Engineering Asset Management and Industrial Artificial Intelligence (IAI)

Asset management is a critical function across industries such as railway, mining, aviation, construction, and manufacturing. As these industries face increasing operational complexity and performance demands, the emergence of advanced technologies, including artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) has transformed traditional asset management practices into intelligent data-driven systems.

Modern asset management leverages connected sensors, cyber-physical systems, cloud platforms, and advanced analytics to optimise asset performance throughout the entire lifecycle. These technologies generate large volumes of operational and contextual data, which, when integrated with AI and machine learning techniques, enable enhanced condition monitoring, diagnostics, prognostics, and performance optimisation. Consequently, engineering asset management has shifted from reactive and time-based maintenance approaches toward predictive, prescriptive, and increasingly autonomous strategies supported by digital twins and intelligent analytics.

Despite these advancements, challenges related to data quality, interoperability, cybersecurity, human-machine interaction, resilience, and climate adaptation require multidisciplinary solutions. This Research Topic provides a platform to advance AI-enabled asset lifecycle management research and practice.

The goal of this Research Topic is to address the urgent need for intelligent, resilient, and sustainable management of engineering assets in increasingly complex industrial environments. Traditional asset management approaches are no longer sufficient to handle the scale of data, system interdependencies, cybersecurity threats, and climate-related risks facing modern infrastructure and industrial systems. By integrating Industrial AI technologies with established asset management principles, this collection aims to develop advanced methodologies for predictive analytics, autonomous maintenance, lifecycle optimisation, and intelligent decision-making, including human-in-the-loop. The Research Topic seeks to foster collaboration between academia and industry, enabling scalable, secure, economically viable, and environmentally sustainable AI-driven asset management solutions.

This Research Topic welcomes contributions related to:
- Digitalisation, Industrial AI, Machine Learning, Digital Twins & Data Analytics
- Asset Performance Management & ISO 55000 Practices
- Condition Monitoring & Diagnostics
- Prognostics & Health Management
- Reliability, RAMS, Safety & Sustainability
- Human-Machine Interface (HMI), Human-System Interface (HSI) & Industrial Wearables
- Cybersecurity, Blockchain & Secure Industrial Data Architectures
- Generative AI and Large Language Models (LLMs)
- Climate Adaptation & Climate-Resilient Infrastructure Management
- Future Frontiers in Asset Management & Industry 5.0

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: Industrial AI, Engineering Asset Management, Digital Twin, Condition Monitoring, Machine Learning, Prognostics and Health Management

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.