Industrial cyber-physical systems rely increasingly on data from Internet-of-Things (IoT) devices and other systems as continuously emerging use cases implement new intelligent features. Edge computing can be seen as an extension of the cloud in close physical proximity, in which some of the typical cloud computing loads are beneficial to run. This article studies data analytics application development for integration of industrial IoT data and composition of application services executed on edge and cloud. A solution is designed to support heterogeneous hardware and run-time platforms, and focuses on the service layer that enables flexible orchestration of data flows and dynamic service compositions. The unified model and system architecture implemented, using the open Arrowhead framework model, is verified through two representative industrial use cases.

Rotor bars are one of the most failure-critical components in induction machines. We present an approach for developing a rotor bar fault identification classifier for induction machines. The developed machine learning-based models are based on simulated electrical current and vibration velocity data and measured vibration acceleration data. We introduce an approach that combines sequential model-based optimization and the nested cross-validation procedure to provide a reliable estimation of the classifiers’ generalization performance. These methods have not been combined earlier in this context. Automation of selected parts of the modeling procedure is studied with the measured data. We compare the performance of logistic regression and CatBoost models using the fast Fourier-transformed signals or their extracted statistical features as the input data. We develop a technique to use domain knowledge to extract features from specific frequency ranges of the fast Fourier-transformed signals. While both approaches resulted in similar accuracy with simulated current and measured vibration acceleration data, the feature-based models were faster to develop and run. With measured vibration acceleration data, better accuracy was obtained with the raw fast Fourier-transformed signals. The results demonstrate that an accurate and fast broken rotor bar detection model can be developed with the presented approach.

This study quantified environmental impacts of circularity strategies in the manufacturing industry, focusing on maintenance-driven product lifetime extension (PLE) using Life Cycle Assessment (LCA). The analyzed case builds on earlier work, which showed that the product’s lifetime was shorter than the industry average lifetime and that the use phase was the dominant contributor to overall environmental impacts, identifying it as a key area for improvement. A computational code was developed to model maintenance-driven lifetime extension scenarios and to calculate selected total and normalized environmental impacts for the studied industrial process equipment. The model assumes maintenance-related impacts are smaller than the impacts avoided through reduced new production. Results show that while total impacts increase with longer use, normalized impacts per unit of production and per year of service life decrease by 58%, improving resource efficiency. Maintenance-driven PLE supports circular economy (CE) strategies by slowing material flows and extending product use. The findings highlight the importance of incorporating maintenance into circularity frameworks and life cycle-based decision-making.