Intelligent Diagnostics and Control for Offshore Mechanical Systems

Dear Colleagues,

Offshore mechanical systems, encompassing wind turbines, oil and gas platforms, wave energy converters, and subsea robotics, constitute vital elements of the global energy and marine infrastructure. These systems operate in remote and harsh environments, where they are continuously subjected to extreme external loads, including intense wave action, strong winds, and corrosive saline conditions. Such factors significantly accelerate material degradation, structural fatigue, and component failure. Furthermore, the geographical remoteness of offshore installations renders manual and visual inspection, maintenance, and repair operations logistically challenging, and economically burdensome. The assurance of reliability, availability, safety, and operational efficiency within these systems therefore presents a central challenge. Conventional maintenance strategies, primarily reliant on rigid, time-based scheduling, are increasingly recognized as inadequate and inefficient under such demanding conditions. A paradigm shift toward data-driven maintenance and control strategies is thus essential. The integration of advanced sensing technologies, the Industrial Internet of Things (IIoT), artificial intelligence (AI), and modern control theory offers transformative opportunities for enhancing the resilience and sustainability of offshore operations.

This Special Issue seeks to collect state-of-the-art research and developments in the domain of intelligent diagnostics and control for offshore mechanical systems. The objective is to provide a scholarly platform for the dissemination of novel methodologies, advanced computational frameworks, and empirically validated case studies that address the unique challenges inherent to monitoring, diagnosing, and controlling offshore systems. Particular emphasis is placed on approaches that enable prognostics and health management, condition-based maintenance, performance optimization, and the advancement of autonomy and resilience in offshore engineering practice.

This Special Issue invites original research articles, comprehensive review papers, and insightful case studies on topics including, but not limited to, the following:

• AI and machine-learning-based fault detection and identification (FDI).
• Deep learning models for prognostics and health management (PHM).
• Physics-informed machine learning (PIML) for diagnostics.
• Signal processing techniques for feature extraction from sensor data.
• Fault-tolerant and adaptive control strategies.
• Reinforcement learning for real-time control optimization.
• Advanced control for motion compensation, stabilization, and station-keeping.
• Control of robotic systems for inspection, maintenance, and repair (IMR).
• Development and application of digital twins for offshore systems.
• Simulation of system degradation and failure modes.
• Cybersecurity for offshore control and monitoring systems.
• Structural health monitoring (SHM) of offshore platforms.
• Predictive maintenance scheduling and logistical optimization.

Prof. Dr. Yassine Amirat
Prof. Dr. Tianzhen Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• offshore engineering
• marine technology
• Structural Health Monitoring (SHM)
• system resilience
• reliability and safety
• Prognostics and Health Management (PHM)
• Condition-Based Maintenance (CBM)
• data-driven maintenance
• performance optimization
• fault diagnosis and fault-tolerant control