A Model Based Fault Detection Scheme for Nonlinear Multivariable Discrete-Time Systems

In this paper, a novel robust scheme is developed for detecting faults in nonlinear discrete time multi-input and multi-output systems in contrast with the available schemes that are developed in continuous-time. Both state and output faults are addressed by considering separate time profiles. The faults, which could be incipient or abrupt, are modeled using input and output signals of the system. By using nonlinear estimation techniques, the discrete-time system is monitored online. Once a fault is detected, its dynamics are characterized using an online approximator. A stable parameter update law is developed for the online approximator scheme in discrete-time. The robustness, sensitivity, and performance of the fault detection scheme are demonstrated mathematically. Finally, a Continuous Stir Tank Reactor (CSTR) is used as a simulation example to illustrate the performance of the fault detection scheme

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Fault isolation schemes for a class of continuous-time stochastic dynamical systems

In this paper a new method for fault isolation in a class of continuous-time stochastic dynamical systems is proposed. The method is framed in the context of model-based analytical redundancy, consisting in the generation of a residual signal by means of a diagnostic observer, for its posterior analysis. Once a fault has been detected, and assuming some basic a priori knowledge about the set of possible failures in the plant, the isolation task is then formulated as a type of on-line statistical classification problem. The proposed isolation scheme employs in parallel different hypotheses tests on a statistic of the residual signal, one test for each possible fault. This isolation method is characterized by deriving for the unidimensional case, a sufficient isolability condition as well as an upperbound of the probability of missed isolation. Simulation examples illustrate the applicability of the proposed scheme

An Adaptive Approach to Sensor Bias Fault Diagnosis and Accommodation for a Class of Input-Output Nonlinear Systems

This paper presents an adaptive sensor fault diagnosis and accommodation scheme for multiple sensor bias faults for a class of input-output nonlinear systems subject to modeling uncertainty and measurement noise. The proposed scheme consists of a nonlinear estimation model that includes an adaptive component which is initiated upon the detection of a fault, in order to approximate the magnitude of the bias faults. A detectability condition characterizing the class of detectable sensor bias faults is derived and the robustness and stability properties of the adaptive scheme are presented. The estimation of the magnitude of the sensor bias faults allows the identification of the faulty sensors and it is also used for fault accommodation purposes. The effectiveness of the proposed scheme is demonstrated through a simulation example

Evaluation of machine learning techniques for electro-mechanical system diagnosis

The application of intelligent algorithms, in electro-mechanical diagnosis systems, is increasing in order to reach high Reliability and performance ratios in critical and complex scenarios. In this context, different multidimensional intelligent diagnosis systems, based on different machine learning techniques, are presented and evaluated in an electro-mechanical actuator diagnosis scheme. The used diagnosis methodology includes the acquisition of different physical magnitudes from the system, such as machine vibrations and stator currents, to enhance the monitoring capabilities. The features calculation process is based on statistical time and frequency domains features, as well as timefrequency fault indicators. A features reduction stage is, additionally, included to compress the descriptive fault information in a reduced feature set. After, different classification algorithms such as Support Vector Machines, Neural Network, k-Nearest Neighbors and Classification Trees are implemented. Classification ratios over inputs corresponding to previously learnt classes, and generalization capabilities with inputs corresponding to learnt classes slightly modified are evaluated in an experimental test bench to analyze the suitability of each algorithm for this kind of application.Peer ReviewedPostprint (author’s final draft

A survey on uninhabited underwater vehicles (UUV)

ASME Early Career Technical Conference, ASME ECTC, October 2-3, 2009, Tuscaloosa, Alabama, USAThis work presents the initiation of our underwater robotics research which will be focused on underwater vehicle-manipulator systems. Our aim is to build an underwater vehicle with a robotic manipulator which has a robust system and also can compensate itself under the influence of the hydrodynamic effects. In this paper, overview of the existing underwater vehicle systems, thruster designs, their dynamic models and control architectures are given. The purpose and results of the existing methods in underwater robotics are investigated

Robust Neural Network RISE Observer Based Fault Diagnostics And Prediction

A novel fault diagnostics and prediction scheme in continuous time is introduced for a class of nonlinear systems. The proposed method uses a novel neural network (NN) based robust integral sign of the error (RISE) observer, or estimator, allowing for semi-global asymptotic stability in the presence of NN approximation errors, disturbances and unmodeled dynamics. This is in comparison to typical results presented in the literature that show only boundedness in the presence of uncertainties. The output of the observer/estimator is compared with that of the nonlinear system and a residual is used for declaring the presence of a fault when the residual exceeds a user defined threshold. The NN weights are tuned online with no offline tuning phase. The output of the RISE observer is utilized for diagnostics. Additionally, a method for time-to-failure (TTF) prediction, a first step in prognostics, is developed by projecting the developed parameter-update law under the assumption that the nonlinear system satisfies a linear-in-the-parameters (LIP) assumption. The TTF method uses known critical values of a system to predict when an estimated parameter will reach a known failure threshold. The performance of the NN/RISE observer system is evaluated on a nonlinear system and a simply supported beam finite element analysis (FEA) simulation based on laboratory experiments. Results show that the proposed method provides as much as 25% increased accuracy while the TTF scheme renders a more accurate prediction. © 2010 IEEE

Intelligent flight control systems

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms

Detection and Isolation of Simultaneous Additive and Parametric Faults in Nonlinear Stochastic Dynamical Systems

This paper presents a new fault detection and isolation scheme for dealing with simultaneous additive and parametric faults. The new design integrates a system for additive fault detection based on Castillo and Zufiria, 2009 and a new parametric fault detection and isolation scheme inspired in Munz and Zufiria, 2008 . It is shown that the so far existing schemes do not behave correctly when both additive and parametric faults occur simultaneously; to solve the problem a new integrated scheme is proposed. Computer simulation results are presented to confirm the theoretical studies