Data Driven Energy Centered Maintenance
Data Driven Energy Centered Maintenance

Author: Fadi Alshakhshir

Publisher: CRC Press

Published: 2021-07-19

Total Pages: 269

ISBN-13: 1000418332

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Over recent years, many new technologies have been introduced to drive the digital transformation in the building maintenance industry. The current trend in digital evolution involves data-driven decision making which opens new opportunities for an energy centered maintenance model. Artificial Intelligence and Machine Learning are helping the maintenance team to get to the next level of maintenance intelligence to provide real-time early warning of abnormal equipment performance. This edition follows the same methodology as the First. It provides detailed descriptions of the latest technologies associated with Artificial Intelligence and Machine Learning which enable data-driven decision-making processes about the equipment’s operation and maintenance. Technical topics discussed in the book include: Different Maintenance Types and The Need for Energy Centered Maintenance The Centered Maintenance Model Energy Centered Maintenance Process Measures of Equipment and Maintenance Efficiency and Effectiveness Data-Driven Energy Centered Maintenance Model: Digitally Enabled Energy Centered Maintenance Tasks Artificial Intelligence and Machine Learning in Energy Centered Maintenance Model Capabilities and Analytics Rules Building Management System Schematics The book contains a detailed description of the digital transformation process of most of the maintenance inspection tasks as they move away from being manually triggered. The book is aimed at building operators as well as those building automation companies who are working continuously to digitalize building operation and maintenance procedures. The benefits are reductions in the equipment failure rate, improvements in equipment reliability, increases in equipment efficiency and extended equipment lifespan.

Energy Centered Maintenance
Energy Centered Maintenance

Author: Marvin T. Howell

Publisher: CRC Press

Published: 2020-11-26

Total Pages: 250

ISBN-13: 8770222665

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Energy Centered Maintenance proves a detailed description of how to implement Energy Centered Maintenance (ECM) at any organization. It includes a new six-step technical process with detailed instructions of each of these steps explained with clear examples. Areas covered include preventative maintenance, predictive maintenance and reliability centered maintenance. ECM uses energy consumption excesses or energy waste as the primary criterion for determining specific maintenance or repair needs. Therefore, the primary purpose of this book is to provide strategies to reduce energy use by identifying equipment or items that can become energy hogs while still performing their function and prevent that from occurring. The primary reasons organizations need ECM is due to poor maintenance of energy-using systems and energy losses from motors not turning off when they should. The book includes ECM for electrical, mechanical, building transportation, HVAC, fire-fighting, water supply, drainage and storm water management systems. In some cases, ECM in data centers can help reduce energy consumption by as much as 30%. The six-step process detailed in this text will enable any organization to implement ECM in an orderly, cost effective manner thus improving your equipment and machines, lowering your energy consumption and helping save the planet.

Reliability-centered Maintenance
Reliability-centered Maintenance

Author: John Moubray

Publisher: Industrial Press Inc.

Published: 2001

Total Pages: 452

ISBN-13: 9780831131463

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Completely reorganised and comprehensively rewritten for its second edition, this guide to reliability-centred maintenance develops techniques which are practised by over 250 affiliated organisations worldwide.

Big Data Driven Supply Chain Management
Big Data Driven Supply Chain Management

Author: Nada R. Sanders

Publisher: Pearson Education

Published: 2014-05-07

Total Pages: 273

ISBN-13: 0133762823

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Master a complete, five-step roadmap for leveraging Big Data and analytics to gain unprecedented competitive advantage from your supply chain. Using Big Data, pioneers such as Amazon, UPS, and Wal-Mart are gaining unprecedented mastery over their supply chains. They are achieving greater visibility into inventory levels, order fulfillment rates, material and product delivery… using predictive data analytics to match supply with demand; leveraging new planning strengths to optimize their sales channel strategies; optimizing supply chain strategy and competitive priorities; even launching powerful new ventures. Despite these opportunities, many supply chain operations are gaining limited or no value from Big Data. In Big Data Driven Supply Chain Management, Nada Sanders presents a systematic five-step framework for using Big Data in supply chains. You'll learn best practices for segmenting and analyzing customers, defining competitive priorities for each segment, aligning functions behind strategy, dissolving organizational boundaries to sense demand and make better decisions, and choose the right metrics to support all of this. Using these techniques, you can overcome the widespread obstacles to making the most of Big Data in your supply chain — and earn big profits from the data you're already generating. For all executives, managers, and analysts interested in using Big Data technologies to improve supply chain performance.

Predictive Maintenance in Dynamic Systems
Predictive Maintenance in Dynamic Systems

Author: Edwin Lughofer

Publisher: Springer

Published: 2019-02-28

Total Pages: 564

ISBN-13: 3030056457

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This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power.

Encyclopedia of Data Science and Machine Learning
Encyclopedia of Data Science and Machine Learning

Author: Wang, John

Publisher: IGI Global

Published: 2023-01-20

Total Pages: 3296

ISBN-13: 1799892212

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Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.

IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning
IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning

Author: Joao Gama

Publisher: Springer Nature

Published: 2021-01-09

Total Pages: 317

ISBN-13: 3030667707

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This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online. The 21 full papers and 3 short papers presented in this volume were thoroughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics: IoT Streams 2020: Stream Learning; Feature Learning; ITEM 2020: Unsupervised Machine Learning; Hardware; Methods; Quantization.

Big Data Analytics Strategies for the Smart Grid
Big Data Analytics Strategies for the Smart Grid

Author: Carol L. Stimmel

Publisher: CRC Press

Published: 2014-07-25

Total Pages: 258

ISBN-13: 1482218283

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By implementing a comprehensive data analytics program, utility companies can meet the continually evolving challenges of modern grids that are operationally efficient, while reconciling the demands of greenhouse gas legislation and establishing a meaningful return on investment from smart grid deployments. Readable and accessible, Big Data Analytics Strategies for the Smart Grid addresses the needs of applying big data technologies and approaches, including Big Data cybersecurity, to the critical infrastructure that makes up the electrical utility grid. It supplies industry stakeholders with an in-depth understanding of the engineering, business, and customer domains within the power delivery market. The book explores the unique needs of electrical utility grids, including operational technology, IT, storage, processing, and how to transform grid assets for the benefit of both the utility business and energy consumers. It not only provides specific examples that illustrate how analytics work and how they are best applied, but also describes how to avoid potential problems and pitfalls. Discussing security and data privacy, it explores the role of the utility in protecting their customers’ right to privacy while still engaging in forward-looking business practices. The book includes discussions of: SAS for asset management tools The AutoGrid approach to commercial analytics Space-Time Insight’s work at the California ISO (CAISO) This book is an ideal resource for mid- to upper-level utility executives who need to understand the business value of smart grid data analytics. It explains critical concepts in a manner that will better position executives to make the right decisions about building their analytics programs. At the same time, the book provides sufficient technical depth that it is useful for data analytics professionals who need to better understand the nuances of the engineering and business challenges unique to the utilities industry.

An Introduction to Predictive Maintenance
An Introduction to Predictive Maintenance

Author: R. Keith Mobley

Publisher: Elsevier

Published: 2002-10-24

Total Pages: 451

ISBN-13: 0080478697

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This second edition of An Introduction to Predictive Maintenance helps plant, process, maintenance and reliability managers and engineers to develop and implement a comprehensive maintenance management program, providing proven strategies for regularly monitoring critical process equipment and systems, predicting machine failures, and scheduling maintenance accordingly. Since the publication of the first edition in 1990, there have been many changes in both technology and methodology, including financial implications, the role of a maintenance organization, predictive maintenance techniques, various analyses, and maintenance of the program itself. This revision includes a complete update of the applicable chapters from the first edition as well as six additional chapters outlining the most recent information available. Having already been implemented and maintained successfully in hundreds of manufacturing and process plants worldwide, the practices detailed in this second edition of An Introduction to Predictive Maintenance will save plants and corporations, as well as U.S. industry as a whole, billions of dollars by minimizing unexpected equipment failures and its resultant high maintenance cost while increasing productivity. - A comprehensive introduction to a system of monitoring critical industrial equipment - Optimize the availability of process machinery and greatly reduce the cost of maintenance - Provides the means to improve product quality, productivity and profitability of manufacturing and production plants

Handbook of Energy Audits
Handbook of Energy Audits

Author: Albert Thumann

Publisher: The Fairmont Press, Inc.

Published: 2003

Total Pages: 448

ISBN-13: 0881734233

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Now there is a comprehensive reference to provide tools on implementing an energy audit for any type of facility. Containing forms, checklists and handy working aids, this book is for anyone implementing an energy audit. Accounting procedures, rate of return, analysis and software programs are included to provide evaluation tools for audit recommendations. Technologies for electrical, mechanical and building systems are covered in detail.