Synchronous motors are pivotal to modern industrial systems, particularly those aligned with Industry 4.0 initiatives, due to their high precision, reliability, and energy efficiency. This review systematically examines fault detection and diagnostic techniques for synchronous motors from 2021 to 2025, emphasizing recent methodological innovations. A PRISMA-guided literature survey combined with scientometric analysis via VOSviewer 1.6.20 highlights growing reliance on data-driven approaches, especially deep learning models such as CNNs, RNNs, and hybrid ensembles. Model-based and hybrid techniques are also explored for their interpretability and robustness. Cross-domain methods, including acoustic and flux-based diagnostics, offer non-invasive alternatives with promising diagnostic accuracy. Key challenges persist, including data imbalance, non-stationary operating conditions, and limited real-world generalization. Emerging trends in sensor fusion, digital twins, and explainable AI suggest a shift toward scalable, real-time fault monitoring. This review consolidates theoretical frameworks, comparative analyses, and application-oriented insights, ultimately contributing to the advancement of predictive maintenance and fault-tolerant control in synchronous motor systems.

Every day, a considerable number of new cybersecurity attacks are reported, and the traditional methods of defense struggle to keep up with them. In the current context of the digital era, where industrial environments handle large data volumes, new cybersecurity solutions are required, and intrusion detection systems (IDSs) based on artificial intelligence (AI) algorithms are coming up with an answer to this critical issue. This paper presents an approach for implementing a generic model of a network-based intrusion detection system for Industry 4.0 by integrating the computational advantages of the Nvidia Morpheus open-source AI framework. The solution is modularly built with two pipelines for data analysis. The pipelines use a pre-trained XGBoost (eXtreme Gradient Boosting) model that achieved an accuracy score of up to 90%. The proposed IDS has a fast rate of analysis, managing more than 500,000 inputs in almost 10 s, due to the application of the federated learning methodology. The classification performance of the model was improved by integrating a generative adversarial network (GAN) that generates polymorphic network traffic packets.

This paper investigates the integration of intrusion detection systems (IDSs) within Digital Twin (DT) architectures to enhance cybersecurity in industrial environments. Using the CICIDS2017, CIC Modbus, and 4SICS 2015 datasets, we evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) in detecting network intrusions. Results indicate that RF achieves an accuracy of 99.9% for CICIDS2017, with high precision, recall, and low false positives. In contrast, SVM exhibits an accuracy of 94.2% for the same dataset, struggling with high rates of false positives and moderate recall. Similarly, for 4SICS 2015, RF demonstrates an accuracy of 93%, being balanced and reliable for industrial applications, while SVM shows only 88% accuracy, with a low precision of 65% and a high false alarm rate. For the CIC Modbus dataset, RF displays an accuracy of 95% in validation and 93% in testing, highlighting strong detection in ICS networks. However, SVM maintains an accuracy of 88%, with weak separation between benign and malicious traffic, and a higher misclassification rate. Our findings highlight the importance of DT-IDS integration in real-time threat detection and system resilience, paving the way for future research in deep learning-based IDS solutions.