The Theory of Matrices. By F R. Gantmacher. Two volumes, pp. 374 and 276. 1959. (Translated from the Russian by K. A. Hirsch; Chelsea Publishing Company, New York)

With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.

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A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

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

Overview of fault diagnosis methods for Discrete Event Systems

In this paper, we investigate supervisory control of networked discrete event systems. Such systems are now widely used in practice. The key feature of a networked discrete event system is communication delays and losses in observation and control. Without addressing communication delays and losses in networked systems, no control will be effective. We propose to use some special observation mappings to deal with observation delays and losses. Based on these mappings, we define network observability. We also consider control delays and losses in networked discrete event systems and define network controllability. We show that these definitions capture precisely the requirements on effective supervisory control in networked discrete event systems in the sense that a networked supervisor exists if and only if both network controllability and network observability are satisfied.

Control of Networked Discrete Event Systems: Dealing with Communication Delays and Losses

Robust diagnosis of discrete event systems against intermittent loss of observations

The first step in the diagnosis of failure occurrences in discrete event systems is the verification of the system diagnosability. Several works have addressed this problem using either diagnosers or verifiers for both centralized and decentralized architectures. In this technical note, we propose a new algorithm to verify decentralized diagnosability of discrete event systems. The proposed algorithm requires polynomial time in the number of states and events of the system and has lower computational complexity than all other methods found in the literature. In addition, it can also be applied to the centralized case.

Polynomial Time Verification of Decentralized Diagnosability of Discrete Event Systems

A practical model for evaluating the performance of proton exchange membrane fuel cells

In [1] , the authors claim that there is an oversight in [2] , in the sense that the proposed verifier is, in general, nondeterministic and the computational complexity analysis is incorrect. The authors in [1] also claim that the complexity of the verification algorithm presented in [3] is reduced when considering the more restrictive setting of projection masks, in contrast to the more general non-projection masks case, and equals the complexity of the verification algorithm presented in [2] . In this note, we show that the computational complexity analysis of [2] is actually correct and that the complexity of the verification algorithm presented in [3] is not reduced without additional modification of the algorithm (not yet proposed in the literature) if projection masks are used, and, therefore, is not equal to the complexity of the algorithm presented in [2] .

“Polynomial Time Verification of Decentralized Diagnosability of Discrete Event Systems” Versus “Decentralized Failure Diagnosis of Discrete Event Systems”: A Critical Appraisal

Marcos V. Moreira

Papers

Robust diagnosis of discrete event systems against intermittent loss of observations

Polynomial Time Verification of Decentralized Diagnosability of Discrete Event Systems

A practical model for evaluating the performance of proton exchange membrane fuel cells

“Polynomial Time Verification of Decentralized Diagnosability of Discrete Event Systems” Versus “Decentralized Failure Diagnosis of Discrete Event Systems”: A Critical Appraisal

Brief paperRobust diagnosis of discrete-event systems against permanent loss of observations☆