The Internet of Things (IoT) has significant potential in upgrading legacy production machinery with monitoring capabilities to unlock new capabilities and bring economic benefits. However, the introduction of IoT at the shop floor layer exposes it to additional security risks with potentially significant adverse operational impact. This article addresses such fundamental new risks at their root by introducing a novel endpoint security-by-design approach. The approach is implemented on a widely applicable production-machinery-monitoring application by introducing real-time adaptation features for IoT device security through subsystem isolation and a dedicated lightweight authentication protocol. This paper establishes a novel viewpoint for the understanding of IoT endpoint security risks and relevant mitigation strategies and opens a new space of risk-averse designs that enable IoT benefits, while shielding operational integrity in industrial environments.

When closed loop product lifecycle management was first introduced, much effort focused on establishing ways to communicate data between different lifecycle phase activities. The concept of a smart product, able to communicate its own identity and status, had a key role to play to this end. Such a concept has further matured, benefiting from internet things-enabled product lifecycle management advancements. Product data exchanges can now be brought closer to the point of end use consumption, enabling users to become more proactive actors within the product lifecycle management process. This paper presents a conceptual approach and a pilot implementation of how this can be achieved by superimposing middle of life relevant product information to beginning of life product views, such as a 3D product CAD model. In this way, linked maintenance data and knowledge become visual features of a product design representation, facilitating a user’s understanding of middle-of life concepts, such as occurrence of failure modes. The proposed approach can be particularly useful when dealing with product data streams as a natural visual analytics add-in to closed loop product lifecycle management.

In the modern competitive environments companies must have rapid production systems that are able to deliver parts that satisfy highest quality standards. Companies have also an increased need for advanced machines equipped with the latest technologies in maintenance to avoid any reduction or interruption of production. Eminent therefore is the need to monitor the health status of the manufacturing equipment in real time and thus try to develop diagnostic technologies for machine tools. This paper lays the foundation for the creation of a safe remote monitoring system for machine tools using a Cloud environment for communication between the customer and the maintenance service company. Cloud technology provides a convenient means for accessing maintenance data anywhere in the world accessible through simple devices such as PC, tablets or smartphones. In this context the safety aspects of a Cloud system for remote monitoring of machine tools becomes crucial and is, thus the focus of this paper.

Wind turbines are pivotal in the transition towards renewable energy. The operational conditions of these machines are continuously monitored through sensors that measure key indicators of efficiency and performance, including yaw angles, rotational speed, and vibrations. However, sensors are subjected to wear, degradation and consequent reduction in data reliability over time, which provides scope for developing a consistent and effective method to detect misinterpretation of turbine operating conditions caused by faulty measurements. This research presents a novel method that integrates Computational Fluid Dynamics (CFD) simulations into a Digital Twin (DT) model to detect and correct yaw misalignment caused by faulty wind direction readings. Yaw error is estimated by interpolation across CFD-based performance data using live sensor measurements. The novel DT-based method was validated through experimental testing on a small-scale horizontal-axis wind turbine. The results provide scope for a significant improvement in the resilience of wind turbines under conditions of sensor malfunctions, without the need for human intervention or supervision. The proposed method is intended to be adaptable, enabling analysis of diverse failure modes under varying operational conditions. This work also advances condition monitoring and sustainable asset management, offering potential for a larger adoption across different turbomachinery applications.