Data Analytics in the Era of the Industrial Internet of Things

1. Auflage 2021
ISBN: 978-3-030-63141-3
Verlag: Springer International Publishing

Buch, Englisch, 133 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 242 g

This book presents the characteristics and benefits industrial organizations can reap from the Industrial Internet of Things (IIoT). These characteristics and benefits include enhanced competitiveness, increased proactive decision-making, improved creativity and innovation, augmented job creation, heightened agility to respond to continuously changing challenges, and intensified data-driven decision making. In a straightforward fashion, the book also helps readers understand complex concepts that are core to IIoT enterprises, such as Big Data, analytic architecture platforms, machine learning (ML) and data science algorithms, and the power of visualization to enrich the domains experts’ decision making. The book also guides the reader on how to think about ways to define new business paradigms that the IIoT facilitates, as well how to increase the probability of success in managing analytic projects that are the core engine of decision-making in the IIoT enterprise.   The book starts by defining an IIoT enterprise and the framework used to efficiently operate. A description of the concepts of industrial analytics, which is a major engine for decision making in the IIoT enterprise, is provided. It then discusses how data and machine learning (ML) play an important role in increasing the competitiveness of industrial enterprises that operate using the IIoT technology and business concepts. Real world examples of data driven IIoT enterprises and various business models are presented and a discussion on how the use of ML and data science help address complex decision-making problems and generate new job opportunities. The book presents in an easy-to-understand manner how ML algorithms work and operate on data generated in the IIoT enterprise. 
Useful for any industry professional interested in advanced industrial software applications, including business managers and professionals interested in how dataanalytics can help industries and to develop innovative business solutions, as well as data and computer scientists who wish to bridge the analytics and computer science fields with the industrial world, and project managers interested in managing advanced analytic projects.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Dagnino, Aldo

Fachgebiete

• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Ambient Intelligence, RFID, Internet der Dinge
• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
• Wirtschaftswissenschaften Betriebswirtschaft Management Forschung & Entwicklung (F&E), Innovation
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik

Inhaltsverzeichnis

Chapter 1: Industrial Internet of Things Framework.- Chapter 2: Industrial Analytics.- Chapter 3: Machine Learning to Predict Fault Events in Power Distribution Systems.- Chapter 4: Analyzing Events and Alarms in Control Systems.- Chapter 5: Condition Monitoring of Rotating Machines in Power Generation Plants.- Chapter 6: Machine Learning Recommender for New Products and Services.- Chapter 7: Managing Analytic Projects in the IIoT Enterprise.

Über Autor(innen)

Dr. Aldo Dagnino is an Industrial Engineer and received his M. A. Sc. and Ph. D degrees in the Department of Systems Design Engineering at the University of Waterloo in Canada. He has  collaborated with various universities such as North Carolina State University and  the University of Calgary where he held Adjunct Faculty appointments to conduct joint research, co-supervise graduate students, and create industrial internship programs to bridge academia with industry needs. Dr. Dagnino has 30 years’ experience developing advanced software solutions for industrial applications. The main focus of his work has been to bridge the technical fields of Computer Science, Software Engineering, and Industrial Systems Engineering for the development of new production systems that will enhance environmentally sustainable industrial processes and create new job opportunities. Dr. Dagnino has also utilized the fields of Artificial Intelligence, Machine Learning, Data Mining, Operations Research, Robotics, Software Engineering, Industrial Engineering, and Manufacturing Engineering in the development of new software products and services for electronics, telecommunications, electro-mechanics, oil and gas, power generation, manufacturing, and power transmission and distribution. Dr. Dagnino led the Advanced Industrial Analytics Group at ABB US Corporate Research and is currently leading the advanced analytics activities within the ABB Global Information Systems organization.