Expander system passes 50 000 hours of testing - The end of lug wear

PIVOT WEAR - With Expander System, the repair can be carried out directly in the worn mountings without welding and line boring, a solution that is permanent -The Nord-Lock Expander - PPS UK are the distributor for West Africa. Email james.wood@processandplant.co.uk

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Expander system passes 50 000 hours of testing - The end of lug wear

This document provides information on Flexco's Bolt Solid Plate fastener system for heavy-duty belt conveyor applications. It summarizes the key features and benefits of the Bolt Solid Plate fastener system, including its superior strength from compression of plates mounted above and below belts, deep belt carcass penetration without damage, and piloted bolts for easy installation. Installation guidelines and a selection guide are provided to help customers choose the appropriate fastener size and splice pattern for their conveyor belts and pulley diameters. Ordering information and installation tools are also listed.

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THE END OF LUG WEAR - NORD-LOCK EXPANDER

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Les rondelles-freins Nord-Lock sécurisent les assemblages vissés avec la tension au lieu de la friction. Il s’agit d’un produit haut de gamme qui remporte depuis longtemps déjà un grand succès dans de nombreuses industries à travers le monde. Les rondelles de sécurité Nord-Lock ont été rigoureusement testées et approuvées aussi bien par des instituts indépendants que par des autorités de certification. Elles sont utilisées dans de nombreuses industries telles que la production d’énergie, les transports en commun, la production offshore, l’exploitation des mines et carrières, la construction, l’agriculture et l’exploitation forestière, les poids lourds et les équipements militaires. Nos rondelles freins sont approuvées par plusieurs normes spécifiques aux industries et spécifiées par de nombreuses entreprises internationales. Durant toute la vie d’un produit, les rondelles par effet de coin Nord-Lock améliorent la fiabilité des assemblages vissés et réduisent les coûts de maintenance tout en diminuant de manière significative les risques d’arrêt de production, d’accidents et de recours en garantie.

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Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network

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Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.

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Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.

DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL

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As digital technology becomes more deeply embedded in power systems, protecting the communication networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3) represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities. Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network (CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to train and test our model. The results of our experiments show that our CNN-LSTM method is much better at finding smart grid intrusions than other deep learning algorithms used for classification. In addition, our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection accuracy rate of 99.50%.

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A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS

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The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.

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