Diagnosis of Intermittent Faults

The diagnosis of intermittent faults in dynamic systems modeled as discrete event systems is considered. In many systems, faulty behavior often occurs intermittently, with fault events followed by corresponding reset events for these faults, followed by new occurrences of fault events, and so forth. Since these events are usually unobservable, it is necessary to develop diagnostic methodologies for intermittent faults. Prior methodologies for detection and isolation of permanent faults are no longer adequate in the context of intermittent faults, since they do not account explicitly for the dynamic behavior of these faults. This paper addresses this issue by: (i) proposing a modeling methodology for discrete event systems with intermittent faults; (ii) introducing new notions of diagnosability associated with fault and reset events; and (iii) developing necessary and sufficient conditions, in terms of the system model and the set of observable events, for these notions of diagnosability. The definitions of diagnosability are complementary and capture desired objectives regarding the detection and identification of faults, resets, and the current system status (namely, is the fault present or absent). The associated necessary and sufficient conditions are based upon the technique of diagnosers introduced in earlier work, albeit the structure of the diagnosers needs to be enhanced to capture the dynamic nature of faults in the system model. The diagnosability conditions are verifiable in polynomial time in the number of states of the diagnosers.     

Diagnosability of stochastic discrete-event systems

We investigate diagnosability of stochastic discrete-event systems. We define the notions of A- and AA-diagnosability for stochastic automata; these notions are weaker than the corresponding notion of diagnosability for logical automata introduced by Sampath et al. Through the construction of a stochastic diagnoser, we determine offline conditions necessary and sufficient to guarantee A-diagnosability and sufficient to guarantee AA-diagnosability. We also show how the stochastic diagnoser can be used for on-line diagnosis of failure events. We illustrate the results through two examples from HVAC systems.     

Low-energy wireless communication network design

Energy-efficient wireless communication network design is an important and challenging problem. Its difficulty lies in the fact that the overall performance depends, in a coupled way, on the following subsystems: antenna, power amplifier, modulation, error control coding, and network protocols. In addition, given an energy constraint, improved operation of one of the aforementioned subsystems may not yield better overall performance. Thus, to optimize performance one must account for the coupling among the above subsystems and simultaneously optimize their operation under an energy constraint. In this article we present a generic integrated design methodology that is suitable for many kinds of mobile systems and achieves global optimization under an energy constraint. By pointing out some important connections among different layers in the design procedure, we explain why our integrated design methodology is better than traditional design methodologies. We present numerical results of the application of our design methodology to a situational awareness scenario in a mobile wireless network with different mobility models. These results illustrate the improvement in performance that our integrated design methodology achieves over traditional design methodologies, and the tradeoff between energy consumption and performance.     

Control of semiconductor manufacturing equipment: real-timefeedback control of a reactive ion etcher

This paper describes the development of real-time control technology for the improvement of manufacturing characteristics of reactive ion etchers. A general control strategy is presented. The principal ideas are to sense key plasma parameters, develop a dynamic input-output model for the subsystem connecting the equipment inputs to the key plasma variables, and design and implement a multivariable control system to control these variables. Experimental results show that this approach to closed-loop control leads to a much more stable etch rate in the presence of a variety of disturbances as compared to current industrial practice     

Spectral Solar Irradiance And Some Optical Properties For Various Polluted Atmospheres

Using ground-based spectroradiometric measurements taken over the Athens atmosphere during May 1995, the influence of gaseous pollutants and aerosol on the spectral radiant energy distribution was investigated. It was found that spectral measurements exhibited variations based on various polluted urban atmospheric conditions as determined via gaseous pollutants record analysis. The relative attenuations caused by gaseous pollutants and aerosol can exceed 27%, 17% and 16% in the global ultraviolet, visible and near-infrared portions of the solar spectrum respectively, as compared to “background” values. In contrast, an enhancement of the near-infrared diffuse component by 66%, was observed, while in visible and ultraviolet bands the relative increases reached 54% and 21% respectively. Experimental total Rayleigh-corrected and spectral aerosol optical depths were retrieved, representing differences in polluted air over the Athens atmosphere. The diffuse component accounts for more than 80% of the total radiation field under high polluted atmosphere. The observed differences of solar radiation between the Athens center and at a nearby suburban site are a manifestation of contrasting air properties provided mainly by automotive traffic.     

The decentralized quickest detection problem

Two detectors making independent observations must decide when a Markov chain jumps from state 0 to state 1. The decisions are coupled through a common cost function. It is shown that the optimal decision is characterized by thresholds as in the decoupled case. However, the thresholds are time-varying and their computation requires the solution of two coupled sets of dynamic programming equations. A comparison to the decoupled case shows the structure of the coupling.     

Further results on the asymptotic agreement problem

The problem of reaching a concensus between two decision-makers provided with different information is considered. The problem in which the decision-makers may have different underlying probability models is studied. Results are developed to characterize the likelihood of an agreement being reached eventually in terms of the nature of the inter-decision-maker communications. The problem in which the decision-makers are aware of the possibility that they may have different models is treated. It is found that in this case a deadlock can be reached where neither decision maker can learn additional information from the concensus process and they cannot reach a concensus decision. This result indicates that incorporating human uncertainty in probability assessment into the asymptotic agreement problem can lead to outcomes not anticipated in the general theory previously developed     

Active diagnosis of discrete-event systems

The need for accurate and timely diagnosis of system failures and the advantages of automated diagnostic systems are well appreciated. However, diagnosability considerations are often not explicitly taken into account in the system design. In particular, design of the controller and that of the diagnostic subsystem are decoupled, and this may significantly affect the diagnosability properties of a system. The authors present an integrated approach to control and diagnosis. More specifically, they present an approach for the design of diagnosable systems by appropriate design of the system controller. This problem, which they refer to as the active diagnosis problem, is studied in the framework of discrete-event systems (DESs); it is based on prior and new results on the theory of diagnosis for DESs and on existing results in supervisory control under partial observations. They formulate the active diagnosis problem as a supervisory control problem where the legal language is an “appropriate” regular sublanguage of the regular language generated by the system. They present an iterative procedure for determining the supremal controllable, observable, and diagnosable sublanguage of the legal language and for obtaining the supervisor that synthesizes this language. This procedure provides both a controller that ensures diagnosability of the closed-loop system and a diagnoser for online failure diagnosis. The procedure can be implemented using finite-state machines and is guaranteed to converge in a finite number of iterations. The authors illustrate their approach using a simple pump-valve system     

Sensitivity analysis of an optimal routing policy in an ad hoc wireless network

We examine the sensitivity of optimal routing policies in ad hoc wireless networks with respect to estimation errors in channel quality. We consider an ad hoc wireless network where the wireless links from each node to its neighbors are modeled by a probability distribution describing the local broadcast nature of wireless transmissions. These probability distributions are estimated in real-time. We investigate the impact of estimation errors on the performance of a set of proposed routing policies.