A committee machine gas identification system based on dynamically reconfigurable FPGA

This paper proposes a gas identification system based on the committee machine (CM) classifier, which combines various gas identification algorithms, to obtain a unified decision with improved accuracy. The CM combines five different classifiers: K nearest neighbors (KNNs), multilayer perceptron (MLP), radial basis function (RBF), Gaussian mixture model (GMM), and probabilistic principal component analysis (PPCA). Experiments on real sensors' data proved the effectiveness of our system with an improved accuracy over individual classifiers. Due to the computationally intensive nature of CM, its implementation requires significant hardware resources. In order to overcome this problem, we propose a novel time multiplexing hardware implementation using a dynamically reconfigurable field programmable gate array (FPGA) platform. The processing is divided into three stages: sampling and preprocessing, pattern recognition, and decision stage. Dynamically reconfigurable FPGA technique is used to implement the system in a sequential manner, thus using limited hardware resources of the FPGA chip. The system is successfully tested for combustible gas identification application using our in-house tin-oxide gas sensors

Selective Detection of Volatile Organic Compounds Using Metal Oxide Sensor Arrays

Selective detection of organic contaminant using widely available and inexpensive metal oxide sensors has the potential to be used in various robotic platforms for navigation, harmful chemical leak detection, mobile environmental monitoring, etc. Selective gas detection in real world environments using easily available sensors has not been reported and can be used in many industries. A sensor system using only four commercially available sensors with accompanying signal conditioning and clustering algorithm capable of discriminatory detection of chemical marker is possible. Tests have shown that temperature, humidity and concentration fluctuations can be accounted for to produce systems for real world environments. An algorithm that accounts for sensor fouling and degradation is produced to achieve a repeatability rate of ninety three percent in a simulated real world environment

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

Comparative Case Study Analysis of Combustible Dust Explosions: Determining the Need for an OSHA Combustible Dust Standard

This study examined the hazards associated with combustible dust, the need for an OSHA standard to assist in the prevention of combustible dust explosions, and the influence such a standard would have on employers in industries where combustible dust is used. The framework of this study was to compare and evaluate the performance of two companies that experienced a combustible dust explosion. Past Kentucky Occupational Safety and Health (KYOSH) inspection history was reviewed as well as all data collected prior to and after the explosions. The following information was reviewed: OSHA industry standards, OSHA Compliance Directives, NFPA codes and standards, additional consensus standards, and peer-reviewed journal articles. This study found that the most effective method of preventing a combustible dust explosion is implementing a combustible dust management program including emphasis on housekeeping and management of change. An OSHA combustible dust General Industry Standard would provide the knowledge and additional motivation to implement the necessary mitigation procedures to prevent a combustible dust explosion. However, it would be difficult to develop one single standard to cover combustible dust in every industry. One solution is that industries that are covered by additional industry consensus standards be exempt from the standard. An example would be a woodworking facility that is covered by NFPA 664, Standard for the Prevention of Fires and Explosions in Wood Processing and Woodworking Facilities. Special care would have to be taken by OSHA with regard to how combustible dust is defined, if the standard is performance-based or specification-based, small versus large businesses, and economic concerns

Partial Inerting and Minimum Ignition Energy (Mie) Prediction of Combustible Dusts

Minimum Ignition Energy (MIE) is a critical dust hazard parameter guiding elimination of ignition sources in solids handling facilities. Partial inerting is an important but underutilized mitigation technique in which MIE of a dust cloud is increased through inerting, reducing the risk of an accidental dust explosion or more accurately, a dust deflagration. This dissertation has reported advances in MIE testing and prediction to prevent and mitigate dust explosions. In this work, a novel purge add-on device to the standard MIE test apparatus was designed which facilitated purging the Hartmann tube before MIE testing. Through experimentation and CFD modeling, this dissertation has attempted to refine the existing MIE testing standard for partial inerting applications by introducing purge time as an essential parameter. The effective experimental purge time required for partial inerting testing in the MIE apparatus was determined to be > 40 s and validated through the ANSYS Fluent CFD purging model. In addition, this work has demonstrated that purging the MIE apparatus Hartmann tube before experimentation significantly affected the measured values in partially inerted atmospheres (O2 < 21 vol. %). It is recommended through this research that purging should be an essential step while MIE testing and reporting. Using this improved methodology, an accurate MIE with changing oxygen concentrations for the combustible dusts Niacin, Anthraquinone, Lycopodium clavatum and Calcium Stearate was obtained and a mathematical equation for MIE-O2 was proposed. Furthermore, Quantitative-Structure Property (QSPR) models for MIE prediction using machine learning algorithms such as Random Forests (RF) and Decision Trees (DT) were developed. A binary classification model was developed for predicting the MIE category of the combustible dusts. The results indicated good MIE predictability through the RF algorithm indicated by the Receiver Operating Characteristic – Area Under Curve (ROC-AUC) of 0.95. Additionally, RF algorithm was used to identify the molecular descriptors which most significantly affected the MIE prediction accuracy. Thus, through experimentation and modeling, this study aims to provide a scientific foundation for a partial inerting MIE test method to supplement existing testing standards (such as ASTM E2019-03) and provides a solid framework for MIE prediction of combustible dusts

English for Study and Work: Coursebook in 4 books. Book 2 Obtaining and Processing Information for Specific Purposes

Подано всі види діяльності студентів з вивчення англійської мови, спрямовані на розвиток мовної поведінки, необхідної для ефективного спілкування в академічному та професійному середовищах. Містить завдання і вправи, типові для різноманітних академічних та професійних сфер і ситуацій. Структура організації змісту – модульна, охоплює мовні знання і мовленнєві вміння залежно від мовної поведінки. Даний модуль має на меті розвиток у студентів стратегій, умінь, навичок читання, пошуку та вилучення професійно-орієнтованої інформації, необхідної для ефективної професійної діяльності і навчання. Містить завдання і вправи, типові для академічних та професійних сфер, пов’язаних з гірництвом і розробкою родовищ корисних копалин. Зразки текстів – автентичні, різножанрові, взяті з реального життя, містять цікаву й актуальну інформацію про особливості видобутку мінеральних ресурсів в провідних країнах світу, сучасний підхід до розробки родовищ тощо. Ресурси для самостійної роботи (Частина ІІ) містять завдання та вправи для розширення словникового запасу та розвитку знань найуживанішої термінології з гірництва, що спрямовано на організацію самостійної роботи з розвитку мовленнєвих умінь, знань про корисні копалини, методи їх видобутку. За допомогою засобів діагностики студенти можуть самостійно перевірити засвоєння навчального матеріалу й оцінити свої досягнення. Призначений для студентів вищих навчальних закладів, зокрема технічних університетів. Може використовуватися для самостійного вивчення англійської мови викладачами, фахівцями і науковцями різних галузей

Risk analysis of the LHC underground area: fire risk due to faulty electrical equipment

The European Organisation for Nuclear Research (CERN) in Geneva, Switzerland, is currently building the latest generation of particle accelerators, the LHC (Large Hadron Collider). The machine is housed in a circular tunnel of 27 km of circumference and is situated approximately 100 metres beneath the surface astride the Franco-Swiss border. Electrically induced fires in the LHC are a major concern, since an incident could present a threat to CERN personnel as well as the public. Moreover, the loss of equipment would result in significant costs and downtime. However, the amount of electrical equipment in the underground area required for operation, supervision and control of the machine is essential. Thus the present thesis is assessing the risk of fire due to faulty electrical equipment in both a qualitative as well as quantitative way. The recommendations following the qualitative analysis suggest the introduction of fire protection zones for the areas with the highest risk of fire due to a combination of possible ignition sources and combustible material in the vicinity. In order to be able to conduct regular follow-up examinations to obtain more precise results for the quantitative analysis in the future, the creation of a material data inventory and the collection of failure probability data throughout the lifetime of the LHC are recommended

Prediction of planned outages of a power transformer using dissolved gas analysis and nonlinear autoregressive neural networks

Abstract: A power transformer is amongst the more expensive and critical equipment installed in the power system. Unplanned power outages as a result of transformer failure have high recovery costs, reduce the life expectancy of the equipment and interrupt continuous power supply to customers. The fault occurrence in an oil-immersed power transformer results in decomposition of mineral oil which in turn causes dissolved gases to be released. To ensure reliability and availability of power transformers, mineral oil needs to be continuous ly monitored and evaluated through condition monitoring. Dissolved Gas Analysis (DGA) was developed to measure, detect, interpret and analyse dissolved gases in mineral oil of a power transformer. Condition monitoring of transformers constitutes an essential step towards prevention of unplanned breakdowns. The literature reviewed identified limitations in DGA techniques as not all incipient fault conditions can be detected by the diagnostic techniques, which makes it difficult to develop failure probability for power transformers. Considerable efforts in research have been made, in the transformer asset management field, to develop accurate models that will provide reliable incipient fault diagnosis and predict planned outage of a power transformer based on the health condition. Studies demonstrated that the Computational Intelligence (CI) and Artificial Intelligence (AI) techniques have the ability to overcome the limitations in DGA techniques. Since there is a lack of common frameworks to determine the accurate health condition and to predict the downtime of a power transformer, this dissertation provides a critical review on mineral oil sampling processes, DGA, CI and AI techniques used to improve the fault diagnostic of a power transformer and predict the planned outage timely. Preventive maintenance is outlined by the preventive model that is built using a combination of Artificial Neural Network (ANN) with DGA techniques to detect accurate incipient fault conditions. The model uses dissolved gases in mineral oil to detect to identify incipient fault conditions in a power transformer. The ANN Multilayer Perceptron (MLP) Feedforward with Back-Propagation (BP) was built using the concentrations of key combustible dissolved gases as input layer, trained using Levenberg-Marquardt (LM) algorithm to obtain the incipient fault conditions as outputs that are enlisted in gas ratio or IEEE C57.108-2004 methods. The results obtained from comparative diagnosis presented in this work show clear improvement and accuracy in the diagnosis of transformer using a combination of ANN with Rodgers ratio or IEEE C57.108-2004 methods over using the diagnostic techniques independently. Limitations of the gas ratio and Duval Triangle methods make it difficult for the model to make decisions if data presented does not fall within the ratio range scheme and fault zones of a triangle. Unidentified diagnosis can have a severe impact on the life of the power transformers. Furthermore, CI and AI algorithms such as fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS), machine learning (ML), ANN and non-linear autoregressive neural networks have been used in various studies to overcome the limitat ions of DGA techniques. Also, the CI and AI algorithms have their limitations. Therefore, when building the preventive and predictive models for power transformers, it is important to select the combinations of methods or techniques that will circumvent most of the limitations and provide reliable and accurate outcomes. Another model that was developed for predictive maintenance is a nonlinear autoregressive exogenous model (NARX) neural network combined with IEEE C57.108.2004. The predictive model is used to study the historical data of dissolved gases and predict the future gas levels that will be used to identify the incipient fault present and predict the planned outage based on results. The proposed models in this dissertation will manage a service life of power transformers efficiently by associating gas concentration values with incipient fault conditions and serve as an early warning in the power system network by predicting planned outage of a transformer.M.Tech. (Electrical and Electronic Engineering Technology

Qualitative and Quantitative Approaches for Evaluation of Safety Risks in Coal Mines

The safety in underground coal mines continues to be a major problem in the Indian mining industry. Despite significant measures taken by the Directorate General of Mines Safety (DGMS) to reduce the number of mining accidents in underground coal mines, the number remains high. To improve the safety conditions, it has become a prerequisite to performing risk assessment for various operations in Indian mines. It is noted that many research studies conducted in the past are limited to either statistical analysis of accidents or study of single equipment or operation using qualitative and quantitative techniques. Limited work has been done to identify, analyse, and evaluate the safety risks of a complete underground coal mine in India. The present study attempts to determine the appropriate qualitative and quantitative risk assessment approaches for the evaluation of safety risks in Indian underground coal mines. This thesis addresses several important objectives as (i) to identify the type of safety risk analysis techniques suitable for evaluating various mining scenarios (ii) to identify and analyse the hazard factors and hazardous events that affects the safety in underground coal using the qualitative and quantitative approaches (iii) to evaluate the risk level (RL) of the hazardous factors/groups, hazardous events, and the overall mine using the proposed methodology. In this research work, the qualitative techniques, i.e. Failure Mode and Effects Analysis (FMEA), Workplace Risk Assessment and Control (WRAC), and the quantitative techniques, i.e. Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) were applied in an underground coal mine to identify and analyse the hazard factors and hazard events. The analysis of FMEA and WRAC results concluded that the qualitative risk assessment is easy to execute and practical as they are not dependent on the historical data; rather they need experience and close examination. On the other hand, they may yield subjective results due to instinctive human assessment. The analysis of the FTA and ETA results concluded that the quantitative risk assessment could not be performed in Indian underground coal mines due to lack of probability, exposure, and consequence data. To overcome the mentioned problems in qualitative and quantitative techniques, a methodology was proposed for evaluation of the safety risks of hazard events, hazard groups, and overall mine. The proposed methodology is the unification of fuzzy logic, VIKOR (In Serbia: VIseKriterijumska Optimizacija I Kompromisno Resenje, that means: Multi-criteria Optimization and Compromise Solution), and Analytic Hierarchy Process (AHP) techniques. Because of the imprecise nature of the information available in the mining industry, fuzzy logic was employed to evaluate the risk of each hazardous event in terms of consequence, exposure, and probability. VIKOR as was used to rank the evaluated risk of hazardous events. AHP technique helps to determine the relative importance of the risk factors. Therefore, AHP technique was integrated into the risk model so that the risk evaluation can progress from hazardous event level to hazard factor level and finally to overall mine level. To reduce the calculation time significantly and to increase the speed of the proposed risk assessment process, a user-friendly Graphical User Interface (TRAM) was developed using the C# language through Microsoft Visual Studio 2015 and .Net libraries. The proposed methodology developed in this thesis was applied to six underground coal mines. The results presented the risk level of hazard events, hazards groups and overall mine of six mines. The mine-5 has the highest risk level among the evaluated mines. The ranking order of the mines observed based on the overall risk level is mine-5> mine-1 > mine-2 > mine-3 > mine-6 > mine-4. The results of the proposed methodology were compared with DGMS proposed rapid ranking method. This is observed that the proposed methodology presents better evaluation than other approaches. This study could help the mine management to prepare safety measures based on the risk rankings obtained. It may also aid to evaluate accurate risk levels with identified hazards while preparing risk management plans