Model Prediction-Based Approach to Fault Tolerant Control with Applications

Abstract— Fault-tolerant control (FTC) is an integral component in industrial processes as it enables the system to continue robust operation under some conditions. In this paper, an FTC scheme is proposed for interconnected systems within an integrated design framework to yield a timely monitoring and detection of fault and reconfiguring the controller according to those faults. The unscented Kalman filter (UKF)-based fault detection and diagnosis system is initially run on the main plant and parameter estimation is being done for the local faults. This critical information\ud is shared through information fusion to the main system where the whole system is being decentralized using the overlapping decomposition technique. Using this parameter estimates of decentralized subsystems, a model predictive control (MPC) adjusts its parameters according to the\ud fault scenarios thereby striving to maintain the stability of the system. Experimental results on interconnected continuous time stirred tank reactors (CSTR) with recycle and quadruple tank system indicate that the proposed method is capable to correctly identify various faults, and then controlling the system under some conditions

Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

Mechanical Behavior of Fabric-Film Laminates

Inflatable structures are gaining wide support in planetary scientific missions as well as commercial applications. For such applications a new class of materials made of laminating thin homogenous films to lightweight fabrics are being considered us structura1 gas envelops. The emerging composite materials are a result of recent advances in the manufacturing cf 1ightweight, high strength fibers, fabrics and scrims. The lamination of these load-carrying members with the proper gas barrier film results in wide range of materials suitable for various loading and environmental conditions. Polyester - based woven fabrics laminated to thin homogeneus film of polyester (Maylar) is an example of this class. This fabric/ film laminate is being considered for the development a material suitable for building large gas envelopes for use in the NASA Ultra Long Duration Balloon Program (ULDB). Compared to commercial homogeneus films, the material provides relatively high strength to weight ratio as well as better resistance to crack and tear propagation. The purpose of this papers is to introduce the mechanical behavior of this class of multi-layers composite and to highlight some of the concerns observed during the characterization of these laminate composites

Improved Distributed Estimation Method for Environmental\ud time-variant Physical variables in Static Sensor Networks

In this paper, an improved distributed estimation scheme for static sensor networks is developed. The scheme is developed for environmental time-variant physical variables. The main contribution of this work is that the algorithm in [1]-[3] has been extended, and a filter has been designed with weights, such that the variance of the estimation errors is minimized, thereby improving the filter design considerably\ud and characterizing the performance limit of the filter, and thereby tracking a time-varying signal. Moreover, certain parameter optimization is alleviated with the application of a particular finite impulse response (FIR) filter. Simulation results are showing the effectiveness of the developed estimation algorithm

Passivity and passivation of interconnected time-delay models of reheat power systems

This paper investigates the problems of delay-dependent passivity and passivation of a class of linear interconnected time-delay systems with particular emphasis on multiarea reheat power systems. This class contains state delay in the dynamics and observation at the subsystem (local) level. A new state transformation is developed to exhibit the delay dependence in the system dynamics and a less conservative passivity-bounding inequality is incorporated. Through the analytical development, it is established that the passivity condition can be cast in a linear matrix inequality (LMI) format at the subsystem level thereby facilitating decentralized passivity analysis. For state-feedback passivation, it is proven that it is indifferent to use instantaneous or delayed decentralized state feedback. The case of dynamic output-feedback passivation is also treated. The analytical developments are simulated to a typical multiarea power system and the ensuing results show satisfactory performance

Detecting shielded explosives by coupling prompt gamma neutron activation analysis and deep neural networks

Prompt Gamma Neutron Activation Analysis is a nuclear-based technique that can be used in explosives detection. It relies on bombarding unknown samples with neutrons emitted from a neutron source. These neutrons interact with the sample nuclei emitting the gamma spectrum with peaks at specific energies, which are considered a fingerprint for the sample composition. Analyzing these peaks heights will give information about the unknown sample material composition. Shielding the sample from gamma rays or neutrons will affect the gamma spectrum obtained to be analyzed, providing a false indication about the sample constituents, especially when the shield is unknown. Here we show how using deep neural networks can solve the shielding drawback associated with the prompt gamma neutron activation analysis technique in explosives detection. We found that the introduced end-to-end framework was capable of differentiating between explosive and non-explosive hydrocarbons with accuracy of 95% for the previously included explosives in the model development data set. It was also, capable of generalizing with accuracy 80% over the explosives which were not included in the model development data set. Our results show that coupling prompt gamma neutron activation analysis with deep neural networks has a good potential for high accuracy explosives detection regardless of the shield presence

Epididymal contribution to male infertility: An overlooked problem

The diagnosis and treatment of male infertility, excluding assisted conception, are limited because of, but not limited to, poor understanding of sperm post-testicular development and storage. Many may think that sperm dysfunction is only self-contained in the sperm cell itself as a result of defective spermatogenesis. However, it can also be a consequence of inadequate epididymal maturation following disorders of the epididymis. Improper epididymal functions can disturb semen parameters and sperm DNA integrity, result in high leucocyte concentrations and high numbers of immature germ cells and debris or even cause idiopathic infertility. To date, the data are limited regarding critical markers of sperm maturation and studies that can identify such markers for diagnosis and managing epididymal dysfunction are scarce. Therefore, this article aims to draw attention to recognise a disturbed epididymal environment as a potential cause of male infertility

Assessment of Glomerular Filtration Rates by Cockcroft-Gault and Modification of Diet in Renal Disease Equations in a Cohort of Omani Patients

Objectives: Glomerular filtration rate (GFR) is the best index of renal function and is frequently assessed by corrected creatinine clearance (CCLcr). The limitations of CCLcr have inspired researchers to derive easy formulas to estimate GFR, with Cockcroft-Gault (C-G) and the modification of diet in renal disease (MDRD) being the most widely used. This study aimed to evaluate the validity of these equations by finding the relation between CCLcr and estimated GFR (eGFR) by C-G, modified C-G and MDRD equations. Methods: From 2007 to 2011, 158 subjects were analysed for serum creatinine and CCLcr at Bowsher Polyclinic, Muscat, Oman. The C-G equation was used to obtain eGFRC-G which was adjusted to body surface area (BSA) to obtain eGFRmC-G, and the MDRD equation was used to obtain eGFRMDRD. The eGFRMDRD, eGFRmC-G and eGFRC-G were then compared to CCLcr. Results: The eGFRMDRD, eGFRmC-G and eGFRC-G significantly correlated with CCLcr, with a slightly stronger correlation with eGFRMDRD (r = 0.701, 0.658 and 0.605, respectively). A receiver operating characteristic curve analysis showed that the diagnostic accuracy of eGFRMDRD for diagnosing chronic kidney disease (CKD) was higher than that of eGFRmC-G, which in turn was higher than that of eGFRC-G (area under the curve was 0.846, 0.831, and 0.791; cut-off limits were 61.9, 58.3 and 59.5, respectively). Conclusion: C-G and MDRD equations can be an alternative to the CCLcr test for assessing GFR, thus avoiding the need for the cumbersome and expensive GFR test. The MDRD formula had greater validity than the C-G equation and the C-G equation validity was improved by an adjustment to BSA