Machine Learning in Industry Systems

Dear Colleagues,

The scope and purpose of this Special Issue and its relationship to other literature on the topic are summarized as follows:

1. Focus

• The focus of this topical collection is to explore the intersection of machine learning and industry systems across a diverse range of engineering disciplines, including electrical and computer engineering, mechanical science engineering, automotive engineering, manufacturing engineering, and more.

• The scope of this topical collection encompasses original research articles, reviews, and case studies that demonstrate innovative applications of machine learning in industrial contexts.
• Topics may include, but are not limited to:
  • Electrical and computer engineering: applications of machine learning in power systems, energy management, smart grids, IoT devices, and renewable energy systems.
  • Mechanical science engineering: utilization of machine learning for predictive maintenance, fault diagnosis, optimization of mechanical systems, robotics, and mechatronics.
  • Automotive engineering: integration of machine learning in autonomous vehicle technologies, driver assistance systems, vehicle diagnostics, and predictive analytics for vehicle performance.
  • Manufacturing engineering: adoption of machine learning for process optimization, predictive maintenance, quality control, digital twinning, and smart factory initiatives.
• Contributions may address both theoretical advancements in machine learning algorithms and practical implementations in real-world industrial settings.

• The purpose of this topical collection is two-fold:

1. To provide a platform for researchers, engineers, and practitioners from diverse engineering disciplines to share their latest findings, innovations, and experiences in applying machine learning to industry systems.
2. To advance the understanding of how machine learning can revolutionize traditional industrial practices, improve operational efficiency, reduce costs, enhance product quality, and drive innovation across various engineering domains.

• By facilitating interdisciplinary collaboration and knowledge exchange, this collection aims to accelerate the adoption of machine learning technologies in industrial settings, ultimately leading to the development of more intelligent, adaptive, and autonomous industry systems.

How will this collection supplement the existing literature? The topical collection will complement existing literature by:

• Providing comprehensive insights into the latest advancements and applications of machine learning in diverse engineering fields, thereby expanding the knowledge base and understanding of the potential impact of these technologies.
• Offering practical case studies and real-world examples that demonstrate the effectiveness and feasibility of machine learning solutions in addressing complex challenges and optimizing industrial processes.
• Fostering interdisciplinary collaboration and cross-pollination of ideas between researchers and practitioners in different engineering disciplines leads to the emergence of new research directions and innovative solutions.
• Bridging the gap between theory and practice by highlighting successful implementations of machine learning algorithms and methodologies in industrial environments, thereby facilitating technology transfer and adoption.

This topical collection serves as a valuable resource for academics, researchers, engineers, and industry professionals seeking to stay abreast of the latest developments in the field of machine learning and its applications in industry systems across various engineering disciplines.

Dr. Xuzhi Du
Dr. Chao Xia
Dr. Wuchen Fu
Guest Editors

Manuscript Submission Information

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• machine learning
• industry systems
• engineering applications
• electrical engineering
• mechanical engineering
• automotive engineering
• manufacturing engineering
• smart manufacturing
• predictive maintenance
• quality control
• supply chain management
• autonomous vehicles
• industrial automation
• robotics
• mechatronics, smart grids
• IoT devices
• renewable energy systems
• digital twinning
• optimization techniques