Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System

Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA) interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP), radial basis function (RBF) neural network, and adaptive neurofuzzy inference system (ANFIS) has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented

Methods of condition monitoring and fault detection for electrical machines

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An integrated diagnostic architecture for autonomous robots

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An Integrated Fuzzy Inference Based Monitoring, Diagnostic, and Prognostic System

To date the majority of the research related to the development and application of monitoring, diagnostic, and prognostic systems has been exclusive in the sense that only one of the three areas is the focus of the work. While previous research progresses each of the respective fields, the end result is a variable grab bag of techniques that address each problem independently. Also, the new field of prognostics is lacking in the sense that few methods have been proposed that produce estimates of the remaining useful life (RUL) of a device or can be realistically applied to real-world systems. This work addresses both problems by developing the nonparametric fuzzy inference system (NFIS) which is adapted for monitoring, diagnosis, and prognosis and then proposing the path classification and estimation (PACE) model that can be used to predict the RUL of a device that does or does not have a well defined failure threshold. To test and evaluate the proposed methods, they were applied to detect, diagnose, and prognose faults and failures in the hydraulic steering system of a deep oil exploration drill. The monitoring system implementing an NFIS predictor and sequential probability ratio test (SPRT) detector produced comparable detection rates to a monitoring system implementing an autoassociative kernel regression (AAKR) predictor and SPRT detector, specifically 80% vs. 85% for the NFIS and AAKR monitor respectively. It was also found that the NFIS monitor produced fewer false alarms. Next, the monitoring system outputs were used to generate symptom patterns for k-nearest neighbor (kNN) and NFIS classifiers that were trained to diagnose different fault classes. The NFIS diagnoser was shown to significantly outperform the kNN diagnoser, with overall accuracies of 96% vs. 89% respectively. Finally, the PACE implementing the NFIS was used to predict the RUL for different failure modes. The errors of the RUL estimates produced by the PACE-NFIS prognosers ranged from 1.2-11.4 hours with 95% confidence intervals (CI) from 0.67-32.02 hours, which are significantly better than the population based prognoser estimates with errors of ~45 hours and 95% CIs of ~162 hours

Thermal modeling and characterization for designing reliable power converters for LHC power supplies

Power supplies for LHC experiments (ATLAS) require DC\u2010DC power converters able to work in very hostile environments. The APOLLO collaboration, funded by the Italian Istituto Nazionale di Fisica Nucleare (INFN), aims to study dedicated topologies and to design, build and test demonstrators, developing the needed technology for the industrialization phase. Besides the presence of radiation and magnetic fields, thermal specifications are particularly stringent in the working environment. In order to have the wanted features in terms of reliability and availability during the experimental activity, these power electronics circuits must be cooled by specifically designed water heat sinks, and an accurate thermal design is mandatory in order to guarantee safe and reliable operation. Moreover, an optimized thermal design allows to have a maintenance strategy in compliance with the requirements of the experiments. In this paper thermal characterization is used for tuning a coupled thermo\u2010fluid\u2010dynamic 3D numerical model, for both the water heat sink and the whole system. Based on this model an optimized water heat sink was designed and fabricated. Thermal characterization of the power converter demonstrator in different operating conditions shows good agreement with simulation results

Development of technological approaches based on supercritical fluids for the production of polymeric micro-nano particulate systems for wound healing

2017 - 2018he PhD project titled “Development of technological approaches based on supercritical fluids for the production of polymeric micro-nano particulate systems for wound healing” aimed to develop novel formulations for topical administration to wounds using innovative, inexpensive and environmentally-friendly technologies based on the use of supercritical-CO2 (sc-CO2): supercritical assisted atomization (SAA) and supercritical assisted extraction (SAE) in tandem with prilling. The specific goal of the project was the designing and development of “in-situ” gelling formulations in form of powders or aerogels using polysaccharide-based polymers as carriers for the encapsulated drugs, due to their biocompatibility, biodegradability, low cost and healing improving properties. Optimization of the process parameters were implemented to obtain either submicrometric particulate particles or aerogel beads with desired properties. Size distribution, textural properties, fluid uptake capability and controlled drug release profiles of the optimized formulations have been studied to evaluate the quality of the different wound healing devices. During the first year, supercritical assisted atomization (SAA) was investigated. SAA was applied for the production of “in-situ” gelling dry powders loaded with doxycycline used as antimicrobial drug due to its inhibiting activity against matrix metalloprotease-2 (MMP-2) and metalloprotease-9 (MMP-9) that could enhance the healing process. High mannnuronic content alginate, low methoxyl grade amidated pectin and low molecular weight chitosan, in different combinations, were used as excipients. Process optimization lead to high process yields (up to 89.0%) and the obtained powders showed good technological characteristics since were able to completely gel in three minutes when in contact with simulated wound fluid. Moreover, powders were able to prolong the release of the doxycycline until up to 21 hours after a fast release during the first two hours (“burst effect”). The second year was focused on the development and characterization of aerogel formulations, in form of beads or capsules, obtained by prilling technique in tandem with the supercritical antisolvent extraction (SAE). High mannuronic content alginate was used as carrier for these formulations. Alginate gel beads were produced by prilling using either aqueous or ethanolic calcium chloride solutions as gelling bath thus producing hydrogels or alcogels in a very narrow size distribution (about 2.4 mm ± 6.0%). Subsequently they were submitted to different supercritical-CO2 drying processes for the production of aerogel in form of spherical beads. The resulted aerogels showed very high porosities (98.4-99.8%) and surface areas (271.0-537.3%) for the different sc-CO2 drying processes. Moreover, influence of alginate molecular weight on aerogel properties was studied, resulting to influence the grade of shrinking and the porosity of the aerogels. In addition, the hydrogels and alcogels were also submitted to freeze-drying and oven drying with the purpose to make a comparison in terms of textural properties. Differently, the production of aerogels capsules with controllable shell tickness was designed with the purpose to increase the exudates absorption when beads gel in contact with wounds. For this purpose, core-shell gel microparticles were produced by prilling in co-axial configuration through the development of a new inverse gelation methodology optimized by Artificial Intelligent (AI) tools (Artificial Neural Networks, ANNs, and Neurofuzzy-Logic in combination with genetic algorithms). The obtained core-shell microparticles were formed by a hydrophilic alginate phase as the outer layer and a hydrophobic phase (water-in-oil emulsion) as the inner one of the particles; such core was subsequently removed by supercritical-CO2 drying producing alginate aerogels with an inner void cavity. The third year was focused on the development and characterization of drug-loaded aerogel capsules through supercritical antisolvent extraction (SAE) in tandem with prilling. Drug loaded microparticles were produced by the novel inverse gelation previously cited through prilling in co-axial configuration using ketoprofen lysinate, as model drug, and alginate as polymeric excipient. Aerogel capsules with a thin alginate shell layer and a hollow inner cavity, in which the ketoprofen was present, were produced after the supercritical drying of core-shell microparticles. Aerogel capsules showed good textural properties in terms of porosity (up to 93.1%) and surface area being promising formulations for high fluid uptake (about 500% the weight of the aerogel) from the wounds within seconds. The supercritical drying processes and the characterization of the textural properties of aerogels were carried out during a 7 months period in the University of Santiago de Compostela (Spain). Hence, such novel technologies, using polymers above mentioned, are promising technologies for the development of a new non-expensive generation of dressing formulations with small particle size (“in-situ” gelling powders) or large particles in order to be easily handled (aerogels) both with high surface areas that make them able to absorb high amount of exudate from wounds maintaining at the same time the moisture environment at the wound bed acting as non-traumatic dressings. [edited by Author]XXXI cicl

An investigation into the prognosis of electromagnetic relays.

Electrical contacts provide a well-proven solution to switching various loads in a wide variety of applications, such as power distribution, control applications, automotive and telecommunications. However, electrical contacts are known for limited reliability due to degradation effects upon the switching contacts due to arcing and fretting. Essentially, the life of the device may be determined by the limited life of the contacts. Failure to trip, spurious tripping and contact welding can, in critical applications such as control systems for avionics and nuclear power application, cause significant costs due to downtime, as well as safety implications. Prognostics provides a way to assess the remaining useful life (RUL) of a component based on its current state of health and its anticipated future usage and operating conditions. In this thesis, the effects of contact wear on a set of electromagnetic relays used in an avionic power controller is examined, and how contact resistance combined with a prognostic approach, can be used to ascertain the RUL of the device. Two methodologies are presented, firstly a Physics based Model (PbM) of the degradation using the predicted material loss due to arc damage. Secondly a computationally efficient technique using posterior degradation data to form a state space model in real time via a Sliding Window Recursive Least Squares (SWRLS) algorithm. Health monitoring using the presented techniques can provide knowledge of impending failure in high reliability applications where the risks associated with loss-of-functionality are too high to endure. The future states of the systems has been estimated based on a Particle and Kalman-filter projection of the models via a Bayesian framework. Performance of the prognostication health management algorithm during the contacts life has been quantified using performance evaluation metrics. Model predictions have been correlated with experimental data. Prognostic metrics including Prognostic Horizon (PH), alpha-Lamda (α-λ), and Relative Accuracy have been used to assess the performance of the damage proxies and a comparison of the two models made

Bushing diagnosis using artificial intelligence and dissolved gas analysis

This dissertation is a study of artificial intelligence for diagnosing the condition of high voltage bushings. The techniques include neural networks, genetic algorithms, fuzzy set theory, particle swarm optimisation, multi-classifier systems, factor analysis, principal component analysis, multidimensional scaling, data-fusion techniques, automatic relevance determination and autoencoders. The classification is done using Dissolved Gas Analysis (DGA) data based on field experience together with criteria from IEEEc57.104 and IEC60599. A review of current literature showed that common methods for the diagnosis of bushings are: partial discharge, DGA, tan- (dielectric dissipation factor), water content in oil, dielectric strength of oil, acidity level (neutralisation value), visual analysis of sludge in suspension, colour of the oil, furanic content, degree of polymerisation (DP), strength of the insulating paper, interfacial tension or oxygen content tests. All the methods have limitations in terms of time and accuracy in decision making. The fact that making decisions using each of these methods individually is highly subjective, also the huge size of the data base of historical data, as well as the loss of skills due to retirement of experienced technical staff, highlights the need for an automated diagnosis tool that integrates information from the many sensors and recalls the historical decisions and learns from new information. Three classifiers that are compared in this analysis are radial basis functions (RBF), multiple layer perceptrons (MLP) and support vector machines (SVM). In this work 60699 bushings were classified based on ten criteria. Classification was done based on a majority vote. The work proposes the application of neural networks with particle swarm optimisation (PSO) and genetic algorithms (GA) to compensate for missing data in classifying high voltage bushings. The work also proposes the application of fuzzy set theory (FST) to diagnose the condition of high voltage bushings. The relevance and redundancy detection methods were able to prune the redundant measured variables and accurately diagnose the condition of the bushing with fewer variables. Experimental results from bushings that were evaluated in the field verified the simulations. The results of this work can help to develop real-time monitoring and decision making tools that combine information from chemical, electrical and mechanical measurements taken from bushings

Fuzzy Systems

This book presents some recent specialized works of theoretical study in the domain of fuzzy systems. Over eight sections and fifteen chapters, the volume addresses fuzzy systems concepts and promotes them in practical applications in the following thematic areas: fuzzy mathematics, decision making, clustering, adaptive neural fuzzy inference systems, control systems, process monitoring, green infrastructure, and medicine. The studies published in the book develop new theoretical concepts that improve the properties and performances of fuzzy systems. This book is a useful resource for specialists, engineers, professors, and students

Biopolymers in Drug Delivery and Regenerative Medicine

Biopolymers including natural (e.g., polysaccharides, proteins, gums, natural rubbers, bacterial polymers), synthetic (e.g., aliphatic polyesters and polyphosphoester), and biocomposites are of paramount interest in regenerative medicine, due to their availability, processability, and low toxicity. Moreover, the structuration of biopolymer-based materials at the nano- and microscale along with their chemical properties are crucial in the engineering of advanced carriers for drug products. Finally, combination products including or based on biopolymers for controlled drug release offer a powerful solution to improve the tissue integration and biological response of these materials. Understanding the drug delivery mechanisms, efficiency, and toxicity of such systems may be useful for regenerative medicine and pharmaceutical technology. The main aim of the Special Issue on “Biopolymers in Drug Delivery and Regenerative Medicine” is to gather recent findings and current advances on biopolymer research for biomedical applications, particularly in regenerative medicine, wound healing, and drug delivery. Contributions to this issue can be as original research or review articles and may cover all aspects of biopolymer research, ranging from the chemical synthesis and characterization of modified biopolymers, their processing in different morphologies and hierarchical structures, as well as their assessment for biomedical uses