Download or read book Data Driven Modeling Monitoring and Control for Smart and Connected Systems written by Chao Wang and published by . This book was released on 2019 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information revolution is turning modern engineering systems into smart and connected systems. The smart and connected systems are defined by three characteristics: tangible physical components that comprise the system, connectivity among components that enables data acquisition and sharing, and smart data analytics and decision making capability. Examples of smart and connected systems include GM's OnStar® tele-service system and the InSite® tele-monitoring system from GE. The unprecedented data availability in smart and connected systems provides significant opportunities for data analytics. For example, since we have observations from potentially a very large number of similar units, we can compare their operations, share the information, and extract some common knowledge to enable accurate prediction and control at the individual level. In addition, for a complex system such as multistage manufacturing processes, we can collect synchronized data from multiple stations within the system so that we can identify the operational relationships among these stations. Such relationship can enable better process control. On the other hand, the tremendous data volume and types also reveal critical challenges. First, the high dimensional data with heterogeneity often poses difficulties in sharing common information within/across similar units/processes in the smart and connected systems. This problem becomes more severe when the system under the start-up period, where insufficient data and experience could result in the deficiency of data driven approaches. Second, the non-Gaussian data and non-linear relationship among various units impede the quantitative description of the inter-relationship of processes in the smart and connected systems. Although existing non-parametric methods, e.g., Kriging, can deal with these situations to some extent, limited description power (focus on mean value prediction) and lack of physical interpretation are the common drawbacks in these methods. Moreover, the real time monitoring and control for the smart and connected systems require efficient and scalability algorithms and strategies to meet the rapid and large scale response under advanced sensing and data acquisition environment. Lastly, the efficient control of the smart and connected systems also becomes challenging due to the complex relationship among units. Data-driven methods are required to meet the exigent demands for effectively formulating and solving the control problem. To address the issues listed above, four tasks are investigated in this dissertation under different applications in the smart and connected systems. [1] Transfer learning among heterogeneous multistage manufacturing processes. A series of data analytical methods for modeling and learning inter-relationships among product quality characteristics in multistage connected manufacturing processes are developed. The methods offer a rigorous way to reveal commonalities among heterogeneous data from different manufacturing processes to benefit the learning in complex connected manufacturing processes. [2] Statistical modeling and inference for Key Performance Indicators (KPI) in production systems. A surrogate model for inference and prediction at distribution level of different KPIs is developed. This model utilizes the pair-copula construction to capture the non-linear association in the non-Gaussian data. [3] Real time contamination detection in water distribution network. A contamination source identification framework is proposed for real time tracking and detection of contamination released in the urban water distribution network. The framework utilizes the Bayesian theory to sequentially update the posterior probability for determining the contamination source upon very limited sensor readings. [4] Control of KPIs in manufacturing production systems. The KPI control problem is formulated as a stochastic optimization problem, where the noise distribution in the cost function depends on the decision variables. The standard uniform distributions are employed to link the KPI relationship surrogate model and the objective function to efficiently solve the KPI control problem. The proposed methods can be applied to a broad range of data analytics problems, and the emerging challenges in modeling, monitoring and control of smart and connected systems can be effectively addressed.

Download or read book Data driven Modeling Prognosis and Control of Discrete Events in Smart and Connected Systems written by Akash Deep (Ph.D.) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid advances in data acquisition, communication, storage, and processing technologies in recent years have enabled the transformation of conventional industrial equipment into smart and connected systems as well as the automation of business processes. The wealth of data extracted from these systems presents unprecedented opportunities for applying advanced data analytics to enhance industrial operations and services. Specifically, the data analytics-driven improvements in system performance can be achieved through effective monitoring of the evolution of the system's condition, modeling of complex relationships between industrial processes, accurate and individualized prognosis, and subsequently, using these insights to make intelligent optimal decisions. In this context, the proposed research focuses on a particular kind of data (known as "event data") which are commonly present in data gathered from industrial systems and processes. An event marks the occurrence of an underlying phenomenon in the temporal domain such as critical warnings, failures, maintenance actions, customer interactions, etc. From an analytics perspective, event data present several significant challenges including, but not limited to, non-normality, censoring, heterogeneity, and associations between different processes. This research simultaneously addresses multiple challenges, and the following tasks are pursued. (a) Individualized prognosis of in-field units in presence of unobserved heterogeneity - a method is proposed to dynamically update the heterogeneity parameter and make unit-specific predictions of a succeeding event, (b) Modeling and prognosis in presence of multi-type events - first, a copula-based framework is proposed for prognosis in presence of censoring, and second, a multivariate stochastic process is proposed to capture impact between event-types, (c) Monitoring of event sequences with unknown event-types - an approach utilizing multiple survival models for monitoring is proposed, and (d) Modeling, prognosis, and control of hard failures - a hidden Markov model-based degradation model is proposed for predicting hard failures. Thereafter, a partially observed Markov decision process is employed to recommend optimal maintenance actions. While these methods have been developed in the context of industrial systems and services, they can be generalized and applied to other business contexts as well.

Download or read book Data Driven Modeling Filtering and Control written by Carlo Novara and published by Control, Robotics and Sensors. This book was released on 2019-09 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using important examples, this book showcases the potential of the latest data-based and data-driven methodologies for filter and control design. It discusses the most important classes of dynamic systems, along with the statistical and set membership analysis and design frameworks.

Download or read book Dynamic Modeling Predictive Control and Performance Monitoring written by Biao Huang and published by Springer Science & Business Media. This book was released on 2008-04-11 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: A typical design procedure for model predictive control or control performance monitoring consists of: 1. identification of a parametric or nonparametric model; 2. derivation of the output predictor from the model; 3. design of the control law or calculation of performance indices according to the predictor. Both design problems need an explicit model form and both require this three-step design procedure. Can this design procedure be simplified? Can an explicit model be avoided? With these questions in mind, the authors eliminate the first and second step of the above design procedure, a “data-driven” approach in the sense that no traditional parametric models are used; hence, the intermediate subspace matrices, which are obtained from the process data and otherwise identified as a first step in the subspace identification methods, are used directly for the designs. Without using an explicit model, the design procedure is simplified and the modelling error caused by parameterization is eliminated.

Download or read book Recent Developments in Model Based and Data Driven Methods for Advanced Control and Diagnosis written by Didier Theilliol and published by Springer Nature. This book was released on 2023-07-15 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book consists of recent works on several axes either with a more theoretical nature or with a focus on applications, which will span a variety of up-to-date topics in the field of systems and control. The main market area of the contributions include: Advanced fault-tolerant control, control reconfiguration, health monitoring techniques for industrial systems, data-driven diagnosis methods, process supervision, diagnosis and control of discrete-event systems, maintenance and repair strategies, statistical methods for fault diagnosis, reliability and safety of industrial systems artificial intelligence methods for control and diagnosis, health-aware control design strategies, advanced control approaches, deep learning-based methods for control and diagnosis, reinforcement learning-based approaches for advanced control, diagnosis and prognosis techniques applied to industrial problems, Industry 4.0 as well as instrumentation and sensors. These works constitute advances in the aforementioned scientific fields and will be used by graduate as well as doctoral students along with established researchers to update themselves with the state of the art and recent advances in their respective fields. As the book includes several applicative studies with several multi-disciplinary contributions (deep learning, reinforcement learning, model-based/data-based control etc.), the book proves to be equally useful for the practitioners as well industrial professionals.

Download or read book Handbook of Research on Data Driven Mathematical Modeling in Smart Cities written by Pramanik, Sabyasachi and published by IGI Global. This book was released on 2023-02-17 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: A smart city utilizes ICT technologies to improve the working effectiveness, share various data with the citizens, and enhance political assistance and societal wellbeing. The fundamental needs of a smart and sustainable city are utilizing smart technology for enhancing municipal activities, expanding monetary development, and improving citizens’ standards of living. The Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities discusses new mathematical models in smart and sustainable cities using big data, visualization tools in mathematical modeling, machine learning-based mathematical modeling, and more. It further delves into privacy and ethics in data analysis. Covering topics such as deep learning, optimization-based data science, and smart city automation, this premier reference source is an excellent resource for mathematicians, statisticians, computer scientists, civil engineers, government officials, students and educators of higher education, librarians, researchers, and academicians.

Download or read book Machine Learning Approach for Cloud Data Analytics in IoT written by Sachi Nandan Mohanty and published by John Wiley & Sons. This book was released on 2021-07-14 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Approach for Cloud Data Analytics in IoT The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology. Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.

Download or read book Data Driven Smart Manufacturing Technologies and Applications written by Weidong Li and published by Springer Nature. This book was released on 2021-02-20 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reports innovative deep learning and big data analytics technologies for smart manufacturing applications. In this book, theoretical foundations, as well as the state-of-the-art and practical implementations for the relevant technologies, are covered. This book details the relevant applied research conducted by the authors in some important manufacturing applications, including intelligent prognosis on manufacturing processes, sustainable manufacturing and human-robot cooperation. Industrial case studies included in this book illustrate the design details of the algorithms and methodologies for the applications, in a bid to provide useful references to readers. Smart manufacturing aims to take advantage of advanced information and artificial intelligent technologies to enable flexibility in physical manufacturing processes to address increasingly dynamic markets. In recent years, the development of innovative deep learning and big data analytics algorithms is dramatic. Meanwhile, the algorithms and technologies have been widely applied to facilitate various manufacturing applications. It is essential to make a timely update on this subject considering its importance and rapid progress. This book offers a valuable resource for researchers in the smart manufacturing communities, as well as practicing engineers and decision makers in industry and all those interested in smart manufacturing and Industry 4.0.

Download or read book Learning Data driven Models for Decision making in Intelligent Physical Systems written by Nurali Virani and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent physical systems use machine learning for a variety of tasks from health monitoring to control. As the dependence on autonomous decision-making agents increases, it is of importance to understand and quantify the uncertainty associated with the decisions from machine learning frameworks. In order to facilitate the interaction with human agents (e.g., maintenance engineers and medical doctors) as well as to enable robust control for safety (e.g., autonomous navigation and sensor network adaptation), density estimation enables quantification of uncertainty in the output of a learning framework. In statistical learning, density estimation is a core problem, where the objective is to identify the underlying distribution from which the data are being generated. In this work, density estimation is established as a practical tool for data-driven modeling. A new and simple technique for density estimation is developed using concepts from statistical learning and optimization theory. Along with detection, classification, estimation, and tracking, which are crucial in learning and control, these models can also quantify uncertainty in their outputs.This dissertation uses density estimation for developing new methods to solve practical problems of learning and decision-making. A few restrictive assumptions have been eliminated from these problems, yet tractable and accurate methods have been developed in this research. Specifically, in the sequential classification problem, the naive Bayes' assumption of conditional independence between measurements, given state, is relaxed. A novel technique to learn a unified context from multi-modal sensor data is developed. This knowledge of context is used to achieve tractable and accurate multi-modal sensor fusion, which cannot be achieved using the naive Bayes' assumption. Additionally, the context-aware measurement models are also used for unifying state estimation and dynamic sensor selection problems in a stochastic control framework. In sequential hypothesis testing with streaming data, the assumption that the observation sequence is independent and identically distributed (IID) has been removed by developing sequential tests for Markov models of time-series data. Further, density estimation has been used to create Markov models from multidimensional time-series data by developing a unified formulation for alphabet-size selection and measurement-space partitioning. In sequential tracking, the assumption of additive Gaussian noise has been eliminated by learning nonparametric density estimation-based measurement models, which can capture all the uncertainties in a given set of data. These measurement models have been used for state estimation and tracking with particle filters. In a sequential measurement model learning setting, the labels provided by instructors are allowed to be incorrect as the assumption of the instructor being perfect has not been used. A recursive density estimation algorithm has been developed and analyzed to show that correct models can be obtained even with noisy labels. In physical systems, the assumptions noted above, usually do not hold due to causal dependencies, physical constraints, operating conditions, various uncertainties, etc., and hence have been selectively relaxed. The theoretical frameworks developed in this research have been validated using simulations and real-world experiments. The practical applications covered in this dissertation include target detection and classification in border surveillance, indoor localization of smart wheelchairs with user-assistance for safety during navigation, and detection of combustion instability using streaming data. These widely differing real-world problems have been used to illustrate the general applicability of the results developed in this thesis. It is envisioned that the formulations and results from this dissertation will be useful in data-driven modeling, real-time decision-making, and robust control of physical systems, making them more intelligent.

Download or read book Modeling Analysis and Control of Smart Energy Systems written by Naoui, Mohamed and published by IGI Global. This book was released on 2024-08-08 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increasing demand for cleaner and more intelligent energy solutions poses a challenge that resonates across academic, engineering, and policymaking spheres. The complexity of integrating renewable energy sources, energy storage solutions, and advanced communication technologies demands a comprehensive understanding, rigorous analysis, and innovative control strategies. The academic community, in particular, seeks a guiding light through this intricate maze of evolving energy dynamics. Modeling, Analysis, and Control of Smart Energy Systems is a groundbreaking publication that offers more than theoretical exploration; it is a roadmap equipped with the knowledge and tools required to shape the future of energy systems. From laying conceptual foundations to unraveling real-world case studies, the book seamlessly bridges the gap between theory and application. Its comprehensive coverage of mathematical modeling, dynamic system analysis, intelligent control strategies, and the integration of renewable energy sources positions it as an authoritative reference for researchers, engineers, and policymakers alike.

Download or read book Data Driven Modeling and Pattern Recognition of Dynamical Systems written by Pritthi Chattopadhyay and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Human-engineered complex systems need to be monitored consistently to ensuretheir safety and efficiency, which might be affected due to degradation over timeor unanticipated disturbances. For systems that change at a fast time scale, insteadof active health monitoring, preventative system design is more feasible andeffective. Both active health monitoring and preventative system design can bedone using physics-based or data-driven models. In comparison to physics-basedmodels, data-driven models do not require knowledge of the underlying systemdynamics; they determine the relation between the relevant input and output variablesfrom a training data set. This is useful when there is lack of understandingof the system dynamics or the developed models are inadequate. One such scenariois combustion, where the difficulties include nonlinear dynamics involvingseveral input parameters; existence of bifurcations in the dynamic behavior andextremely high sensitivity of the combustor behavior to even small changes insome of the design parameters. Similarly, for batteries, sufficient knowledge of theelectrochemical characteristics is necessary to develop models for parameter identification at different operating points of the nonlinear battery dynamics. Thisdissertation develops dynamic data-driven models for combustor design and batteryhealth monitoring, using concepts of machine learning and statistics, whichdo not require much knowledge of the underlying system dynamics.But the performance of a data-driven algorithm depends on many factors namely:1. Availability of training data which covers all events of interest. For applicationsinvolving time series data, each individual time series must also besufficiently long, to encompass the dynamics of the underlying system foreach event.2. The quality of extracted features, i.e. whether they capture all the informationabout the system.3. The relation between the relevant input and output variables remaining constantduring the time the algorithm is being trained.Hence, the second part of the dissertation develops an unsupervised algorithm forscenarios where condition (iii) might not hold; quanties the eect of the nonconformityof condition (i) on the performance of an algorithm and proposes afeature extraction algorithm to ensure conformity of condition (ii).

Download or read book Data driven Modeling Control and Tools for Cyber physical Energy Systems written by Madhur Behl and published by . This book was released on 2015 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop Model-IQ, a methodology for analysis of uncertainty propagation for building inverse modeling and controls. Given a grey-box model structure and real input data from a temporary set of sensors, Model-IQ evaluates the effect of the uncertainty propagation from sensor data to model accuracy and to closed-loop control performance. We also developed a statistical method to quantify the bias in the sensor measurement and to determine near optimal sensor placement and density for accurate data collection for model training and control. Using a real building test-bed, we show how performing an uncertainty analysis can reveal trends about inverse model accuracy and control performance, which can be used to make informed decisions about sensor requirements and data accuracy.

Download or read book Scalable Data driven Modeling and Analytics for Smart Buildings written by Srinivasan Iyengar and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Buildings account for over 40% of the energy and 75% of the electricity usage. Thus, by reducing our energy footprint in buildings, we can improve our overall energysustainability. Further, the proliferation of networked sensors and IoT devices in recent years have enabled monitoring of buildings to provide data at various granularity. For example, smart plugs monitor appliance level usage inside the house, while solar meters monitor residential rooftop solar installations. Furthermore, smart meters record energy usage at a grid-scale. In this thesis, I argue that data-driven modeling applied to the IoT data from a smart building, at varying granularity, in association with third party data can help to understand and reduce human energy consumption. I present four data-driven modeling approaches - that use sophisticated techniques from Machine Learning, Optimization, and Time Series Analysis - applied at different granularities. First, I study IoT devices inside the house and discuss an approach called NIMD that automatically models individual electrical loads found in a household. The analytical model resulting from this approach can be used in several applications. For example, these models can improve the performance of NILM algorithms to disaggregate loads in a given household. Further, faulty or energy-inefficient appliances can be identified by observing deviations in model parameters over its lifetime. Second, I examine data from solar meters and present a machine learning framework called SolarCast to forecast energy generation from residential rooftop installations. The predictions enable exploiting the benefits of locally-generated solar energy. Third, I employ a sensorless approach utilizing a graphical model representation to report city-scale photovoltaic panel health and identify anomalies in solar energy production. Immediate identification of faults maximizes the solar investment by aiding in optimal operational performance. Finally, I focus on grid-level smart meter data and use correlations between energy usage and external weather to derive probabilistic estimates of energy, which is leveraged to identify the least efficient buildings from a large population along with the underlying cause of energy inefficiency. The identified homes can be targeted for custom energy efficiency programs.

Download or read book Data Intensive Industrial Asset Management written by Farhad Balali and published by Springer Nature. This book was released on 2020-01-22 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a step by step Asset Health Management Optimization Approach Using Internet of Things (IoT). The authors provide a comprehensive study which includes the descriptive, diagnostic, predictive, and prescriptive analysis in detail. The presentation focuses on the challenges of the parameter selection, statistical data analysis, predictive algorithms, big data storage and selection, data pattern recognition, machine learning techniques, asset failure distribution estimation, reliability and availability enhancement, condition based maintenance policy, failure detection, data driven optimization algorithm, and a multi-objective optimization approach, all of which can significantly enhance the reliability and availability of the system.

Download or read book Intelligent Data Driven Modelling and Optimization in Power and Energy Applications written by B Rajanarayan Prusty and published by CRC Press. This book was released on 2024-05-09 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive understanding of how intelligent data-driven techniques can be used for modelling, controlling, and optimizing various power and energy applications. It aims to develop multiple data-driven models for forecasting renewable energy sources and to interpret the benefits of these techniques in line with first-principles modelling approaches. By doing so, the book aims to stimulate deep insights into computational intelligence approaches in data-driven models and to promote their potential applications in the power and energy sectors. Its key features include: an exclusive section on essential preprocessing approaches for the data-driven model a detailed overview of data-driven model applications to power system planning and operational activities specific focus on developing forecasting models for renewable generations such as solar PV and wind power, and showcasing the judicious amalgamation of allied mathematical treatments such as optimization and fractional calculus in data-driven model-based frameworks This book presents novel concepts for applying data-driven models, mainly in the power and energy sectors, and is intended for graduate students, industry professionals, research, and academic personnel.

Download or read book Data Driven Strategies written by Wang Jianhong and published by CRC Press. This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finding exciting and efficient ways to integrate data into control theory has been a problem of great interest. As most of the classical contributions in control strategy rely on model description, the issue of finding such a model from measured data, i.e., system identification, has become mature research filed.

Download or read book Data Driven Mathematical Modeling in Agriculture written by Sabyasachi Pramanik and published by CRC Press. This book was released on 2024-08-23 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: The research in this book looks at the likelihood and level of use of implemented technological components with regard to the adoption of different precision agricultural technologies. To identify the variables affecting farmers' choices to embrace more precise technology, zero-inflated Poisson and negative binomial count data regression models are utilized. Outcomes from the count data analysis of a random sample of various farm operators show that various aspects, including farm dimension, farmer demographics, soil texture, urban impacts, farmer position of liabilities, and position of the farm in a state, were significantly associated with the approval severity and likelihood of precision farming technologies. Technical topics discussed in the book include: Precision agriculture Machine learning Wireless sensor networks IoT Deep learning