Wind turbine condition assessment through power curve copula modeling

Power curves constructed from wind speed and active power output measurements provide an established method of analyzing wind turbine performance. In this paper it is proposed that operational data from wind turbines are used to estimate bivariate probability distribution functions representing the power curve of existing turbines so that deviations from expected behavior can be detected. Owing to the complex form of dependency between active power and wind speed, which no classical parameterized distribution can approximate, the application of empirical copulas is proposed; the statistical theory of copulas allows the distribution form of marginal distributions of wind speed and power to be expressed separately from information about the dependency between them. Copula analysis is discussed in terms of its likely usefulness in wind turbine condition monitoring, particularly in early recognition of incipient faults such as blade degradation, yaw and pitch errors

Warranty Data Analysis: A Review

Warranty claims and supplementary data contain useful information about product quality and reliability. Analysing such data can therefore be of benefit to manufacturers in identifying early warnings of abnormalities in their products, providing useful information about failure modes to aid design modification, estimating product reliability for deciding on warranty policy and forecasting future warranty claims needed for preparing fiscal plans. In the last two decades, considerable research has been conducted in warranty data analysis (WDA) from several different perspectives. This article attempts to summarise and review the research and developments in WDA with emphasis on models, methods and applications. It concludes with a brief discussion on current practices and possible future trends in WDA

Hidden Markov Models and their Application for Predicting Failure Events

We show how Markov mixed membership models (MMMM) can be used to predict the degradation of assets. We model the degradation path of individual assets, to predict overall failure rates. Instead of a separate distribution for each hidden state, we use hierarchical mixtures of distributions in the exponential family. In our approach the observation distribution of the states is a finite mixture distribution of a small set of (simpler) distributions shared across all states. Using tied-mixture observation distributions offers several advantages. The mixtures act as a regularization for typically very sparse problems, and they reduce the computational effort for the learning algorithm since there are fewer distributions to be found. Using shared mixtures enables sharing of statistical strength between the Markov states and thus transfer learning. We determine for individual assets the trade-off between the risk of failure and extended operating hours by combining a MMMM with a partially observable Markov decision process (POMDP) to dynamically optimize the policy for when and how to maintain the asset.Comment: Will be published in the proceedings of ICCS 2020; @Booklet{EasyChair:3183, author = {Paul Hofmann and Zaid Tashman}, title = {Hidden Markov Models and their Application for Predicting Failure Events}, howpublished = {EasyChair Preprint no. 3183}, year = {EasyChair, 2020}

Energy rating of a water pumping station using multivariate analysis

Among water management policies, the preservation and the saving of energy demand in water supply and treatment systems play key roles. When focusing on energy, the customary metric to determine the performance of water supply systems is linked to the definition of component-based energy indicators. This approach is unfit to account for interactions occurring among system elements or between the system and its environment. On the other hand, the development of information technology has led to the availability of increasing large amount of data, typically gathered from distributed sensor networks in so-called smart grids. In this context, data intensive methodologies address the possibility of using complex network modeling approaches, and advocate the issues related to the interpretation and analysis of large amount of data produced by smart sensor networks. In this perspective, the present work aims to use data intensive techniques in the energy analysis of a water management network. The purpose is to provide new metrics for the energy rating of the system and to be able to provide insights into the dynamics of its operations. The study applies neural network as a tool to predict energy demand, when using flowrate and vibration data as predictor variables

Digital system of quarry management as a SAAS solution: mineral deposit module

Purpose. Improving the efficiency of functioning the mining enterprises and aggregation of earlier obtained results into a unified digital system of designing and operative management by quarry operation. Methods. Both the traditional (analysis of scientific and patent literature, analytical methods of deposit parameters research, analysis of experience and exploitation of quarries, conducting the passive experiment and processing the statistical data) and new forms of scientific research - deposit modeling on the basis of classical and neural network methods of approximation – are used in the work. For the purpose of the software product realization on the basis of cloud technologies, there were used: for back-end implementation – server-based scripting language php; for the front-end – multi-paradigm programming language javascript, javascript framework jQuery and asynchronous data exchange technology Ajax. Findings. The target audience of the system has been identified, SWOT-analysis has been carried out, conceptual directions of 3D-quarry system development have been defined. The strategies of development and promotion of the software product, as well as the strategies of safety and reliability of the application both for the client and the owner of the system have been formulated. The modular structure of the application has been developed, and the system functions have been divided to implement both back-end and front-end applications. The Mineral Deposit Module has been developed: the geological structure of the deposit has been simulated and its block model has been constructed. It has been proved that the use of neural network algorithms does not give an essential increase in the accuracy of the block model for the deposits of 1 and 2 groups in terms of the geological structure complexity. The possibility and prospects of constructing the systems for subsoil users on the basis of cloud technologies and the concept of SaaS have been substantiated. Originality. For the first time, the modern software products for solving the problems of designing and operational management of mining operations have been successfully developed on the basis of the SaaS concept. Practical implications. The results are applicable for enterprises-subsoil users, working with deposits of 1 and 2 groups in terms of the geological structure complexity: design organizations, as well as mining and processing plants.Мета. Підвищення ефективності функціонування гірничорудних підприємств та агрегація раніше отриманих результатів в єдину цифрову систему проектування і оперативного управління роботою кар’єрів. Методика. У роботі використані як традиційні (аналіз науково-патентної літератури, аналітичні методи дослідження параметрів родовища, аналіз досвіду й експлуатації кар’єрів, проведення пасивного експерименту та статистичної обробки даних), так і нові форми наукового дослідження – моделювання родовища на основі класичних і нейромережевих методів апроксимації. Для реалізації програмного продукту на основі хмарних технологій використані: для реалізації back-end – серверна скриптова мова програмування php; для front-end – мультипарадігменна мова програмування javascript, javascript framework jQuery і технологія асинхронного обміну даними Ajax. Результати. Виявлено цільову аудиторію системи, проведено SWOT-аналіз, визначено концептуальні напрями розвитку системи 3D-кар’єр, розроблені стратегії розвитку та просування програмного продукту, розроблені стратегії безпеки й надійності додатки як для клієнта, так і власника системи. Розроблено модульну структуру програми, вироблено розподіл функцій системи для реалізації як back-end і front-end додатки. Розроблено модуль “Родовище”: проведено моделювання геологічної структури родовища та побудована його блокова модель. Доведено, що використання нейромережевих алгоритмів не дає принципового підвищення точності блокової моделі для родовищ 1 і 2 груп за складністю геологічної будови. Виявлено недоліки нейромережевих алгоритмів, такі як високі витрати обчислювальних ресурсів сервера і проблеми візуалізації великих масивів геоданих при використанні web-рішень, знайдені шляхи їх вирішення. Доведено можливість і перспективність побудови систем для надрокористувачів на основі хмарних технологій і концепції SaaS. Наукова новизна. Вперше на основі концепції ASP успішно побудовані сучасні програмні продукти для вирішення завдань проектування та оперативного керування гірничими роботами. Практична значимість. Результати корисні для підприємств-надрокористувачів, які працюють з родовищами 1 і 2 груп за складністю геологічної будови – проектних організацій і ГЗК.Цель. Повышение эффективности функционирования горнорудных предприятий и агрегация ранее полученных результатов в единую цифровую систему проектирования и оперативного управления работой карьеров. Методика. В работе использованы как традиционные (анализ научно-патентной литературы, аналитические методы исследования параметров месторождения, анализ опыта и эксплуатации карьеров, проведение пассивного эксперимента и статистической обработкой данных), так и новые формы научного исследования – моделирование месторождения на основе классических и нейросетевых методов аппроксимации. Для реализации программного продукта на основе облачных технологий использованы: для реализации back-end – серверный скриптовый язык программирования php; для front-end – мультипарадигменный язык программирования javascript, javascript framework jQuery и технология асинхронного обмена данными Ajax. Результаты. Выявлена целевая аудитория системы, проведен SWOT-анализ, определены концептуальные направления развития системы 3D-карьер, разработаны стратегии развития и продвижения программного продукта, разработаны стратегии безопасности и надежности приложения как для клиента, так и владельца системы. Разработана модульная структура приложения, произведено деление функций системы для реализации как back-end и front-end приложения. Разработан модуль “Месторождение”: проведено моделирование геологической структуры месторождения и построена его блочная модель. Доказано, что использование нейросетевых алгоритмов не дает принципиального повышения точности блочной модели для месторождений 1 и 2 групп по сложности геологического строения. Выявлены недостатки нейросетевых алгоритмов, такие как высокие затраты вычислительных ресурсов сервера и проблемы визуализации больших массивов геоданных при использовании web-решений, найдены пути их решения. Доказана возможность и перспективность построения систем для недропользователей на основе облачных технологий и концепции SaaS. Научная новизна. Впервые на основе концепции ASP успешно построены современные программные продукты для решения задач проектирования и оперативного управления горными работами. Практическая значимость. Результаты применимы для предприятий-недропользователей, работающих с месторождениями 1 и 2 групп по сложности геологического строения – проектных организаций и ГОКов.We express our profound gratitude to A.B. Naizabekov for his assistance in scientific research, to A.F. Tsekhovoy, P.A. Tsekhovoy, D.Sh. Akhmedov, V. V. Yankovenko and D.V. Nikitas for scientific advice in implementation of the program code. The research was carried out within the framework of the initiative research theme “Improving the Efficiency of Mining Enterprises” on the basis of the RSE at the Rudny Industrial Institute of the Ministry of Education and Science of the Republic of Kazakhstan

Modeling Quantum Mechanical Observers via Neural-Glial Networks

We investigate the theory of observers in the quantum mechanical world by using a novel model of the human brain which incorporates the glial network into the Hopfield model of the neural network. Our model is based on a microscopic construction of a quantum Hamiltonian of the synaptic junctions. Using the Eguchi-Kawai large N reduction, we show that, when the number of neurons and astrocytes is exponentially large, the degrees of freedom of the dynamics of the neural and glial networks can be completely removed and, consequently, that the retention time of the superposition of the wave functions in the brain is as long as that of the microscopic quantum system of pre-synaptics sites. Based on this model, the classical information entropy of the neural-glial network is introduced. Using this quantity, we propose a criterion for the brain to be a quantum mechanical observer.Comment: 24 pages, published versio

Contextualized property market models vs. Generalized mass appraisals: An innovative approach

The present research takes into account the current and widespread need for rational valuation methodologies, able to correctly interpret the available market data. An innovative automated valuation model has been simultaneously implemented to three Italian study samples, each one constituted by two-hundred residential units sold in the years 2016-2017. The ability to generate a "unique" functional form for the three different territorial contexts considered, in which the relationships between the influencing factors and the selling prices are specified by different multiplicative coefficients that appropriately represent the market phenomena of each case study analyzed, is the main contribution of the proposed methodology. The method can provide support for private operators in the assessment of the territorial investment conveniences and for the public entities in the decisional phases regarding future tax and urban planning policies