to the conventional approach of testing the joint probability density function of the residual vector. However, the Chi-squared test is the simpler of the two, and requires less computation. The neural (weighted) Chi-squared test is proposed as a refinement of the ordinary (unweighted) test. It is shown that the weighted Chi-squared test can be easily implemented by a neural learning technique, in the absence of a priori information about how to select the weights. An example of how to implement the Chi-squared test is also presented, using real power system data recorded by digital monitors.

Effect of coupling on the epidemic threshold in interconnected complex networks: A spectral analysis

In epidemic spreading models, if the infection strength is higher than a certain critical value - which we define as the epidemic threshold - then the epidemic spreads through the population. For a single arbitrary graph representing the contact network of the population under consideration, the epidemic threshold turns out to be equal to the inverse of the spectral radius of the contact graph. However, in a real world scenario, it is not possible to isolate a population completely: there is always some interconnection with another network, which partially overlaps with the contact network. In this paper, we study the spreading process of a susceptible-infected-susceptible (SIS) epidemic model in an interconnected network of two generic graphs with generic interconnection and different epidemic-related parameters. Using bifurcation theory and spectral graph theory, we find the epidemic threshold of one network as a function of the infection strength of the other coupled network and adjacency matrices of each graph and their interconnection, and provide a quantitative measure to distinguish weak and strong interconnection topology. These results have implications for the broad field of epidemic modeling and control.

Fault detection of flight critical systems

Describes initial results of a project developing fault tolerant control systems for critical aircraft systems and focuses on the early detection of faulty components. The main goal is the use of signal processing techniques to analyze sensor information and data-mine changes that could be attributable to faulty behavior. The approach compares well with residual-based techniques but requires only output data. Hence, it could be applied to situations where residual-based approaches are not feasible. We present the use of orthogonal filter banks as the processing elements to create fault indicators. The case study is an F14 jet fighter. Using computer simulations we create various faults and monitor the plane angle of attack. The filter bank creates a number of orthogonal components, some of which have a clearly distinct pre and post fault behavior. This behavior changes with the type of fault suggesting that it is possible to classify the faults. Another issue addressed is the generation of alarm signals based on the results of the fault detector. The paper discusses how the information in the components can be processed to create automatic alarm systems.

FAULT TOLERANT SAFE FLIGHT CONTROLLER BANK 1

Abstract This paper examines the issues of safety and certification as they apply to flight control system for commercial planes. Theoretical designs may guarantee stability but some stable trajectories should not be considered safe for human passengers. On the issue of certification, getting acceptance for a control structure may prove challenging if it is not possible to establish a priori the control algorithms that will be used in a particular situation. The approach presented here proposes control structures of reduced complexity that can be exhaustively tested and where the issue of safety can also be addressed. The reduced complexity is achieved by introducing a finite partition of the fault space and designing robust controllers that perform safely within each partition. The transition among controllers is determined by a fault isolation mechanism that uses to advantage the “over-instrumentation” in commercial planes. The concept has been tested with extensive simulations using a 6DOF nonlinear model for B747/200 as a rigid body. This paper presents results for the case of single faults on the control surface actuators. The results support the premises that the structure enables the examination of all possible errors in the fault isolation and that the testing of the controller supplies valuable data for the design of a fault isolation technique.