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Über dieses Buch

This book is devoted to the demands of research and industrial centers for diagnostics, monitoring and decision making systems that result from the increasing complexity of automation and systems, the need to ensure the highest level of reliability and safety, and continuing research and the development of innovative approaches to fault diagnosis. The contributions combine domains of engineering knowledge for diagnosis, including detection, isolation, localization, identification, reconfiguration and fault-tolerant control.

The book is divided into six parts: (I) Fault Detection and Isolation; (II) Estimation and Identification; (III) Robust and Fault Tolerant Control; (IV) Industrial and Medical Diagnostics; (V) Artificial Intelligence; (VI) Expert and Computer Systems.



Fault Detection and Isolation


Fault Diagnosis Based on Controller Modification

Detection and isolation of parametric faults in closed-loop systems will be considered in this paper. A major problem is that a feedback controller will in general reduce the effects from variations in the systems including parametric faults on the controlled output from the system. Parametric faults can be detected and isolated using active methods, where an auxiliary input is applied. Using active methods for the diagnosis of parametric faults in closed-loop systems, the amplitude of the applied auxiliary input need to be increased to be able to detect and isolate the faults in a reasonable time. A negative effect of increasing the amplitude of the auxiliary input is that the disturbances in the external output will be increased and consequently reduce the closed-loop performance. This problem can be handled by using a modification of the feedback controller. Applying the YJBK-parameterization (after Youla, Jabr, Bongiorno and Kucera) for the controller, it is possible to modify the feedback controller with a minor effect on the closed-loop performance in the fault-free case and at the same time optimize the detection and isolation in a faulty case. Controller modification in connection with both fault detection and isolation will be discussed. Also passive fault diagnosis methods based on controller modification will be discussed.
Henrik Niemann

One Approach to Design the Fuzzy Fault Detection Filters for Takagi-Sugeno Models

The paper relates a principle for designing the fuzzy fault detection filters devoted to a class of continuous-time nonlinear systems represented by Takagi-Sugeno models. The extension of the fuzzy reference model principle and the incremental quadratic constraints are proposed to obtain an approximation of H\(_{\infty }\)/H\(_{\_}\) criterion in the residual weight matrix parameter design for TS fuzzy fault detection filters. The design conditions are outlined in terms of linear matrix inequalities to posses a stable design framework.
Dušan Krokavec, Anna Filasová

Robust UIO Design for an Actuator Fault Identification

In this paper an actuator robust fault identification scheme is developed, which is based on an observer within \(\mathcal {H}_{\infty }\) framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error while guaranteeing the convergence of the observer. The effectiveness of the proposed approach is verified with the laboratory multi-tank system.
Piotr Witczak, Marcin Mrugalski

Design of an Adaptive Sensor and Actuator Fault Estimation Scheme with a Quadratic Boundedness Approach

This paper is concerned with the problem of designing adaptive sensor and actuator fault estimation scheme for linear discrete-time systems. In particular, a suitable system parametrization is introduced in order to transform the simultaneous sensor and actuator fault estimation problem into an actuator estimation one. The scheme is dedicated to the system influenced by disturbances and hence, a quadratic boundedness approach is employed to prove its convergence. The final part of the paper shows an illustrative example concerning fault estimation of a three-tank system.
Marcin Witczak, Daniel Zegar, Marcin Pazera

Single Fault Isolability Metrics of the Binary Isolating Structures

First, the critical analysis of isolability features of three chosen metrics is presented. It is proved that all studied and analysed metrics are not sufficiently well designed with respect to requirements of optimization methods referring to the binary fault isolation structures. Next, a set of the novel relatively simple definitions of single fault isolability metrics based on the binary valued structures of residual sets are introduced. The basic features of these metrics are formulated and proved. They allow for quantized fault isolability analysis within well defined normalized spans as well as are indicative of the degree of fault isolability. Finally, an illustrative example of determination of the isolability metrics of the electro-pneumatic actuator assembly is provided.
Michał Bartyś

Optimal Sensor Placement Under Budgetary Constraints

In this paper a method for solving the optimal sensor placement problem is presented. The approach maximizes diagnosability and isolability, while not exceeding the budgetary constraint. The proposed strategy is based on a Binary Diagnostic Matrix. The proposed isolability measure distinguishes weak and strong isolability. It uses the branch-and-bound algorithm to find a solution. The method is then tested on a Fuel Cell Stack System.
Kornel Rostek

Estimation and Identification


Discrete-Time Estimation of Nonlinear Continuous-Time Stochastic Systems

In this paper we consider the problem of state estimation of a dynamic system whose evolution is described by a nonlinear continuous-time stochastic model. We also assume that the system is observed by a sensor in discrete-time moments. To perform state estimation using uncertain discrete-time data, the system model needs to be discretized. We compare two methods of discretization. The first method uses the classical forward Euler method. The second method is based on the continuous-time simulation of the deterministic part of the nonlinear system between consecutive times of measurement. For state estimation we apply an unscented Kalman Filter, which—as opposed to the well known Extended Kalman Filter—does not require calculation of the Jacobi matrix of the nonlinear transformation associated with this method.
Mariusz Domżalski, Zdzisław Kowalczuk

Identification of Models and Signals Robust to Occasional Outliers

In this paper estimation algorithms derived in the sense of the least sum of absolute errors are considered for the purpose of identification of models and signals. In particular, off-line and approximate on-line estimation schemes discussed in the work are aimed at both assessing the coefficients of discrete-time stationary models and tracking the evolution of time-variant characteristics of monitored signals. What is interesting, the procedures resulting from minimization of absolute-error criteria appear to be insensitive to sporadic outliers in the processed data. With this fundamental property the deliberated absolute-error method provides correct results of identification, while the classical least-squares estimation produces outcomes, which are definitely unreliable in such circumstances. The quality of estimation and the robustness of the discussed identification procedures to occasional measurement faults are demonstrated in a few practical numerical tests.
Janusz Kozłowski, Zdzisław Kowalczuk

Adaptive Actuator Fault Estimation for DC Servo Motor

The paper present the problem of robust adaptive actuator fault estimation for linear discrete-time systems. The main part of this paper presents problem of design a robust observer that will be able to estimate state vector of the system, actuator fault and decoupling the effect of an unknown input. For that purpose, the structure for robust observer was proposed. The first part of the paper deals with the design of observer. The observer is designed in such a way that a prescribed attenuation level is achieved with respect to the fault estimation error and state estimation error. The subsequent part of the paper deals with laboratory system of DC servo motor that will be used in experiment. The final part of the paper shows the experimental results for DC servo motor system, which confirm the effectiveness of the proposed approach.
Mariusz Buciakowski, Marcin Witczak

Evaluating the Position of a Mobile Robot Using Accelerometer Data

This paper analyzes the problem of determining the position of a robot using an accelerometer, which is an essential part of inertial measurement units (IMU). The information gained from such a gauge, however, requires double integration of sensor data. To assure an expected effect, a mathematical model of a low-cost accelerometer of the MEMS type is derived. Moreover, in order to improve the performance of positioning based on acceleration, we propose to construct the designed location system using a mathematical model of the considered mobile robot controlled by a DC motor. Computational and simulation case studies of the resulting observer-based system, in deterministic and stochastic settings, are performed to test the method, to determine its limitations, and, in particular, to verify if the system can work properly for low-cost accelerometers of standard precision.
Zdzisław Kowalczuk, Tomasz Merta

Decentralized Scheduling of Sensor Networks for Parameter Estimation of Spatio-Temporal Processes

The activation scheduling problem for a scanning sensor network monitoring a spatio-temporal process is considered. The configuration of an activation schedule for network nodes measuring the system state is formulated in a sense of a suitable criterion quantifying an estimation accuracy for system parameters. Then, a decomposition of the scheduling problem is provided and a proper distribution of total computational effort and consensus between the network nodes is achieved via information flooding based on a pairwise communication scheme. As a result, a simple exchange algorithm is outlined to solve the design problem in a decentralized fashion. The proposed approach is illustrated on an example of sensor network configuration for monitoring an atmospheric pollution transport process.
Adam Romanek, Maciej Patan, Damian Kowalów

Robust and Fault Tolerant Control


MPC Framework for System Reliability Optimization

This work presents a general framework taking into account system and components reliability in a Model Predictive Control (MPC) algorithm. The objective is to deal with a closed-loop system combining a deterministic part related to the system dynamics and a stochastic part related to the system reliability from an availability point of view. The main contribution of this work consists in integrating the reliability assessment computed on-line using a Dynamic Bayesian Network (DBN) through the weights of the multiobjective cost function of the MPC algorithm. A comparison between a method based on the components reliability (local approach) and a method focused on the system reliability sensitivity analysis (global approach) is considered. The effectiveness and benefits of the proposed control framework are presented through a Drinking Water Network (DWN) simulation.
Jean C. Salazar, Philippe Weber, Fatiha Nejjari, Didier Theilliol, Ramon Sarrate

Towards Robust Predictive Control for Non-linear Discrete Time System

The paper is devoted to the issue of a robust predictive control for a class of non-linear discrete-time systems with an application of an ellipsoidal inner-bounding of a robust invariant set. The crucial issue is to maintain the state of the system inside the robust invariant feasible set, which is a set of states guaranteeing the stability of the proposed control strategy. The approach presented in this paper starts with a robust control design. In case the robust control does not provide expected results, which means that the current state does not belong to the robust invariant set, then a suitable predictive control action is performed in order to enhance the ellipsoidal invariant set. This appealing phenomenon makes it possible to enlarge the domain of attraction of the system that makes the proposed approach an efficient solution to the model predictive control problem.
Mariusz Buciakowski, Marcin Witczak, Józef Korbicz

Self-healing Control Against Actuator Stuck Failures Under Constraints: Application to Unmanned Helicopters

This paper investigates the problem of actuator stuck failures under constraints. In order to guarantee the post-failure system stability and acceptable performance, self-healing control framework is proposed which includes self-healing management module, fault-tolerant controller, reference redesigner and anti-windup compensator. Because of the existence of actuator constraints, the post-failure system may be unstable and the reference may be unreachable. Hence, fault-tolerant controller with anti-windup compensator was used to guarantee stability which was proved by introducing \(H_{\infty }\) performance. Reachability of reference was analyzed by self-healing management module and a new reference could be calculated by reference redesigner. At last, the proposed self-healing framework was applied to a linear unmanned helicopter model for velocities and yaw tracking control.
Xin Qi, Didier Theilliol, Juntong Qi, Youmin Zhang, Jianda Han

H $$_{\infty }$$ Approach to Virtual Actuators Design

The H\(_{\infty }\) approach to virtual actuators design, intended for linear continuous-time systems, is presented in the paper. Using the H\(_{\infty }\) principle, new conditions for virtual actuators design in P and PI structures are formulated in terms of linear matrix inequalities. Related to the static output control under influence of single actuator faults, an example is presented to highlight the benefit of the proposed framework.
Dušan Krokavec, Anna Filasová, Vladimír Serbák, Pavol Liščinský

Design of a Predictive Fault-Tolerant Control for the Battery Assembly Station

The paper deals with modeling and fault-tolerant control of a real battery assembly system, which is under implementation at RAFI GmbH Company. For that purpose a unified max-plus algebra and model predictive control framework is introduced. Subsequently, the control strategy is enhanced with the fault-tolerance features that enhance the overall performance of the production system. As a result, a novel predictive fault-tolerant strategy is developed that is applied to the battery assembly system. Finally, the last part of the paper shows an illustrative example, which clearly exhibits the performance of the proposed approach.
Pawel Majdzik, Anna Akielaszek-Witczak, Lothar Seybold

Industrial and Medical Diagnostics


Approximate Models and Parameter Analysis of the Flow Process in Transmission Pipelines

Basically, the paper deals with the problem of early leak detection in transmission pipelines. First we present the derivation of state-space equations of the flow process in the pipelines. This description is then aggregated in order to obtain a principal model. Next, the problem of process model parametrization is addressed, taking into account the maximization of a model stability margin. The location of the maximum is determined using optimization methods and curve fitting techniques. In such a way an optimal process parametrization is obtained. A simplified state-space model is then derived based on diagonal approximation, referred to as the analytic model (AMDA). Finally, the useful properties of the developed model are analyzed, including the speed and accuracy of an applied inverse matrix.
Zdzisław Kowalczuk, Marek Tatara

Leak Detection in Liquid Transmission Pipelines During Transient State Related to a Change of Operating Point

This article presents leakage detection techniques in a liquid transmission pipeline. It is focused on leaks, which occur during changes of a pipeline’s operating conditions. Elaborated procedures aimed at leakage evaluation are presented. They are based on measuring of process variables such as flow and pressure. The presented solutions do not involve implementing of complex mathematical process dynamics models. The procedures are evaluated by carrying out experimental tests on a physical model of the pipeline.
Paweł Ostapkowicz, Andrzej Bratek

Accuracy Investigations of Turbine Blading Neural Models Applied to Thermal and Flow Diagnostics

Possibility of replacing computational fluid dynamics simulations by a neural model for fluid flow and thermal diagnostics of steam turbines is investigated. Results of calculations of velocity magnitude of steam for a 3D model of the stator of a steam turbine is presented.
Anna Butterweck, Jerzy Głuch

Proposition of Electromyographic Signal Interpretation in the Rehabilitation Process of Patients with Spinal Cord Injuries

Surface electromyography (sEMG) is one of the examinations within the protocol of neuro-rehabilitation processes, that allow the assessment of possible patient progress with respect to conductivity of neurons and skeletal muscle functionality. The interpretation of sEMG signal is one of the critical issues that should be considered in order to diagnose patients with severe spinal cord injuries. Currently, it is very hard to relate values gathered from sEMG to existing reference scale of patient rehabilitation progress. What more, the interpretation of the signal data is very subjective and it is also strongly related to current physical disposition of the patient. Therefore, the objective of our research, is to introduce a mathematical approach which determines the patient’s physical condition, based on sEMG data. To achieve this goal, we propose to use properly defined fuzzy Sugeno integral. The proposed operator allows to combine both: subjective expert knowledge and signal data.
Martin Tabakov, Paweł Kozak, Stefan Okurowski

Hybrid Classification of High-Dimensional Biomedical Tumour Datasets

This paper concerns hybrid approach to classification of high-dimensional tumour data. The research presents a comparison of hybrid classification methods: bagging with Naive Bayes (NaiveBayes), IBk, J48 and SMO as base classifiers, random forest as a variant of bagging with a decision tree as a base classifier, boosting with NaiveBayes, SMO, IBk and J48 as base classifiers, and voting by all single classifiers using majority as a combination rule, as well as five single classification strategies, including k-nearest neighbours (IBk), J48, NaiveBayes, random tree and sequential minimal optimization algorithm for training support vector machines. The major conclusion drawn from the study was that hybrid classifiers has demonstrated its potential ability to accurately and efficiently classify both binary and multiclass high-dimensional sets of tumour specimens.
Liliana Byczkowska-Lipinska, Agnieszka Wosiak

Artificial Intelligence


Learning and Memory Processes in Autonomous Agents Using an Intelligent System of Decision-Making

This paper analyzes functions and structures of the memory that is an indispensable part of an Intelligent System of Decision-making (ISD), developed as a universal engine for autonomous robotics. A simplified way of processing and coding information in human cognitive processes is modelled and adopted for the use in autonomous systems. Based on such a knowledge structure, an artificial model of reality representation and a model of human memory (using, in particular, the concept of Long-Term Memory) are discussed. Finally, the paper presents a way of rearranging the system memory and modelling the processes of learning.
Zdzisław Kowalczuk, Michał Czubenko, Wojciech Jędruch

Solving Highly-Dimensional Multi-Objective Optimization Problems by Means of Genetic Gender

Paper presents a computational optimization study using a genetic gender approach for solving multi-objective optimization problems of detection observers. In this methodology the information about an individual gender of all the considered solutions is applied for the purpose of making distinction between different groups of objectives. This information is drawn out of the fitness of individuals and applied during a current parental crossover in the performed evolutionary multi-objective optimization (EMO) processes.
Tomasz Białaszewski, Zdzisław Kowalczuk

Experimental Comparison of Straight Lines and Polynomial Interpolation Modeling Methods in Ship Evolutionary Trajectory Planning Problem

Paper presents the application of evolutionary algorithms and polynomial interpolation in ship evolutionary trajectory planning method and its comparison to classic approach, where trajectory is modeled by straight lines. Evolutionary algorithms are group of methods that allows\ to find a collision free trajectory in real time, while polynomial interpolation allows to model smooth trajectory, which keeps continuity of velocity and acceleration values along path in opposition to straight lines approach. Paper presents the experimental researches for several collision situations at sea with application of trajectory modeled by straight lines and polynomial interpolation.
Piotr Kolendo, Roman Śmierzchalski

Robust Fault Detection by Means of Echo State Neural Network

This paper deals with the application of Echo State Network (ESN) model to robust fault diagnosis of the Twin Rotor Aero-Dynamical System (TRAS) through modeling the uncertainty of the neural model with the so-called Model Error Modeling method (MEM). The work describes the modeling process of the plant and scenarios in which the system is under influence of the unknown fault. In such fault scenarios the ESN model together with MEM are used to form the uncertainty bands. If the system output exceeds the uncertain region the fault occurrence is signalized. All data used in experiments are collected from the TRAS through the Matlab/Simulink environment.
Andrzej Czajkowski, Krzysztof Patan

Expert and Computer Systems


Towards Knowledge Compilation for Automated Diagnosis: A Qualitative, Model-Based Approach with Constraint Programming

The main idea of Consistency-Based Diagnosis rests in generation of diagnostic hypotheses stating which components of the system may be faulty, so that assuming them faulty explains the observations in a consistent way. Such diagnostic process is analyzed from qualitative perspective. Qualitative diagnostic inference, qualitative conflicts and qualitative diagnoses are presented in detail. Finally, we examine how qualitative knowledge can contribute to refinement of diagnostic inference and how compilation of diagnostic knowledge can be approached.
Antoni Ligęza

Development of Expert System Shell with Context-Based Reasoning

The paper focuses on the expert system shell which is proposed as a tool that can be used for a wide spectrum of industrial applications. A new architecture of the system enables reasoning by means of multi-domain knowledge representations and multi-inference engines. Moreover, the extended functionality of the system is developed using a context based approach. The system is implemented applying a data mining software which makes possible to acquire domain-specific knowledge and its direct application in the expert system shell. In this study, the preliminary verification is presented using the data registered by the SCADA system of the water supply network. The case study results are useful to illustrate the merits and limitations of the proposed approach.
Dominik Wachla, Piotr Przystałka, Mateusz Kalisch, Wojciech Moczulski, Anna Timofiejczuk

Fault Detection Method Using Context-Based Approach

The paper describes the context based and model-free fault detection method. The main purpose of the research is to present that there is the possibility of development of diagnostic schemes using ensemble learning and context based approach to obtain the high efficiency of the fault detection system. The achieved results confirm the effectiveness of the proposed approach and also show its limitations.
Mateusz Kalisch

Automatic Graph-Based Local Edge Detection

In this paper, we present a method of edge detection in the absence of prior knowledge of the analyzed image. The method is based on a graph representation of the image, more precisely, on graph representations of the image parts. The process of edge detection is based on a flow network and graph-cut techniques. “Source” and “sink” are indicated automatically. The results of edge detection are based on an artificial example and real images.
Jagoda Lazarek, Piotr S. Szczepaniak

Harmony Search to Self-Configuration of Fault-Tolerant Grids for Big Data

In this paper, harmony search algorithms have been proposed to self-configuration of fault-tolerant grids for big data processing. Some tasks related to big data processing have been considered. Moreover, two criteria have been applied to evaluate quality of grids. The first criterion is a probability that all tasks meet their deadlines and the second one is grid reliability. Furthermore, some intelligent agents based on harmony search have been developed to support a middleware layer of grids.
Jerzy Balicki, Waldemar Korłub, Maciej Tyszka


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