Sensor placement for fault diagnosis based on structural models: application to a fuel cell stak system

The present work aims to increase the diagnosis systems capabilities by choosing the location of sensors in the process. Therefore, appropriate sensor location will lead to better diagnosis performance and implementation easiness. The work is based on structural models ands some simplifications are considered in order to only focus on the sensor placement analysis. Several approaches are studied to solve the sensor placement problem. All of them find the optimal sensor configuration. The sensor placement techniques are applied to a fuel cell stack system. The model used to describe the behaviour of this system consists of non-linear equations. Furthermore, there are 30 candidate sensors to improve the diagnosis specifications. The results obtained from this case study are used to strength the applicability of the proposed approaches.El present treball té per objectiu incrementar les prestacions dels diagnosticadors mitjançant la localització de sensors en el procés. D'aquesta manera, instal·lant els sensors apropiats s'obtenen millors diagnosticador i més facilitats d'implementació. El treball està basat en models estructurals i contempla una sèrie de simplificacions per tal de entrar-se només en la problemàtica de la localització de sensors. S'utilitzen diversos enfocs per tal de resoldre la localització de sensors, tot ells tenen com objectiu trobar la configuració òptima de sensors. Les tècniques de localització de sensors són aplicades a un sistema basat en una pila de combustible. El model d'aquest sistema està format per equacions no lineals. A més, hi ha la possibilitat d'instal·lar fins a 30 sensors per tal de millorar la diagnosis del sistema. Degut a aquestes característiques del sistema i del model, els resultats obtinguts mitjançant aquest cas d'estudi reafirmen l'aplicabilitat dels mètodes proposats.Postprint (published version

Resilient Design for Process and Runtime Variations

The main objective of this thesis is to tackle the impact of parameter variations in order to improve the chip performance and extend its lifetime

WACCO and LOKO: Strong Consistency at Global Scale

Motivated by a vision for future global-scale services supporting frequent updates and widespread concurrent reads, we propose a scalable object-sharing system called WACCO offering strong consistency semantics. WACCO propagates read responses on a tree-based topology to satisfy broad demand and migrates objects dynamically to place them close to that demand. To demonstrate WACCO, we use it to develop a service called LOKO that could roughly encompass the current duties of the DNS and simultaneously support granular status updates (e.g., currently preferred routes) in a future Internet. We evaluate LOKO, including the performance impact of updates, migration, and fault tolerance, using both traces of DNS queries served by Akamai and traces of NFS traffic on the UNC campus. WACCO uses a novel consistency model that is both stronger than sequential consistency and more scalable than linearizability. Our results show that this model performs better in the DNS case than the NFS case because the former represents a global, shared-object system which better fits the design goals of WACCO. We evaluate two different migration techniques, one of which considers not just client-visible latency but also the budget for the network (e.g., for public and hybrid clouds) among other factors.Doctor of Philosoph

Benchmarks and Controls for Optimization with Quantum Annealing

Quantum annealing (QA) is a metaheuristic specialized for solving optimization problems which uses principles of adiabatic quantum computing, namely the adiabatic theorem. Some devices implement QA using quantum mechanical phenomena. These QA devices do not perfectly adhere to the adiabatic theorem because they are subject to thermal and magnetic noise. Thus, QA devices return statistical solutions with some probability of success where this probability is affected by the level of noise of the system. As these devices improve, it is believed that they will become less noisy and more accurate. However, some tuning strategies may further improve that probability of finding the correct solution and reduce the effects of noise on solution outcome. In this dissertation, these tuning strategies are explored in depth to determine the effect of preprocessing, annealing, and post-processing controls on performance. In particular, these tuning strategies were applied to a real-world NP (nondeterministic polynomial time)-hard optimization problem and portfolio optimization. Although the performance improved very little from tuning the spin reversal transforms, anneal time, and embedding, the results revealed that reverse annealing controls improved the probability of success by an order of magnitude over forward annealing alone. The chain strength experiments revealed that increasing the strength of the intra-chain coupling improves the probability of success until the intra-chain coupling strengths begin to overpower the inter-chain couplings. By taking a closer look at each physical qubit in the embedded chains, the probability for each qubit to be faulty was visualized and was used to develop a post-processing strategy that outperformed the standard, which chooses a logical qubit value from a broken chain. The results of these findings provide a guide for researchers to find the optimal set of controls for their unique real-world optimization problem to determine whether QA provides some benefit over classical computing, lay the groundwork for developing new tuning strategies that could further improve performance, and characterize the current hardware for benchmarking future generations of QA hardware

Modeling and Simulation of Components in an Integrated Gasification Combined Cycle Plant for Developing Sensor Networks to Detect Faults

The goal of this work is to help synthesize a sensor network to detect and diagnose faults and to monitor conditions of the key equipment items. The desired algorithm for sensor network design would provide information about the number, type and location of sensors that should be deployed for fault diagnosis and condition monitoring of a plant. In this work, the focus was on the integrated gasification combined cycle (IGCC) power plant where the faults at the equipment level and the plant level are considered separately. At the plant level, the objective is to observe whether a fault has occurred or not and identify the specific fault. For component-level faults, the objective is to obtain quantitative information about the extent of a particular fault. For the model-based sensor network design, high-fidelity process model of the IGCC plant is the key requirement.;For component level sensor placement, high-fidelity partial differential algebraic equation (PDAE)-based models are developed. Mechanistic models for faults are developed and included in the PDAE-based models. For system-level sensor placement, faults are simulated in the IGCC plant and the dynamic response of the process is captured. Both the steady-state and dynamic information are used to generate markers that are then utilized for sensor network design.;Whether faults in a particular equipment item should be considered at the unit level or system level depend on the criticality of the equipment item, its likelihood to failure, and the resolution desired for specific faults. In this work, the sour water gas shift reactor (SWGSR) and the gasifier are considered at the unit level. Fly ash may get deposited on the SWGSR catalyst and in the voids in the SWGSR resulting in decreased conversion of carbon monoxide. A MATLAB-based PDAE model of the SWGSR has been developed that considers key faults such as changes in the porosity, surface area, and catalyst activity. In a slagging gasifier, the molten slag that flows along the inner wall can penetrate into the refractory layer, and due to chemical corrosion and thermal and mechanical stress eventually result in thinning or spalling of the refractory. Extent of penetration of slag into the refractory wall and the spalling of the refractory are considered to be important variables for condition monitoring of the gasifier. In addition, as an increasing slag layer thickness can eventually lead to shutdown of the gasifier yet the slag layer thickness cannot be directly measured using the current measurement technology, slag layer thickness is also considered to be an important variable for condition monitoring. For capturing the slag formation, and detachment phenomena accurately, a novel hybrid shrinking core-shrinking particle (HSCSP) model is developed. For tracking the detached slag droplets and the char particles along the gasifier, a particle model is developed and integrated with the HSCSP model. A slag model is developed that captures the process of the detachment of the slag droplets from the char surface, transport of the droplets towards the wall, deposition of a fraction of the droplets on the wall and formation of a slag layer on the wall. Finally, a refractory degradation model is developed for calculating the penetration of the slag inside the wall and the size and time for a spall to occur due to the combined effects of volume change as a result of slag penetration as well as thermal and mechanical stresses.;System-level models are enhanced and faults are simulated spanning across various sections of the IGCC plant. For example, in the SELEXOL-based acid gas removal unit the available area in the trays of distillation columns may get reduced due to deposition of solids. This can result in loss of efficiency. Leakages in heat exchangers in this unit can result in the loss of expensive solvent or hazardous gases. In the combined cycle section, faults such as leakages and fouling in the heat exchangers, increased loss of heat through the combustor insulation that can result in loss of efficiency are simulated.;Sensor placement using a two-tier approach is also performed by developing a sensor network for a combined system that includes unit level as well as system level faults. A model of the gasification island is developed by integrating the SWGSR model developed in MATLAB with the model of the rest of the plant developed in Aspen Plus Dynamics. Since the two models are developed using different software platforms, an integration framework is developed that couples and synchronizes the two dynamic models. The sensor network obtained using the models developed in this work is found to be effective in observing and resolving faults both at the unit level as well as the plant level. (Abstract shortened by UMI.)

Optimal sensor placement for sewer capacity risk management

2019 Spring.Includes bibliographical references.Complex linear assets, such as those found in transportation and utilities, are vital to economies, and in some cases, to public health. Wastewater collection systems in the United States are vital to both. Yet effective approaches to remediating failures in these systems remains an unresolved shortfall for system operators. This shortfall is evident in the estimated 850 billion gallons of untreated sewage that escapes combined sewer pipes each year (US EPA 2004a) and the estimated 40,000 sanitary sewer overflows and 400,000 backups of untreated sewage into basements (US EPA 2001). Failures in wastewater collection systems can be prevented if they can be detected in time to apply intervention strategies such as pipe maintenance, repair, or rehabilitation. This is the essence of a risk management process. The International Council on Systems Engineering recommends that risks be prioritized as a function of severity and occurrence and that criteria be established for acceptable and unacceptable risks (INCOSE 2007). A significant impediment to applying generally accepted risk models to wastewater collection systems is the difficulty of quantifying risk likelihoods. These difficulties stem from the size and complexity of the systems, the lack of data and statistics characterizing the distribution of risk, the high cost of evaluating even a small number of components, and the lack of methods to quantify risk. This research investigates new methods to assess risk likelihood of failure through a novel approach to placement of sensors in wastewater collection systems. The hypothesis is that iterative movement of water level sensors, directed by a specialized metaheuristic search technique, can improve the efficiency of discovering locations of unacceptable risk. An agent-based simulation is constructed to validate the performance of this technique along with testing its sensitivity to varying environments. The results demonstrated that a multi-phase search strategy, with a varying number of sensors deployed in each phase, could efficiently discover locations of unacceptable risk that could be managed via a perpetual monitoring, analysis, and remediation process. A number of promising well-defined future research opportunities also emerged from the performance of this research

Proactive-reactive, robust scheduling and capacity planning of deconstruction projects under uncertainty

A project planning and decision support model is developed and applied to identify and reduce risk and uncertainty in deconstruction project planning. It allows calculating building inventories based on sensor information and construction standards and it computes robust project plans for different scenarios with multiple modes, constrained renewable resources and locations. A reactive and flexible planning element is proposed in the case of schedule infeasibility during project execution

Proactive-reactive, robust scheduling and capacity planning of deconstruction projects under uncertainty

A project planning and decision support model is developed and applied to identify and reduce risk and uncertainty in deconstruction project planning. It allows calculating building inventories based on sensor information and construction standards and it computes robust project plans for different scenarios with multiple modes, constrained renewable resources and locations. A reactive and flexible planning element is proposed in the case of schedule infeasibility during project execution

Comparison of two distributed fault diagnosis approaches based on Binary Integer Linear Programming (BILP) optimization

Two distributed fault diagnosis approaches were compared, by analogy, to determine which is more efficient regarding computational complexity. The first approach considered all “locally computed” global and compound sets with minimal cardinality using a heuristic optimization method while minimizing subsystems interactions (communication). The second approach aimed at obtaining minimal coupled MSOs for minimizing the number of common links between MSOs by adding constraints in already existing optimal sensor placement algorithm, which uses BILP, but not in a distributed context. As a result of comparison, complexity of both approaches is characterized.Peer Reviewe

Comparison of two distributed fault diagnosis approaches based on Binary Integer Linear Programming (BILP) optimization

Two distributed fault diagnosis approaches were compared, by analogy, to determine which is more efficient regarding computational complexity. The first approach considered all “locally computed” global and compound sets with minimal cardinality using a heuristic optimization method while minimizing subsystems interactions (communication). The second approach aimed at obtaining minimal coupled MSOs for minimizing the number of common links between MSOs by adding constraints in already existing optimal sensor placement algorithm, which uses BILP, but not in a distributed context. As a result of comparison, complexity of both approaches is characterized.Peer ReviewedPostprint (author's final draft