Acoustic maps are valuable tools for visualizing mechanical conditions in industrial processes, but their quality is often compromised by noise, interference, and low signal sensitivity. This work presents an enhanced methodology that combines the fusion of acoustic emission (AE) and vibration signals with digital image processing techniques to improve the visual quality of the generated maps. Pixel formation was refined using bidimensional rms, improving smoothing. Subsequently, a distribution adjustment block generated a composite signal with balanced contributions from AE and vibration, increasing image interpretability while preserving the relevance of each sensing modality. Experiments were conducted on an aluminum oxide grinding wheel employed in tangential surface grinding, which was dressed with a single-point diamond tool at different depths, with reference patterns in the shapes of plus (+) and tee (T) machined onto its surface. The resulting acoustic maps achieved a correlation index of 0.830 with the reference images, representing an average improvement of 22.87% compared to AE-only approaches. The proposed methodology proved particularly effective under intermediate and deep dressing conditions, offering a robust, noninvasive, and efficient solution for grinding wheel monitoring, with potential compatibility with digital manufacturing systems.

The growing integration of sensors and embedded devices into distributed Internet of Things (IoT) systems has increased the demand for real-time data collection and processing solutions. However, the high volume and frequency of sensor data create challenges related to storage, transmission, and response latency, especially in resource-constrained environments. In this context, locally executed compression techniques, aligned with the Tiny Machine Learning (TinyML) paradigm, become differentiators for enabling embedded applications. Thus, this work proposes an autoencoder-based approach for efficiently compressing sensor data on edge devices. Three autoencoder variants (feed-forward, sparse, and contractive) are evaluated, combined with symmetric and asymmetric architectures, considering criteria such as compression ratio, information preservation, and embedded execution feasibility. For practical validation, a case study was conducted using vehicular data collected via the OBD-II interface, where the selected models were deployed on the OBDII Edge Freematics One+ device. The results show that the models could reduce data dimensionality with minimal information loss, maintain competitive performance on discriminative tasks, and exhibit inference times compatible with real-time applications. Autoencoders represent a viable neural compression solution for IoT environments, potentially applicable to various embedded sensing scenarios.

The advancement of Industry 4.0 has intensified the demand for intelligent systems that can efficiently access and interpret technical information critical to industrial operations. However, recovering knowledge from extensive and complex technical documentation remains a significant challenge. This study examines the effectiveness of various Retrieval-Augmented Generation (RAG) strategies, combined with different Large Language Models (LLMs), in extracting and generating answers from industrial technical documents. A case study was conducted based on PROFIBUS, a widely adopted digital communication protocol in automation networks, with technical documents organized into categories for engineers and developers. Twenty questions of varying complexity were formulated, and responses were generated using three RAG strategies (Basic, Decomposition, and HyDE) combined with two LLMs (Gemma 3 and GPT-4o-mini). The outputs were compared against reference answers generated by the NotebookLM system and evaluated using automatic metrics, including ROUGE, METEOR, BERTScore, and MATTR. The results indicate that the Decomposition and HyDE strategies achieved superior semantic similarity scores when combined with more capable models. That model’s performance varied depending on the complexity of the document profile. These findings highlight the importance of tailored RAG strategies in enhancing intelligent information retrieval in industrial domains, which supports safer and more efficient operational environments.

The Internet of Things (IoT) generates massive multivariate time series data requiring real-time anomaly detection and correction for reliable monitoring. This challenges resource-constrained embedded devices due to conventional offline training and batch processing. To address this, we propose two algorithms derived from the TEDARLS framework: Multivariate Sequential TEDA (MST) and Multivariate Parallel TEDA (MPT). Derived from the TEDARLS framework, both enable on-device detection and correction of multivariate anomalies within TinyML constraints. A case study with real vehicular sensor data demonstrated low inference times and consistent embedded behavior. Supervised metrics were only assessed on synthetic data. MPT, though more sensitive, introduced greater signal distortions and required significantly longer processing times. Overall, MST demonstrated superior stability and suitability for real-time anomaly correction in resource-constrained IoT environments. This approach addresses an important gap in embedded analytics for IoT by enabling lightweight, accurate, and autonomous anomaly detection and correction at the edge.

Respecting real-time requirements in networked control systems requires that the network traffic respects some well-defined constraints. Any abnormal behavior, e.g. due to malfunctioning of few devices or representing a malicious attack, could disrupt the overall plant behavior. Immediate recognition of anomalies should occur, to determine the causes and mitigate the impairments. Despite hardware sniffer being ready available, deployment cost, maintenance cost and topology constrains of industrial network discourage their adoption for live traffic collection. Additionally, anomaly detection systems are typically complex solutions that demand experienced personnel, which is not generally available among plant maintenance workers. This paper presents the ongoing research activity for a simple but effective expert system that gathers network-related figure of merits natively evaluated by industrial-grade devices, acquired by standard SNMP (Simple Network Management Protocol) queries. Data collected by such a distributed measurement system are further analyzed using simple classification techniques, as the unsupervised clustering, which limit the computational burden. In this work some suitable performance indicators are addressed and the related multi dimensional clustering is discussed. Some preliminary results are provided, exploiting a reference use case designed around a real-world assembly machine.

The Industry 4.0 has led to a significant transformation in manufacturing and industrial processes, characterized by increased connectivity and widespread digitalization. This revolution is driven by the convergence of IT (Information Technology) paradigms with OT (Operational Technology) domains. Virtualization technology plays a crucial role in this integration, thanks to its characteristics of agility, scalability, and efficiency. This work investigates the virtualization of Programmable Logic Controllers (PLCs) and it is focused on the need for evaluating their performance. PLCs are essential components of industrial automation and are being virtualized to increase flexibility, reduce hardware dependencies, and enable seamless integration with IT infrastructures. This work proposes a methodology for evaluating the execution time of a benchmark program and the PLC real-time behavior. Experiments on a real use case demonstrate that the PLCs implemented in containers may be 50 times faster than the real ones in executing data elaboration, while no significant differences were observed in real-time communication. In any case, virtual PLCs are characterized by greater jitter with respect to real PLCs.

There are major discussions about the vulnerability of protocols based on real-time Ethernet (RTE) and techniques for detecting anomalies. Thus, this work proposes a methodology for detecting anomalies by optimizing the data extraction and by classifying traffic-related features. In order to cope with this proposal, an artificial neural network (ANN)-based classifier is trained using selected relevant features. These features are extracted using variable sized sliding window and selected according to their correlation with the other features and the expected output of the classifier. The number of relevant features can vary according to performance indicators of the classifier. The proposed methodology was exploited for identifying four different events of PROFINET networks. The performance of the ANN-based classifier was considered successful for all cases. This outcome suggests that the proposed methodology may be successful for anomalies detection in any PROFINET network. However, the application of the proposed methodology to other RTE protocol is foreseen.

The increased need for power quality monitoring and active control of distribution grid necessitates the introduction of the Smart Grid (SG) approach, requiring an efficient ICT system for the monitoring and control of distribution grid state. The main obstacle to deployment of SG in real system is the lack of an efficient communication infrastructure. The use a heterogeneous network, which employs various technologies, appears to be the most promising solution. Nevertheless, the management of these communication systems has proven to be hard and error prone. In this paper, a Software Defined Networking (SDN) approach has been proposed to manage SG communication system applied to grid monitoring/supervision. The preliminary feasibility analysis is promising, although a more detailed modeling and analysis of the system is needed due to the extreme heterogeneity of the network.

This research proposes the use of Artificial Neural Networks to diagnose industrial networks communication via Profibus DP Protocol. These diagnostics are based on information provided by the Physical Layer from the Profibus DP Protocol. In order to analyze the physical layer, an Artificial Neural Network first analyzes signal samples transmitted through the industrial network. In case these signals show some deformation, the Artificial Neural Network indicates a possible cause for the problem, after all, problems from Profibus networks generate specific and distinctive standards imprinted on the digital signal wave formats. Before the Artificial Neural Network analysis, the signal was pre-processed through a clipper methodology. The project was validated by data obtained from concrete Profibus networks created in laboratory. The results were satisfactory, proving the great strength and versatility that intelligent computer systems have when applied to the purposes outlined in this work.

During the setup phase of an industrial plant and mainly during the maintenance phase, the tuning of industrial systems is executed. It enhances the performance of the industrial process during production life cycle. This paper focus on remote tuning applications and issues. The remote tuning approach supports several practical applications, such as specialized companies outsourcing services or companies distributed in different areas centralizing optimization. The developed software tool for remote tuning of open or closed PID control loops in an industrial environment fulfils the requirements described above, in a single platform. The transport technology CyberOPC supports the remote data update rate required using open technologies. The software tool could be used in control loops tuning in industrial systems, as well as in an academic environment simulating control applications and industrial networks.

Wireless mesh networks are mobile communication networks where the nodes may have fixed or mobile relative position among each other. The application range is large, they can be used either as a sensor network or connected to Internet through gateways. The communication is carried over low power and low range wireless channels, so its necessary a multi hop routing. The Better Directed Progression routing protocol is presented and proposed as a solution to industrial networks. Simulations are conducted in order to compare its performance to the Compass Routing protocol and also to the lowest number of hops solution, given by the Dijkstra graph algorithm. Results show that the Better Directed Progression routing protocol is self healing protocol with robustness to be considered for industrial applications.