The oil and gas industry plays a crucial role in the global energy landscape, contributing to a significant share of the world's energy needs. It is an extremely competitive sector characterized by intensive capital and operational demands and highly complex processes and systems requiring state-of-the-art technology. In this scenario, proper maintenance is essential to ensure equipment reliability and the system’s operational efficiency since failures or unplanned downtime can lead to catastrophic outcomes on different levels. Thus, the need for safe, accurate, and cost-effective equipment operation requires reliable maintenance strategies and good practices capable of estimating and identifying such equipment failures before they occur.

The advent of Industry 4.0 and the maturing of its enabling technologies such as the Internet of Things (IoT), artificial intelligence (AI) and machine learning (ML), cloud and edge computing, smart sensors, digital twins, and augmented reality have enabled the evolution of the traditional manufacturing industries toward smart manufacturing.

In particular, the recent innovation in sensing technologies and monitoring systems and their integration with data analytics tools based on artificial intelligence and machine learning has enabled decision-makers to have access to real-time data on operating conditions, performance, and safety of their assets. This has provided a pathway to perform a predictive maintenance approach leading the way to efficient use of machinery up to their useful life, reducing machine downtime and costs, and enhancing production and control quality.

This Research Topic aims to share the experience of industrial engineers, both from industry and academia, and discuss the state of the art about approaches, methods, tools, case studies, and techniques on systems reliability and predictive maintenance applied to oil and gas industries. This Special Issue will collect advances in smart maintenance such as approaches for smart maintenance decision support, data-driven decision-making, or novel methodologies of machine learning, neural networks, or deep learning for pattern recognition. In addition, reliability analysis, fault detection and diagnosis, deteriorating systems modeling, and optimization methods for maintenance task scheduling in oil and gas industries will be welcome.

Topics of interest for publication include, but are not limited to:

• Asset lifecycle management;

• Maintenance management;

• Data-driven maintenance;

• Predictive maintenance;

• Smart maintenance;

• Reliability analysis;

• AI for reliability and resilience of complex systems maintenance

• Condition monitoring, diagnosis, and prediction

• Simulation and optimization in maintenance

• Data-driven decision-making

• Condition-based maintenance

• Failure modes and effect analysis;

• Analysis and modeling of deterioration processes;

• In-depth analysis and comparison of case studies;

• Optimization methods for maintenance;

• Techno-economic analysis for maintenance approaches;

• Industry 4.0 technologies for predictive maintenance

• Innovative computing technologies in reliability;

• Decision support systems

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Frontiers in Manufacturing Technology

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