A most critical and important issue surrounding the design of automatic control systems with the successively increasing complexity is guaranteeing a high system performance over a wide operating range and meeting the requirements on system reliability and dependability. As one of the key technologies for the problem solutions, advanced fault detection and identification (FDI) technology is receiving considerable attention. The objective of this book is to introduce basic model-based FDI schemes, advanced analysis and design algorithms and the needed mathematical and control theory tools at a level for graduate students and researchers as well as for engineers.

Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

Fault detection, isolation, and reconfiguration (FDIR) is an important and challenging problem in many engineering applications and continues to be an active area of research in the control community. This paper presents a survey of the various model-based FDIR methods developed in the last decade. In the paper, the FDIR problem is divided into the fault detection and isolation (FDI) step, and the controller reconfiguration step. For FDI, we discuss various model-based techniques to generate residuals that are robust to noise, unknown disturbance, and model uncertainties, as well as various statistical techniques of testing the residuals for abrupt changes (or faults). We then discuss various techniques of implementing reconfigurable control strategy in response to faults.

A Survey of Fault Detection, Isolation, and Reconfiguration Methods

A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process

In this paper, problems of optimizing observer-based fault detection (FD) systems in the sense of increasing the robustness to the unknown inputs and simultaneously enhancing the sensitivity to the faults are studied. The core of the study is the development of an approach that simultaneously solves four optimization problems. Different algorithms are derived for the application of this approach to the optimal selection of post-filters as well as optimization of fault detection filters, and to the systems with and without structure constraints. The achieved results also reveal some interesting relationships among the optimization problems considered. Copyright © 2000 John Wiley & Sons, Ltd.

A unified approach to the optimization of fault detection systems

Deals with problems related to the parity relation-based residual generation. A characterization of parity vectors and a relationship between the order of parity relations and the dimension of the parity space are derived. The achieved results are used to determine the degree of freedom for designing parity relation-based residual generators and to study the robustness problem.

A characterization of parity space and its application to robust fault detection

Driven by the increasing needs for the integration of model-based fault diagnosis into the electronic control units (ECUs) with limited computation capacity and motivated by the recent study on the fault tolerant controller architecture, we investigate feedback controller structures aiming at accessing the residuals embedded in the control loops. For this purpose, we first develop an observer-based realization of the Youla parameterization. This result reveals a new interpretation of control signals as a composite of the residual and reference signals. From this viewpoint, different control schemes are studied and useful relationships between the controller structures and embedded residual signals are established. It leads to the development of two kinds of schemes: 1) extracting residual signals from an existing control loop and 2) configuring control loops with an integrated residual access. The achieved results are demonstrated by two examples of the feedback control loops in engine management systems.

Feedback Control Structures, Embedded Residual Signals, and Feedback Control Schemes With an Integrated Residual Access

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac11-proceedings/data/html/papers/2842.pdf

A Survey of the Application of Basic Data-Driven and Model-Based Methods in Process Monitoring and Fault Diagnosis

Multivariate statistical process monitoring (MSPM) methods are powerful tools for detecting faults in industrial systems. However, industrial processes are often subjected to dynamic changes. This dynamic behavior is mainly due to set-point changes and nonlinearities. Because of the nonlinearity of processes, the performance of the classical MSPM methods, which are mainly based on the linearity assumption, becomes unsatisfactory, since the process characteristics will change from one operating point to another. The main objective of the work is to develop an efficient fault detection technique for complex industrial systems, using process historical data and considering the nonlinear behavior of the process. In the proposed approach, the nonlinear system is assumed to be linear around the operating points and therefore considered as a piecewise linear system corresponding to each operating mode. The performance and effectiveness of this approach are demonstrated using data obtained from a paper machine and compared with an available method.

Torsten Jeinsch

Papers

A unified approach to the optimization of fault detection systems

A characterization of parity space and its application to robust fault detection

Feedback Control Structures, Embedded Residual Signals, and Feedback Control Schemes With an Integrated Residual Access

A Survey of the Application of Basic Data-Driven and Model-Based Methods in Process Monitoring and Fault Diagnosis

Quality-Related Fault Detection in Industrial Multimode Dynamic Processes