Remote Operation Research Articles

Investigation of 3D Printed Underwater Thruster Propellers Using CFD and Structural Simulations

Unmanned Surface Vehicles (USV) and Autonomous or Remotely Operated Underwater vehicles (AUV, ROV) are developing and spreading rapidly in various industries. A common feature of these vehicles is that they are propelled by small plastic (or metal) propellers in most cases. Additive manufacturing can offer an excellent opportunity for rapid prototyping and the development of new models. This paper aims to investigate the fundamental aspects to be considered in the geometric design and manufacturing of small (diameter less than 100 mm) PLA (Polylactic acid) propellers 3D-printed using Fused Filament Fabrication (FFF) technology. In-service deformation of 3D-printed PLA ducted propellers with average geometry was investigated to determine the effect on the thrust and torque on the blades. For this purpose, one-directional FSI (Fluid Solid Interaction) simulations were performed using CFD (Computational Fluid Dynamics) and structural simulations. The propeller CAD geometries were generated using an in-house MATLAB script. The variable parameters of each version are the thickness, skew, and rake of the propeller blades. For the structural simulations, it was considered that the material properties of PLA parts printed with FFF technology depend on the print orientation. The results of the simulations show that except for extreme geometries (e.g., thin blades, skew, or rake more than 10°), the deformation of small PLA ducted propellers is not significant. CFD studies of the deformed geometries have shown that the resulting deformation has no significant effect on the thrust and torque of the propeller and thruster.

Photothermal Conversion of the Oleophilic PVDF/Ti3C2Tx Porous Foam Enables Non-Aqueous Liquid System Applicable Actuator.

Various physical and chemical reaction processes occur in non-aqueous liquid systems, particularly in oil phase systems. Therefore, achieving efficient, accurate, controllable, and cost-effective movement and transfer of substances in the oil phase is crucial. Liquid-phase photothermal actuators (LPAs) are commonly used for material transport in liquid-phase systems due to their remote operability and precise control. However, existing LPAs typically rely on materials like hydrogels and flexible polymers, commonly unsuitable for non-aqueous liquids. Herein, a 3D porous poly(vinylidene fluoride)(PVDF)/Ti3C2Tx actuator is developed using a solvent displacement method. It demonstrates directional movement and controlled material transport in non-aqueous liquid systems. When subject to infrared light irradiation (2.0Wcm-2), the actuator achieves motion velocities of 7.3 and 6mms-1 vertically and horizontally, respectively. The actuator's controllable motion capability is primarily attributed to the foam's oil-wettable properties, 3D porous oil transport network, and the excellent photothermal conversion performance of Ti3C2Tx, facilitating thermal diffusion and the Marangoni effect. Apart from multidimensional directions, the actuator enables material delivery and obstacle avoidance by transporting and releasing target objects to a predetermined position. Hence, the developed controllable actuator offers a viable solution for effective motion control and material handling in non-aqueous liquid environments.

How was the SME’s Performance in Oman during Covid-19?

The paper examines the performance and the challenges faced by SMEs in Oman during Covid-19. The data was collected through primary and secondary sources. A questionnaire of 100 respondents was used. Descriptive analysis and chi-squared test have been employed in the data analysis. The main findings of the study showed despite the adverse effect of the pandame, the SMEs in Oman have performed well and indicated a resilient and adaptable strategy to cope wth the virus. The reason for this could be attributed to their ability to quickly transition to remote work and online operations. During such a challenging period, some businesses managed to thrive due to their ability to identify opportunities and leverage them. Moreover, the study revealed that financial stability and preparedness are key to navigating the pandemic's uncertainties, as businesses focus on risk management, budget planning, and saving money. In response to unexpected disruptions, businesses have become more cautious and strategic in their financial decisions. SME resilience against future crises is enhanced as a result of SMEs adopting more prudent financial practices. The key challenges faced by SMEs during Covid-19 included delivery delays, reduced income for consumers, and regulation changes. The SMEs in Oman devised severalrisk management strategies, such as maintaining backup plans and setting aside funds, reflect a forward-thinking approach. The findings indicate that businesses in Oman are actively seeking ways to minimize disruptions to their operations and mitigate potential risks. Long-term resilience can be built by such strategic thinking.

Controllogger: A remote monitoring system for decentralized renewable energy sources

The development of a remote monitoring and control system for decentralized renewable energy plants is described in the present paper. The system has a microcontroller-based unit for local storing and data transmission to internet by a GSM modem and a database server to store the data. The proposed architecture is easily extended for controlling a decentralized renewable energy plant by a remote operator. Correlations and performance tests are used to compare the data collected by the system prototype with data collected by a commercial data acquisition system. The results indicate that the Root Mean Square Error (RMSE) is 0.103 m/s for the wind speed measurement and the correlation factor is greater than 0.9996. Overall, the low errors rates indicate that the data have been stored in a similar form to what would be expected with the use of a commercial data logger.

Strategies to Reduce the Use of Fossil Fuels

Mining industry is a substantial consumer of the energy indispensable to power mining and mineral processing equipment and processes. As more and more mine operations move to remote locations, the access to reliable, secure and environment friendly energy sources becomes a key concern. At present, a great majority of remote mines relies heavily on diesel fuel that has to be transported over long distances. In this context, some of the renewable energy sources such as for example wind power or solar energy seem to provide potentially interesting and viable alternatives. Mine operations however, have a very particular character, much different from other industries and from other potential applications of renewable power sources. This paper presents operational conditions of some mining operations, particularly those in remote regions, in the context of their energy needs. The authors analyse current and future capacities to decrease a reliance of remote mines on conventional fuels and energy. The paper analyses and discusses also the conditions to be met by alternative energy sources so that they might become a viable alternative for remote mining operations.

A kick monitoring method for deepwater open-circuit drilling based on convolutional neural network

In the case of shallow drilling in deepwater without installation of riser and subsea BOP, open-circuit drilling is usually used. In the process of open-circuit drilling, drilling fluid is directly returned to the seabed. Therefore, observing the return of seabed drilling fluid through Remotely Operated Vehicle (ROV) has become one of the main methods for monitoring the kick in open-circuit drilling. However, the current ROV monitoring method only relies on the professional technical personnel to observe the video screen, which is difficult to identify the kick quickly and accurately in the early stage. In view of this problem, an intelligent kick monitoring method based on deep learning image recognition technology is proposed in this paper. By collecting the actual video image data shot by ROV and simulating the drilling fluid return morphology under different gas influx using Ansys Fluent software, a typical sample database is constructed. Then, the pretrained GoogLeNet neural network model is used for transfer learning, and the video images shot by ROV on site are used to test the trained model. The results show that this method can identify the kick more quickly and accurately, and the model has good robustness. Using this method can greatly reduce the economic losses in the process of deepwater open-circuit drilling, and provides technical support for safe and efficient drilling of deepwater open-circuit drilling.

Deepwater 3D Measurements with a Novel Sensor System

A novel 3D sensor system for underwater application is presented, primarily designed to carry out inspections on industrial facilities such as piping systems, offshore wind farm foundations, anchor chains, and other structures at deep depths of up to 1000 m. The 3D sensor system enables high-resolution 3D capture at a measuring volume of approximately 1 m3, as well as the simultaneous capture of color data using active stereo scanning with structured lighting, producing highly accurate and detailed 3D images for close-range inspection. Furthermore, the system uses visual inertial odometry to map the seafloor and create a rough 3D overall model of the environment via Simultaneous Localization and Mapping (SLAM). For this reason, the system is also suitable for geological, biological, or archaeological applications in underwater areas. This article describes the overall system and data processing, as well as initial results regarding the measurement accuracy and applicability from tests of the sensor system in a water basin and offshore with a Remotely Operating Vehicle (ROV) in the Baltic Sea.

Remote Control System for Transformers with On-Load Tap Changer

This paper presents a Remote Control System for Transformers with On-Load Tap Changer. This tool has been designed to be implemented in Remote Telecontrol Units (RTU) located in Transformation Centers (CT) with on-load tap changing capability. Its final application is to provide an integral solution of the telecontrolled on-load tap changer from a Remote Operation Control Position (ROCP), located into the electric company ́s operation office.

A mathematical analysis of teleoperator optimization using multiple sensors

As the role of remote vehicle operation gains prominence, ensuring robust wireless communication systems is crucial. Prior studies have delved into various aspects of vehicle teleoperation, including its legal framework and workforce implications. Some have even formulated models to estimate the number of remote operators for large automated vehicle fleets. These models suggest that automated vehicles overseen by remote operators could supplant a significant portion of current driving jobs in the United States. Such findings underscore the need for governmental review and policy adjustment to better accommodate the burgeoning field of teleoperation, which could substantially accelerate the advancement of vehicle automation. The present study focuses on evaluating the reliability of a bi-directional communication channel based on key variable parameters. This assessment aids in determining the optimal parameter settings, thereby enhancing the effectiveness of teleoperation systems.

Research on Key Technologies of Remote Operation and Maintenance System for Substation Automation Equipment

Abstract With the help of automation technology, the efficiency and reliability of substation operations are synchronized. However, the equipment will operate under abnormal problems for a long time. If the problem is not resolved promptly, the consequences will be unimaginable. For timely detection of problems, this paper proposes a method for the construction of remote operation and maintenance systems. The remote operation and maintenance system is mainly composed of two-factor authentication, responsibility area authentication, and permission authentication. The security of remote operations and maintenance operations is guaranteed with the support of these key technologies. This paper enhances the safety and stability of automation equipment by offering remote wake-up, remote reset, historical information retrieval, and forwarding table monitoring services in the operation and maintenance service module. Adopting IoT technology, the core management module of remote management of master stations is constructed to ensure the safety of remote operations and maintenance data interaction between master stations and sub-stations. Simulation experiments are being conducted to evaluate the performance of IoT technology in remote operations and maintenance interactions at the main station. The energy consumption of the data interaction module of substation automation equipment is 0.19 and 0.012 mA for 1 hour and 1 day, respectively, for the equipment to operate for 5-10 days, where the advantages of IoT technology are reflected. The processing time of the operation and maintenance personnel under the traditional operation and maintenance and remote operation and maintenance modes were analyzed through comparative experiments, and the processing time of the device dead time was reduced from 175.46 minutes to 4.356 minutes after using the remote operation and maintenance system, and the efficiency of the operation and maintenance personnel was improved.

Research on Substation Network Security Situational Awareness Strategy and Equipment Remote Operation and Maintenance

Abstract Substation network security is the key to maintaining the stable operation of power systems. In the face of growing threats of network attacks, traditional security protection measures have been brutal to meet the needs of modern power systems. Research on substation network security, situational awareness strategies, and remote operation and maintenance of equipment is essential to improve network defense capability and ensure the continuity and reliability of power supply. This study explores effective security situational awareness methods and remote operation and maintenance techniques to provide new solutions for substation network security. This paper builds an efficient network attack detection model by introducing linear discriminant analysis (LDA) and radial basis function (RBF) neural networks. The experiment uses the KDD Cup99 dataset, which is preprocessed to provide the model training and testing data. The LDA-RBF model in this paper outperforms the traditional RNF neural and BP neural networks regarding recognition rate. Specifically, the recognition rate reaches 90.2% for the Smurf attack and 100% for the Ipsweep attack. The proposed model of the study also performs well in terms of leakage and false alarm rates, with an overall recognition rate of 97.00%. This study proposes a network security situational awareness strategy and equipment remote operation and maintenance method that can effectively enhance substation networks’ security and operation and maintenance efficiency.

Navigating the Challenges of 3D Printing Personalized Medicine in Space Explorations: A Comprehensive Review.

Space exploration has undergone a paradigm shift in recent years, with a growing emphasis on long-duration missions and human habitation on other celestial bodies. Private aerospace businesses are at the forefront of advancing the next iteration of spacecraft, encompassing a wide range of applications such as deep space exploration (e.g., SpaceX) and cost-effective satellite deployments (e.g., Rocketlab). One of the critical challenges associated with prolonged space missions is the provision of personalized medical care. 3D printing technology has emerged as a potential solution, enabling the on-demand production of personalized medical devices and medications. However, the unique conditions of space pose substantial challenges to the successful implementation of 3D printing for personalized medicine. Tremendous scope for research exists in terms of resource utilization and waste management in space ecosystem, robotic and artificial intelligence (AI) enabled tool utilization, remote operability, interplanetary travel, space education and training tools, digital twins, space tourism and in many other aspects of 3D printing for personalized medicine in space explorations.

Industrial metaverse – company perspectives

The utilisation of the industrial metaverse is a topical discussion in the research community as well as within industrial companies. This study aimed to identify industry expectations towards the industrial metaverse. A questionnaire was sent to 18 companies to collect their views on potential industrial metaverse use cases and possible challenges that may occur. The companies identified use cases under eight thematic groups: remote operation and maintenance, sales and marketing, training, guidance and support, knowledge sharing and collaboration, novel services and business opportunities, and flexible utilization and workforce availability. Companies pointed out that challenges in using the metaverse may originate from technology acceptance, technology maturity, data governance, regulations and standards, cyber security and safety and cost. These findings provide company perspective on the industrial metaverse discussion and can be used when researching the topic and developing metaverse solutions further.

High-performance magnetic metal microrobot prepared by a two-photon polymerization and sintering method.

Magnetically-actuated microrobots (MARs) exhibit great potential in biomedicine owing to their precise navigation, wireless actuation and remote operation in confined space. However, most previously explored MARs unfold the drawback of hypodynamic magnetic torque due to low magnetic content, leading to their limited applications in controlled locomotion in fast-flowing fluid and massive cargo carrying to the target position. Here, we report a high-performance pure-nickel magnetically-actuated microrobot (Ni-MAR), prepared by a femtosecond laser polymerization followed by sintering method. Our Ni-MAR possesses a high magnetic content (∼90 wt%), thus resulting in enhanced magnetic torque under low-strength rotating magnetic fields, which enables the microrobot to exhibit high-speed swimming and superior cargo carrying. The maximum velocity of our Ni-MAR, 12.5 body lengths per second, outperforms the velocity of traditional helical MARs. The high-speed Ni-MAR is capable of maintaining controlled locomotion in fast-flowing fluid. Moreover, the Ni-MAR with massive cargo carrying capability can push a 200-times heavier microcube with translation and rotation motion. A single cell and multiple cells can be transported facilely by a single Ni-MAR to the target position. This work provides a scheme for fabricating high-performance magnetic microrobots, which holds great promise for targeted therapy and drug delivery in vivo.

Remote Handling Control System and Operations of Vacuum-Compatible In-Vessel Inspection System

Remote Handling Control System and Operations of Vacuum-Compatible In-Vessel Inspection System

A semi-quantitative risk assessment of remote handling operations on the SPES Front-End based on HAZOP-LOPA

A semi-quantitative risk assessment of remote handling operations on the SPES Front-End based on HAZOP-LOPA

Smart railways: the design and construction of an autonomous inspection and maintenance vehicle

The Railway Inspection and Maintenance Vehicle (RIMV) is a state-of-the-art autonomous ground robot for the railway environment. The robot has specialised hardware tailored for specific functionalities, ensuring optimal performance in its designated tasks. It incorporates a GPS, IMU, and encoder for robust localisation, and it integrates a LiDAR system and a depth camera for collision avoidance. Prioritising safety, the RIMV features an IP camera for real-time environmental monitoring and is equipped with emergency stop mechanisms. An Ubuntu Linux machine manages the robot’s data processing, complemented by a 5G Wi-Fi router for remote monitoring and operation when necessary. ROS2 is the primary platform for robot operations, standing out for its open-source solution and enhanced cybersecurity features. We validated the RIMV in a heritage railway setting with an ultrasonic inspection kit to detect rail-track defects. Beyond this specific application, the RIMV is versatile and designed to serve as a platform for other autonomous inspection and maintenance tasks within the railway sector. Its adaptability highlights its potential to enhance efficiency and safety in railway maintenance, offering invaluable assistance to human operators across varied scenarios.

APPLICATION OF AN ARTIFICIAL INTELLIGENCE SYSTEM TO INCREASE THE INFORMATION CONTENT OF REMOTE CONTROL OF THE OPERATION OF ON-BOARD SYSTEMS OF AIRCRAFT

This paper explores the application of artificial intelligence (AI) systems to augment the information content and efficacy of remote-control operations for on-board systems in aircraft. With the aviation industry rapidly advancing towards automation and digitalization, there is a growing need for intelligent solutions that optimize remote control processes. This study investigates various AI techniques such as machine learning, deep learning, and natural language processing to analyze vast amounts of data generated by on-board systems. By leveraging AI, this research aims to enhance the efficiency, reliability, and safety of remote-control operations in aircraft. Additionally, the paper discusses challenges, potential benefits, and ethical considerations associated with implementing AI in aviation. Through a comprehensive examination of AI applications, this research contributes to advancing the integration of intelligent technologies in aircraft operations, paving the way for more autonomous and adaptive air transportation systems.

A BlueROV2-based platform for underwater mapping experiments

We propose a low-cost laboratory platform for development and validation of underwater mapping techniques, using the BlueROV2 Remotely Operated Vehicle (ROV). Both the ROV and the objects to be mapped are placed in a pool that is imaged via an overhead camera. In our prototype mapping application, the ROV's pose is found using extended Kalman filtering on measurements from the overhead camera, inertial, and pressure sensors; while objects are detected with a deep neural network in the ROV camera stream. Validation experiments are performed for pose estimation, detection, and mapping. The litter detection dataset and code are made publicly available.

A CNN Plastic Detection Model for Embedded Platform of ROV

Plastic pollution has a negative influence on biodiversity especially in aquatic ecosystems, and it has been labelled as one of the greatest dangers to biota. This paper proposes a Convolutional Neural Networks (CNN) based plastic detection model for the embedded platform to identify different shapes of underwater plastics such as bags, bottles, containers, cups, nets, pipes, ropes, snack wrappers and tarps. The model is optimized for Raspberry Pi using OpenVINO framework, with the intention to produce a cost-effective edge system for a Remote Operating Vehicle (ROV) system. The development of the model utilizes a pre-trained object detection model from YOLOv5 and the TrashCan 1.0 dataset, for training and testing. The final model exhibits a good performance, achieving more than 85% accuracy in the overall prediction, which highlights the model’s accuracy and reliability in detecting and classifying underwater plastic shapes. Results from this work highlight the potential of the deep learning (DL) real-time embedded processing at the edge rather by a separate computer on land, using a cost-effective embedded platform.