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The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of predictive maintenance requires well-trained machine learning algorithms which on their turn require high volumes of reliable data. This paper addresses both challenges and presents the Smart Maintenance Living Lab, an open test and research platform that consists of a fleet of drivetrain systems for accelerated lifetime tests of rolling-element bearings, a scalable IoT middleware cloud platform for reliable data ingestion and persistence, and a dynamic dashboard application for fleet monitoring and visualization. Each individual component within the presented system is discussed and validated, demonstrating the feasibility of IIoT applications for smart machine maintenance. The resulting platform provides benchmark data for the improvement of machine learning algorithms, gives insights into the design, implementation and validation of a complete architecture for IIoT applications with specific requirements concerning robustness, scalability and security and therefore reduces the reticence in the industry to widely adopt these technologies.

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Summary The feasibility of a vibration‐based damage identification approach for impact damage in two advanced composite skin‐stiffener structures is investigated in this paper. Mode shape curvatures combined with the modal strain energy damage index (MSE‐DI) algorithm are utilized to identify the damage. Special attention is paid to the effective application of this vibration‐based methodology by investigating the relation between the damage location, the structural design, and the dynamic behavior. The performance of a 1D and 2D formulation of the MSE‐DI algorithm is compared for several damage scenarios. Experiments demonstrated the capabilities of the MSE‐DI algorithm to detect, localize, and roughly quantify the size of barely visible impact damage in advanced composite structures. It is concluded that the method is particularly effective for health monitoring of skin‐stiffener connections. The most effective results were obtained by considering the 1D formulation in the direction of the stiffeners for the stiffener mid‐section and perpendicular to the stiffeners for the stiffener run‐out. The method remained inconclusive in the case of pure skin‐related damage. The results obtained in this study contribute to the development of guidelines for vibration‐based structural health monitoring of advanced composite skin‐stiffener structures. Copyright © 2015 John Wiley & Sons, Ltd.