Browsing by Author "Spencer, Geoffrey"
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- Design of CAN Bus communication interfaces for forestry machinesPublication . Spencer, Geoffrey; Mateus, Frutuoso; Torres, Pedro; Dionísio, Rogério; Martins, RicardoThis paper presents the initial developments of new hardware devices targeted for CAN (Controller Area Network) bus communications in forest machines. CAN bus is a widely used protocol for communications in the automobile area. It is also applied in industrial vehicles and machines due to its robustness, simplicity, and operating flexibility. It is ideal for forestry machinery producers who need to couple their equipment to a machine that allows the transportation industry to recognize the importance of standardizing communications between tools and machines. One of the problems that producers sometimes face is a lack of flexibility in commercialized hardware modules; for example, in interfaces for sensors and actuators that guarantee scalability depending on the new functionalities required. The hardware device presented in this work is designed to overcome these limitations and provide the flexibility to standardize communications while allowing scalability in the development of new products and features. The work is being developed within the scope of the research project “SMARTCUT—Remote Diagnosis, Maintenance and Simulators for Operation Training and Maintenance of Forest Machines”, to incorporate innovative technologies in forest machines produced by the CUTPLANT S.A. It consists of an experimental system based on the PIC18F26K83 microcontroller to form a CAN node to transmit and receive digital and analog messages via CAN bus, tested and validated by the communication between different nodes. The main contribution of the paper focuses on the presentation of the development of new CAN bus electronic control units designed to enable remote communication between sensors and actuators, and the main controller of forest machines.
- Digital twin environment for Forestry 4.0 application using a CAN Bus architecturePublication . Spencer, Geoffrey; Dionísio, Rogério Pais; Neto, Luis; Torres, Pedro; Gonçalves, GilThis paper presents a digital twin demonstrator of a forest harvesters and wood processing machines. The demonstrator is a cyber-physical system that allow the emulation and identification of faults that may occur during regular machine operations. The proposed solution includes a CAN Bus communication between several electronic controller units connected to sensors and actuators.
- Efficient integration of industry 4.0 technologies in mobile industrial and forestry machines fleet management: Challenges, opportunities, and environmental impactsPublication . Torres, Pedro; Vilela, Francisco; Spencer, Geoffrey; Neto, LuísAs industries embrace the transformative wave of Industry 4.0, the integration of advanced technologies into mobile industrial and forestry machines becomes pivotal for efficient fleet management. This paper explores the challenges and opportunities associated with the incorporation of Industry 4.0 technologies in Mobile machines, focusing on their mobility and applicability in forestry operations. The study delves into the utilization of sensors, remote monitoring, and machine-to-machine communication for real-time data collection and enhanced fleet coordination. Moreover, the paper evaluates the environmental impacts, emphasizing how Industry 4.0 implementation can contribute to sustainability by reducing fuel consumption, minimizing pollutant emissions, and optimizing natural resource utilization. By addressing these aspects, this research offers insights into the intricate interplay between Industry 4.0 technologies and mobile industrial and forestry machines, highlighting the potential for enhanced efficiency, productivity, and environmental responsibility in fleet management. The main goal is to demonstrate the feasibility of leveraging Industry 4.0 principles to enhance the performance of forestry machinery while concurrently reducing ecological footprint.
- Industrial IoT platforms enabling industry 4.0 digitization towards industry 5.0Publication . Torres, Pedro; Spencer, Geoffrey; Lopes, P.; Santos, FranciscoThis research paper explores the evolution of Industrial In- ternet of Things (IIoT) platforms and their pivotal role in the digitiza- tion of Industry 4.0, as well as their contribution to the transition to- wards Industry 5.0. The characteristics, challenges and opportunities of these platforms will be addressed, highlighting their impact on the digital transformation of industrial operations. Two case studies are unveiled, showcasing the implementation of IIoT platforms. These platforms lever- age cutting-edge technology, notably from the AWS (Amazon Web Ser- vices) ecosystem, enabling the development of solutions grounded in the three pillars of Industry 5.0: human-centered design, sustainability, and resilience. Through these case studies, its aim to illustrate how the in- tegration of AWS technologies contributes to the realization of Industry 5.0 principles, emphasizing a focus on human-centric approaches, sus- tainable practices, and resilient industrial systems.
- New can bus communication modules for digitizing forest machines functionalities in the context of Forestry 4.0Publication . Spencer, Geoffrey; Torres, PedroIn line with the context of Industry 4.0, forestry, and in particular the entire ecosystem around it, also needs digitalization solutions in order to allow better interaction between all agents that work and live from the forest. It is important for a better management of forest resources allowing productivity gains, more sustainability and resilience. One of the agents that can benefit, but also contribute to better forestry, are machine producers.With digitalization, the machinery is now equipped with new and better sensors that can be used not only for machine operations but also for forest management, through LiDAR (Light Detection And Ranging) or RGB (Red, Green, Blue) cameras for example. On the other hand, there are new needs for predictive maintenance or solutions for remote assistance of machines operating in the forest, typically in isolated areas with great limitations in access to communications. Thinking about these technological challenges, this work seeks to provide answers with communication solutions in forest machines, enabling the digitalization of functionalities, also allowing remote access to machine controllers in order to provide them with connectivity in an IIoT (Industrial Internet of Things) scenarios. New hardware modules designed in partnership and according to the prerequisites of a forest machine manufacturer are presented. These modules are a step towards digitizing the machines and opening up the scalability of new requirements, as well as remote access through additional gateways. The results already obtained in real scenarios show that these modules can be a concrete solution for the current and emerging needs of industrial machine manufacturers.
- A systematic review on the IEEE 1451 standards: Current status, challenges, and opportunitiesPublication . Spencer, Geoffrey; Torres, Pedro; Gonçalves, Gil M.Given the growth of different manufacturers with diverse hardware/software designs, the growth of transducers functionalities, and rising concerns toward heterogeneity, data security, and trustworthiness, the IEEE Instrumentation and Measurement Society’s Technical Committee 9 on Sensor Technology (IM/ST-TC9-Sensor Technology) supported by the National Institute of Standards and Technology (NIST) formalized the IEEE 1451 family of standards. The standards were developed with the purpose of defining common interfaces for communication within transducers and different systems with plug-and-play (PnP) features, reducing configuration and deployment time. However, in the context of Industry 4.0 (I4.0), the standard fails to address emerging technologies toward Internet of Things (IoT) applications, artificial intelligence (AI), and remaining concerns related to cybersecurity, making it difficult to achieve widespread adoption in the industry sector. This article explores a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 (PRISMA 2020) as a formal systematic literature review guideline to collect and analyze existing knowledge within the scope of the IEEE 1451 standard, examine potential improvements to the standard, and explore future directions to address the constraints identified and open new research opportunities. A total of 53 studies are analyzed, considering publications within the 2019 and 2025 time frame, with data being collected from the Web of Science (WoS), Scopus, and IEEE Xplore scientific database. Findings reveal a strong focus on smart transducers applications in the most diverse domains with interoperability concerns, such as integration with different standards, smart cities applications, environmental monitoring, as well as research on disseminating the adoption of the standards considering both the academic community and industry field while alsosuggesting adaptations/modifications of particular parameters to address evolving applications. Furthermore, a portion of the findings is centered on cybersecurity vulnerabilities and proposes solutions to enhance the robustness and resilience of systems employing the given standards.
- Unsupervised anomaly detection in industrial machines supported by vibration analysis under data scarcity constraintsPublication . Torres, Pedro; Spencer, Geoffrey; Esteves, Arthur; Sousa, Fernando; Pereira, Fernando J.G.; Guerreiro, Rui M.L.The integration of the use of Artificial Intelligence (AI) in industrial environments often faces the lack of labeled data, as well as historical records. This lack of information becomes a problem when implementing predictive maintenance solutions, particularly in monitoring the condition of industrial machines and automatic fault detection. This work addresses this issue in an industrial scenario, through the analysis of vibrations in a spindle motor of an ornamental stone cutting machine. Unsupervised learning techniques are explored for anomaly detection through vibration data, using the training and implementation of an LSTM (Long Short-Term Memory) Autoencoder model. Datasets consist only of unlabeled accelerometer signals acquired during normal machine operation. An analysis based on the extraction of statistical features from the signal is adopted to use them as inputs of the Machine Learning algorithm, to learn the normal behavior of the machine and detect deviations that may correspond to potential anomalies. The experimental results show that even in the absence of labeled data, it is possible to extract meaningful insights from the machine state and establish a practical pipeline for anomaly detection in industrial machines through vibration analysis.