Singh / Patidar / Panwar Data Analytics and Artificial Intelligence for Predictive Maintenance in Industry 4.0

1. Auflage 2025
ISBN: 979-8-89881-087-0
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 330 Seiten

Data Analytics and Artificial Intelligence for Predictive Maintenance in Industry 4.0 unites data science, machine learning, IIOT, and AI to enable predictive and prescriptive maintenance across manufacturing, energy, transportation, agriculture, and healthcare. With contributions from leading academics and practitioners, the book bridges foundational principles with cutting-edge industrial case studies ranging from digital twins and anomaly detection to federated learning and secure healthcare analytics. Key Features Explains fundamental concepts of data analytics, AI, and machine learning for predictive maintenance. Integrates IIoT, digital twins, federated learning, and blockchain into industrial maintenance strategies. Demonstrates real-world applications across manufacturing, energy, healthcare, and agriculture sectors. Analyzes optimization techniques, anomaly detection, condition monitoring, and RUL prediction models. Addresses security and ethical issues, including hardware protection and homomorphic encryption for healthcare. Maps future trends and emerging technologies driving predictive maintenance research.

Autoren/Hrsg.

Singh, Tanu

Patidar, Vinod

Panwar, Arvind

Sugandh, Urvashi

Fachgebiete

• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein

Leseproben

Understanding the Basics of Data Analytics and AI for Predictive Maintenance in Industry 4.0

Arvind Panwar1, *, Urvashi Sugandh2, Neha Sharma3, Manish Kumar1, Kuldeep Singh Kaswan1

1 School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India

2 School of Computer Science and Engineering, Bennett University, Greater Noida, India

3 Department of Information Technology, Bharati Vidyapeeth's College of Engineering, New Delhi, India

Abstract

Industry 4.0 marks a transformational era in industrial practices, defined by the merging of cutting-edge technologies such as the Internet of Things, cyber-physical systems, extensive data examination, cloud computing, artificial intelligence, and machine learning. This chapter, entitled “Understanding the Basics of Data Analytics and AI for Predictive Maintenance in Industry 4.0,” offers an inclusive exploration of how data examination and AI are revolutionizing predictive servicing strategies to improve functional efficacy, decrease expenses, and enhance safety. To commence with an outline of Industry 4.0 and the evolution of servicing strategies—from reactive and preventative to predictive—the chapter underscores the pivotal role of data-driven decision-making in modern industrial operations. It delves into the basics of data examination, analyzing the kinds of industrial data, methods of obtaining information, and preprocessing techniques. Core analytical techniques, like descriptive, diagnostic, predictive, and, briefly, prescriptive analytics, are inspected to demonstrate their applications in servicing contexts. The chapter further examines the joining of AI in predictive servicing, detailing machine learning algorithms. It also highlights the instruments and platforms usually used in data examination and AI, together with programming languages like Python and R, specialized software, and data visualization instruments. The advantages, like reduced downtime, servicing cost savings, extended equipment lifespan, and enhanced decision-making capabilities, are balanced against challenges, for example, data quality management, scalability, cybersecurity concerns, skills gaps, cultural resistance to change, and investment considerations. The chapter also explores emerging developments and future directions, like edge computing, digital twins, comprehensible AI, merging with other Industry 4.0 technologies, and the concept of Predictive Servicing as a Service (PMaaS), analyzing their possible influence to further transform servicing practices and contribute to sustainability. By providing foundational knowledge and practical insights and highlighting both oppor-

tunities and challenges, this chapter aims to provide readers with the understanding necessary to leverage data examination and AI for innovative and efficient predictive servicing in the evolving landscape of Industry 4.0.

Keywords: Artificial intelligence (AI), Data analytics, Industry 4.0, Internet of things (IoT), Machine learning (ML), Predictive maintenance.

* Corresponding author Arvind Panwar: School of Computing Science and Engineering, Galgotias University, Gr. Noida, Uttar Pradesh, India; E-mail: [e-mail]

INTRODUCTION

The dawn of Industry 4.0 has transformed how industries function, communicate, and conceive [1]. This section intends to offer a thorough comprehension of the fundamental principles of data examination and synthetic consciousness for anticipatory servicing in Industry 4.0. In this section, we will plunge into the overview of Industry 4.0, its progression, and the importance of predictive servicing at this time. Additionally, industries must leverage novel technologies to optimize operations whilst ensuring worker safety through automation. While change can inspire apprehension, an open mind to emerging tools may reveal opportunities to enhance productivity, quality, and outcomes [2].

Overview of Industry 4.0

Industry 4.0 and the fusion of cyber systems will revolutionize manufacturing like never before. While companies scramble to integrate networks of intelligent devices and technologies capable of autonomous action, profound transformation lies ahead. Already, robots work alongside workers on factory floors, communicating in real time through IoT platforms to autonomously complete tasks. Machines learn from vast torrents of big data, enabling precision and customization at scale. Human and artificial intelligence will cooperate as never before to realize smart factories envisioned since the dawn of computational might, though challenges remain to full realization [3]. Optimists point to skyrocketing productivity and emancipation from dreary tasks, while others fear widespread economic upheaval and profound social changes as old jobs become obsolete. One thing is clear - a new industrial age defined by sentient systems and omnipresent information looms on the horizon, for good and for bad [4].

The Evolution of Industrial Revolutions

The novelty concept of Industry 4.0 does not exist alone and is deeply rooted in the flow of industrial revolutions that took place in a certain chronological order. The first industrial revolution took place in the late 18th century and tended to associate with the transition from manual labor to the operation of machines [5]. The second revolution unfolded in the late 19th and early 20th century and involved the further development of machinery, as well as the new concept of mass production, which was represented by conveyor belts. The third industrial revolution started in the mid-20th century and was based on the vastly spread computer, automation, and mechanization of production. The fourth industrial revolution, which is sometimes called Industry 4.0, is fuelled by the integration of digital, physical, and biological systems, which provides for the creation of levels of automation, service, and innovation that were not experienced before by the manufacturing industry. The fundamental transformation of Industry 4.0 is not only in industrial facilities’ widespread adoption of modern and highly efficient technologies but also in the development of an entirely new ecosystem where machines, humans, and data interact with each other harmoniously and effectively [6]. Industry 4.0 ecosystem is built on interconnectivity, automation, and the ability to exchange data for the foundation of smart factories, which create smart products and carry out smart services.

In the next sections, the basics of data analytics and AI in predictive maintenance will be discussed. These will include types of data and data analytics techniques, AI algorithms, integration of these technologies in Industry 4.0, benefits, and challenges of the implementation.

Key Technologies Driving Industry 4.0

The hallmarks of Industry 4.0 are the convergence of a number of essential technologies that revolutionize manufacturing. The technologies are the foundation on which smart factories, smart products, and smart services are built. It can be said that the key technologies of Industry 4.0 are:

• Internet of Things: The term Internet of Things refers to the interconnection of devices and machines using sensors to collect data and exchange it. In other words, IoT refers to the connection of anything from house appliances and motor vehicles to the entire factory and networks, enabling their communication. For example, in the case of predictive maintenance, IoT sensors can be used to monitor equipment and detect and possibly predict undesired anomalies and failures. Other uses for IoT devices are keeping track of inventory levels, monitoring the supply chain, and optimizing logistics [7].
• Cyber-Physical Systems (CPS): Cyber-physical systems amalgamate computational and physical mechanisms to generate ingenious infrastructures. CPS combines the tangible with the digital, permitting live observation, administration, and optimization of corporeal processes. In prescient servicing, CPS can monitor hardware performance, distinguish anomalies, and foresee failures. CPS can furthermore optimize fabrication techniques, diminish energy usage, and improve merchandise quality [8].
• Big Data and Analytics: Vast data alludes to the expansive and intricate information assemblages that are created by different sources like gadgets, machines, and sensors. Big data investigation includes breaking down these information accumulations to acquire experiences and settle on educated choices [9]. In prescient support, enormous information examination can dissect hardware execution information, identify examples, and foresee disappointments. Big...