Data Driven Energy Centered Maintenance
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| Author | : Fadi Alshakhshir |
| Publisher | : CRC Press |
| Total Pages | : 269 |
| Release | : 2021-07-19 |
| Genre | : Business & Economics |
| ISBN | : 1000418332 |
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.
| Author | : Marvin T. Howell |
| Publisher | : CRC Press |
| Total Pages | : 250 |
| Release | : 2020-11-26 |
| Genre | : Science |
| ISBN | : 8770222665 |
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.
| Author | : Fadi Alshakhshir |
| Publisher | : CRC Press |
| Total Pages | : 266 |
| Release | : 2021-07-20 |
| Genre | : Business & Economics |
| ISBN | : 8770223564 |
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.
| Author | : FADI. HOWELL ALSHAKHSHIR (MARVIN T.) |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2024-10-21 |
| Genre | : Business & Economics |
| ISBN | : 9788770043014 |
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.
| Author | : John Moubray |
| Publisher | : Industrial Press Inc. |
| Total Pages | : 452 |
| Release | : 2001 |
| Genre | : Business & Economics |
| ISBN | : 9780831131463 |
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.
| Author | : Neil B. Bloom |
| Publisher | : McGraw Hill Professional |
| Total Pages | : 314 |
| Release | : 2005-12-22 |
| Genre | : Technology & Engineering |
| ISBN | : 007158918X |
A properly implemented and managed RCM program can save millions in unscheduled maintenance and breakdowns. However, many have found the process daunting. Written by an expert with over 30 years of experience, this book introduces innovative approaches to simplify the RCM process such as: single vs. multiple failure analysis, hidden failures analysis, potentially critical components analysis, run-to-failure and the difference between redundant, standby, and backup functions. Included are real life examples of flawed preventive maintenance programs and how they led to disasters that could have easily been avoided. Also illustrated in detail, with real-life examples, is the step-by-step process for developing, implementing, and maintaining a premier classical RCM program. Senior management, middle management, supervisors, and craftsmen/technicians responsible for plant safety and reliability will find this book to be invaluable as a means for establishing a first class preventive maintenance program.
| Author | : Weidong Li |
| Publisher | : Springer Nature |
| Total Pages | : 218 |
| Release | : 2021-02-20 |
| Genre | : Technology & Engineering |
| ISBN | : 3030668495 |
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.
| Author | : Wang, John |
| Publisher | : IGI Global |
| Total Pages | : 3296 |
| Release | : 2023-01-20 |
| Genre | : Computers |
| ISBN | : 1799892212 |
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.
| Author | : Nada R. Sanders |
| Publisher | : Pearson Education |
| Total Pages | : 273 |
| Release | : 2014-05-07 |
| Genre | : Business & Economics |
| ISBN | : 0133762823 |
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.
| Author | : Edwin Lughofer |
| Publisher | : Springer |
| Total Pages | : 567 |
| Release | : 2019-02-28 |
| Genre | : Technology & Engineering |
| ISBN | : 3030056457 |
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.
