Paul M. Frank

Bio: Paul M. Frank is an academic researcher from University of Duisburg. The author has contributed to research in topics: Fault detection and isolation & Robustness (computer science). The author has an hindex of 48, co-authored 228 publications receiving 15777 citations. Previous affiliations of Paul M. Frank include University of Duisburg-Essen & Centre national de la recherche scientifique.

Fault diagnostics and fault tolerant control

Abstract: A novel simultaneous fault detection and diagnostics (FDD) and fault tolerant control (FTC) strategy for nonlinear stochastic systems in closed loops based on a continuous stirred tank reactor (CSTR) is presented. The purpose of control is to track the reactant concentration setpoint. Instead of output feedback we propose here to use proportional-integral-derivative (PID) state feedback, which is shown essential to achieve FTC against sensor faults. A new concept of "equivalent bias" is proposed to model the sensor faults. Both the states and the equivalent bias are on-line estimated by a pseudo separate-bias estimation algorithm. The estimated equivalent bias is then evaluated via a modified Bayes' classification based algorithm to detect and diagnose the sensor faults. Many kinds of sensor faults are tested by Monte Carlo simulations, which demonstrate that the proposed strategy has definite fault tolerant ability against sensor faults, moreover the sensor faults can be on-line detected, isolated, and estimated simultaneously.

Fault Diagnosis in Dynamic Systems via State Estimation - a Survey

Abstract: This paper gives a survey on methods for the detection and localization of sensor and component faults of uncertain dynamic systems. In contrast to the commonly used techniques of hardware redundancy these methods make use of analytical redundancy and, thereby, allow to detect and localize faults with the aid of a digital computer. They comprise single, multiple or hierarchical state estimation using Luenberger observers or Kalman filters. An issue of particular relevance is the consideration of parameter uncertainties or parameter variations of the process. Several proposals are discussed to reduce the effects of parameter variations. Moreover, results from computer simulations and a practical technical application to the control of an inverted pendulum are reported.

Evolutionary algorithms for solving multi-objective problems

Abstract: List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.

Stability of Time-Delay Systems

Abstract: Preface, Notations 1.Introduction to Time-Delay Systems I.Frequency-Domain Approach 2.Systems with Commensurate Delays 3.Systems withIncommensurate Delays 4.Robust Stability Analysis II.Time Domain Approach 5.Systems with Single Delay 6.Robust Stability Analysis 7.Systems with Multiple and Distributed Delays III.Input-Output Approach 8.Input-output stability A.Matrix Facts B.LMI and Quadratic Integral Inequalities Bibliography Index

Detection of abrupt changes: theory and application

Abstract: This book is downloadable from http://www.irisa.fr/sisthem/kniga/. Many monitoring problems can be stated as the problem of detecting a change in the parameters of a static or dynamic stochastic system. The main goal of this book is to describe a unified framework for the design and the performance analysis of the algorithms for solving these change detection problems. Also the book contains the key mathematical background necessary for this purpose. Finally links with the analytical redundancy approach to fault detection in linear systems are established. We call abrupt change any change in the parameters of the system that occurs either instantaneously or at least very fast with respect to the sampling period of the measurements. Abrupt changes by no means refer to changes with large magnitude; on the contrary, in most applications the main problem is to detect small changes. Moreover, in some applications, the early warning of small - and not necessarily fast - changes is of crucial interest in order to avoid the economic or even catastrophic consequences that can result from an accumulation of such small changes. For example, small faults arising in the sensors of a navigation system can result, through the underlying integration, in serious errors in the estimated position of the plane. Another example is the early warning of small deviations from the normal operating conditions of an industrial process. The early detection of slight changes in the state of the process allows to plan in a more adequate manner the periods during which the process should be inspected and possibly repaired, and thus to reduce the exploitation costs.

Robust Model-Based Fault Diagnosis for Dynamic Systems

Abstract: There is an increasing demand for dynamic systems to become safer and more reliable This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital It is clear that fault diagnosis is becoming an important subject in modern control theory and practice Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework It contains many important topics and methods; however, total coverage and completeness is not the primary concern The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications The first two chapters are of tutorial value and provide a starting point for newcomers to this field The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications This will certainly appeal to experts in this field Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed Although this is a research monograph, it will be an important text for postgraduate research students world-wide The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world