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This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations. [ABSTRACT FROM AUTHOR]

• Construction of the intelligent pipeline is still in the initial stage. • Building digital twin pipelines is an inevitable trend. • Integrating data and mechanisms is a difficulty. • The internet of Things, big data, and multi-objective optimization are crucial. • Need to establish unified intelligent pipeline standard system and data platform. The pipeline is one of the important carriers in the petroleum industry, but facing with the development bottlenecks of low efficiency, high cost and high decision risk. With machine learning and advanced sensor, new technological breakthroughs are being opening up for intelligent pipeline systems to benefit. Nowadays, the intelligent transformation of pipeline is still in the initial stage. To accelerate this process, this paper elaborates the research on intelligent pipeline systems from the perspective of the full life cycle of pipeline. The life cycle is divided into six stages, including exploration, feasibility study, design, construction, operation and scrap. In each stage, the related theoretical research and key technologies have been systematically reviewed. It is observed that full digital construction and operation is an inevitable trend. The Internet of Things, big data, and multi-objective optimization are critical technologies. Finally, some future directions are put forward to prompt a pipeline into intelligence. [ABSTRACT FROM AUTHOR]