Industry 4.0
Vol. 9 (2024), Issue 3

Table of Contents

  • TECHNOLOGICAL BASIS OF “INDUSTRY 4.0”

    • Simulation experiment for the follow-up controller of the MIMO system

      pg(s) 86-89

      Controlling Multi-Input Multi-Output (MIMO) systems, such as portal conveyors, poses significant challenges due to their inherent complexity and variability. Traditional control methods often fall short in handling the dynamic and nonlinear nature of these systems. This paper presents a novel reinforcement learning (RL) approach, leveraging the twin-delayed deep deterministic policy gradient (TD3) algorithm, to develop a follow-up controller that is robust to changes in system parameters. Our simulation experiments demonstrate the effectiveness of this method.

    • Modeling solar data using artificial neural networks for solar applications in transport infrastructure

      pg(s) 90-93

      This paper examines an approach using artificial neural networks for innovative modeling of solar data that is needed to realize solar applications for transport infrastructure purposes. Through this modeling, detailed solar data are generated by geographical positions, and monthly and annual maps are created for the territory of Bulgaria for horizontal solar irradiation with its diffuse and direct components and for inclined and reflected solar irradiation, according to Norio Igawa’s model. Diffuse fraction and horizontal and inclined solar irradiation can be helpful in designing solar applications in road infrastructure, such as power signaling systems and street lighting. By demonstrating the capabilities of accurate modeling and analysis of solar data, this paper highlights the importance of applying artificial neural networks in planning and improving the resilience of transport infrastructure against climate change. Using solar energy in transport infrastructure reduces carbon emissions and strengthens environmental sustainability.

    • Trends in non-linear MIMO Objects Control in the Era of Industry 4.0: The Use of Artificial Neural Networks

      pg(s) 94-96

      The Industry 4.0 revolution has significantly influenced the control of non-linear Multiple Input Multiple Output (MIMO) systems, particularly through the application of artificial neural networks (ANNs). This paper explores current trends in the control of non-linear MIMO objects, emphasizing the role of ANNs in enhancing performance and efficiency. Key developments, methodologies, and case studies are reviewed to illustrate the impact of ANNs on non-linear MIMO control

    • Review of feature selection methods for Predictive Maintenance Systems

      pg(s) 97-100

      The development of Industry 4.0 and Predictive Maintenance Systems allows for effective utilization of equipment by incorporating ML methods for identifying tool condition. However, including large number of Condition Indicators for machinery monitoring increases computational complexity, hence the response of the system elongates. Therefore, it is important to check the utility of indicators and reduce them. In this paper, we investigate different feature selection methods: AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), Random Forest, Lasso Regression (L1 Regularization) for NASA Gearbox Fault Detection Dataset, PHM 2009. We processed the raw data and calculated CI from time domain, frequency domain and envelope. An SVM Classifier model was trained on full collection of indicator and reduced, then performances were compared. The obtained results highlight the advantage of feature selection, proving that effective PdM systems can be based on diminished number of Health Indicators.

  • DOMINANT TECHNOLOGIES IN “INDUSTRY 4.0”

    • Implementing predictive analysis using self-learning digital twins and image analysis with GPT-4 turbo with vision for inspection and repair of construction

      pg(s) 101-104

      Nowadays, many structures should be inspected, analyzed, and repaired. This is a complex and expensive process that also includes predictive analytics to prevent possible construction failures.
      One of the most used predictive analytics applications involves extracting necessary metadata from images and videos to evaluate the condition of real-world systems and recommend measures to sustain these systems. Image analysis is not a new concept – many solutions have been used for several decades.
      The current paper mainly focuses on OpenAI-based capabilities to implement Image Analysis and Cognitive Digital Twins and proposes faster, cheaper implementation and more adaptive approaches to offering predictive analysis for constructions.
      ChatGPT (Chat Generative Pre-Trained Transformer) is one of the trending technologies in modern Artificial Intelligence (AI), and experts in this area expect to have a very high impact on the industry shortly.
      One of the latest versions – GPT-4 Turbo with Vision, developed by OpenAI, is a significant multimodal model (LMM) capable of interpreting images and providing text-based answers to queries regarding those images. It combines capabilities in natural language processing and visual comprehension.
      The proposed approach considers using OpenAI LLM and Digital Twins for three different aspects of predictive analysis for Construction: image analysis, case decomposition, and creation of self-adaptive models to find possible trends to compromise structures and offer preventive actions. This research compares traditional methods for inspection and repair of Construction, including the time required for predictive analysis, the correctness of the proposed actions, and the cost of the methodology.

    • Preparation and Deployment of Secure Over-The-Air Updates for Embedded Devices: Challenges and Solutions

      pg(s) 105-109

      This paper provides a comprehensive overview of the preparation and deployment of secure Over-The-Air (OTA) updates for embedded devices. In the era of intelligent devices, the need for remotely updating software and firmware has become increasingly prevalent. This paper discusses the principles of OTA updates, focusing on the various types used in the field of embedded devices and the complications that need to be resolved for a successful process. Furthermore, it delves into the theoretical description of OTA updates, security mechanisms, and practical implementation. The paper also highlights the main benefits of OTA updates, such as convenience, security, scalability, reduced maintenance costs, and improved performance. Additionally, it addresses the challenges and issues associated with OTA updates, including update reliability, security risks, compatibility, network propagation, and integrity and warranty implications. Overall, this paper serves as a valuable resource for understanding the complexities and importance of OTA updates in the realm of embedded devices.

    • Small-Scale Photovoltaic Power Systems: Identifying Vulnerabilities and Recommending Security Protocols

      pg(s) 110-112

      The integration of small-scale photovoltaic power systems into residential and commercial buildings is rapidly gaining popularity. Despite the original design specification for these systems to function solely on local networks or without internet connectivity, they are often linked to installers’ or building managers’ monitoring systems. The substantial surge in demand and deployment of photovoltaic systems necessitates an examination of their susceptibility to a broad spectrum of threats, encompassing both physical and cyber-attacks. This analysis endeavours to identify the components of these systems and evaluate the associated security vulnerabilities. Additionally, it will include a practical demonstration of selected attacks on a representative system to illustrate potential threats. The analysis will culminate in a summary of recommended security protocols aimed at fortifying the security and robustness of PV systems.

  • SOCIETY & ”INDUSTRY 4.0”

    • Robotic Applications in Medical Science: Current Advances and Future Prospects

      pg(s) 113-117

      Over the past four decades, the field of medical robotics has achieved remarkable advancements, revolutionizing various medical disciplines. The widespread adoption of robotic platforms across various medical disciplines has been remarkable. Currently, these devices play a crucial role in performing minimally invasive surgical procedures with enhanced precision, resulting in reduced hospitalization and increased safety for physicians. Beyond surgical applications, medical robots are increasingly proving their worth in performing routine tasks, thereby enabling less invasive and more informative diagnostic and therapeutic procedures. Furthermore, the integration of artificial intelligence (AI) holds great promise for developing new systems with higher autonomy levels and improving existing ones. This paper describes a short view on history of the development of robotics from the beginning to the current state along with a brief outline of the future direction of development of medical robotics. At first describes the process of development from the first medical robotic device prototype to the modern minimally invasive surgical devices currently used in medical practice. Then it presents expansion of robotics into other medical fields, including ophthalmology, gastroenterology, cardiology and cardiac surgery, physiotherapy, or radiology. Finally, it describes some perspectives for future development in medical robotics, as well as the obstacles, that need to be overcome to improve the efficiency and level of autonomy, in the systems.