Operational Safety Research Articles

A Fermatean Fuzzy SWARA-TOPSIS Methodology based on SCOR model for Autonomous Vehicle Parking Lot Selection

Population growth in crowded cities and the resulting increase in vehicle use have led to the problem of insufficient parking. When public parking lots and urban growth are not in coordination, vehicles park on the street and close the crosswalks. In the coming years, this problem will become more complicated with the addition of autonomous vehicles (AVs) to urban traffic. This study addresses the research question of how to effectively select AV parking lots in urban areas experiencing population growth and increased vehicle usage. For this aim, a hybrid Multi-Criteria Decision Making (MCDM) methodology, combining SWARA (Step-wise Weight Assessment Ratio Analysis) and TOPSIS (Technique for Order Preference by Similarity) approaches in a Fermatean Fuzzy (FF) environment is proposed. The decision hierarchy based on the SCOR model has been developed to determine and construct the evaluation criteria. Then, a case study analysis has been applied to selected districts in Istanbul, which is Turkiye's most populous and developing city. Operating expenses, safety and security, and land costs are determined as the most important factors. As a result of the detailed fuzzy analysis, which districts should primarily be chosen for AV parking lots in Istanbul is determined and finally, the robustness and validity of the results obtained by the sensitivity analysis being questioned. The study contributes by providing insights into AV parking lot selection, demonstrating the efficacy of the proposed methodology, and highlighting the importance of addressing this issue in urban planning.

Autonomous Drone Navigation Using Computer Vision

Abstract: Autonomous drone navigation with computer vision is an innovative technology that allows drones to move through intricate surroundings without needing human control. This system uses live visual information to identify objects, steer clear of obstacles, and determine routes, improving operational safety and precision. The project's main objective is to create a visionbased navigation system that combines object detection and obstacle avoidance algorithms through deep learning methods like YOLO (You Only Look Once), alongside real-time sensor fusion. The drone uses computer vision algorithms to process aerial images and automatically changes its flight path to prevent crashes. A personalized dataset of aerial images is generated and utilized for improving object detection skills in various environments. In order to guarantee practicality in real- world situations, the system undergoes validation through simulations and field tests on different terrains, focusing on its resilience in changing environments. Improvements in navigation accuracy and obstacle detection are accomplished by implementing adaptive pathplanning and integrating multiple sensors, guaranteeing the drone's efficient operation in real-life situations. The goal of this method is to enhance the drone's ability to make decisions, minimize human mistakes, and expand its range of potential uses in areas like surveillance, agriculture, and disaster response

Ceramic matrix composites for corrosion-resistant next-generation nuclear reactor systems: A conceptual review of enhancements in durability against molten salt attack

Ceramic matrix composites (CMCs) are emerging as a key material class for enhancing corrosion resistance in next-generation nuclear reactor systems, particularly in high-temperature molten salt environments. These environments, critical for advanced nuclear reactors such as molten salt reactors (MSRs), present severe challenges, including aggressive chemical attacks that degrade traditional structural materials over time. This conceptual review explores the development and application of CMCs to improve the durability and corrosion resistance of nuclear reactors exposed to molten salt attacks. CMCs, which consist of ceramic fibers embedded in a ceramic matrix, offer significant advantages, such as high thermal stability, mechanical strength, and improved corrosion resistance compared to conventional materials. This review examines how CMCs can be tailored to withstand harsh operational conditions, with a focus on the selection of ceramic phases, fiber-matrix interactions, and innovative fabrication techniques that enhance their protective capabilities. Key challenges addressed include the optimization of composite design to resist molten salt corrosion, the effects of temperature on the material properties, and the long-term stability of CMCs under extreme conditions. Advances in surface treatments, coatings, and the development of hybrid CMC systems are also discussed, highlighting their potential to further enhance durability. The review outlines the use of advanced characterization techniques, such as high-temperature corrosion testing and in situ microscopy, to evaluate CMC performance in molten salt environments. Additionally, it identifies knowledge gaps in current research, emphasizing the need for long-term studies on CMC behavior under realistic reactor conditions. This review concludes by proposing future research directions and technological advancements required to integrate CMCs into next-generation nuclear reactor designs, aiming to improve system reliability and operational safety.

Green Edge: Harnessing situational awareness for sustainable systems

Green computing and Building Management Systems (BMS)are closely related through their shared goal of improving energy efficiency and reducing environmental impact. Field controllers that connect with the sensors and actuators at the field level act as an interface between field devices and enterprise applications. Data transfer, protocol conversion, data processing, computing, and control actuators based on predefined logic are the functions of the field controllers. Fast response time is one of the crucial parameters of these field controllers hence reducing the load on these field controllers by enabling Edge computing functionality is very much required. Maharudrayya Nandimath and others [16] in their “Edge Computing in IBMS (Integrated Building Management Systems). (2024 International Conference on Computer Communication and Informatics (iccci. in)) the paper suggested many of the measures to reduce the load on these field controllers. Building Management Systems are essential for optimizing the operation of modern buildings, ensuring comfort, safety, and energy efficiency. By integrating advanced technologies such as Situation Awareness (SA) and Artificial Intelligence (AI), BMS can be significantly enhanced to provide real-time monitoring, predictive maintenance, and intelligent decision-making. This paper explores the potential of combining SA and AI with BMS, leveraging insights from recent research on industrial processes, IoT cloud-edge continuum, and smart cities. The proposed system architecture includes feedback loops and contextual awareness, aiming to improve building operations and reduce energy consumption, thereby supporting global sustainability goals.

Towards an integrated model for predictive well control using real-time drilling fluid data

This paper explores the development of an integrated, real-time predictive model for well control that leverages data from drilling fluids to enhance operational safety and efficiency. Traditional well control methods, often reactive in nature, present significant limitations in managing the risks associated with well control issues, such as blowouts and kicks. By utilizing real-time data from drilling fluids—such as pressure, temperature, flow rate, and mud weight—predictive models enable early detection of potential well control problems, allowing for faster decision-making and proactive management of well stability. Key components of the model include data acquisition systems, real-time analytics, and decision-making algorithms that work together to minimize risks and reduce non-productive time (NPT). The paper also highlights the advantages of predictive well control in improving safety, mitigating operational risks, and its potential future applications in broader drilling operations.

Comparative Study on the Application Efficacy of Small Disturbance Equation versus One-Dimensional Euler Equation in Nozzle Flow Analysis

This paper presents a numerical simulation study of subsonic and supersonic nozzle flow regimes utilizing the Small Disturbance Equation (SDE), implemented through Python to analyze flow stability under various boundary conditions. The SDE, extensively applied in aerospace, meteorology, and fluid mechanics, offers a critical framework for examining aircraft stability and maneuverability, essential for ensuring flight safety. Additionally, its application extends to spacecraft stability and control in aerospace science. The findings from this study reveal that subsonic flows, characterized by their stability and smoothness, respond more predictably under varying boundary conditions compared to their supersonic counterparts. Conversely, supersonic flows demonstrate increased sensitivity to changes in boundary conditions, resulting in more complex flow patterns. This sensitivity underscores the need for precise control mechanisms in supersonic applications to maintain flow stability and ensure the safety and efficiency of aerospace operations. The simulations underscore the practical importance of the SDE in advancing the understanding of dynamic flow problems across different scientific and engineering disciplines.

Development and Evaluation of a Robust UART Communication System with Enhanced Interference Resistance

Interference is a significant issue in UART communication, often causing data jitter and distortion, particularly in industrial environments. This study addresses these challenges by developing an anti-interference UART system that includes the design and implementation of receiving, processing, and sending modules. The system utilizes techniques such as multi-level sampling, synchronization, and cumulative decision-making to enhance data transmission accuracy and reliability. In embedded systems, electromagnetic interference (EMI) poses a major problem, leading to potential data loss and communication failures. Reliable communication is crucial for maintaining the performance and safety of industrial operations. The anti-interference UART system developed in this study aims to improve operational stability by mitigating the effects of EMI. The system’s performance was validated through extensive simulations, demonstrating its ability to maintain stable and accurate data transmission even under various interference conditions. The results show significant improvements in data integrity and communication reliability. This paper presents a comprehensive approach to designing, implementing, and validating a robust UART system that can effectively operate in challenging environments, ensuring reliable data communication and enhancing overall system performance.

Image-based chip detection during turning

AbstractThis study proposes a method to analyze chip formation using camera surveillance to enhance safety and efficiency in machine operations. The process involved the face and straight turning of a workpiece under the observation of a camera strategically placed within the workspace. The suggested algorithm carries out initial image preprocessing and edge detection, followed by background subtraction to isolate dynamic elements and filtering based on the size of the objects. Pre-determined masks are applied to eliminate overlaps with existing workspace objects, based on the tool’s trajectory. The research validates that the applied technique effectively recognizes chips in both face and straight turning. Specific filtering techniques improve the algorithm’s capability to detect even smaller chips, and it substantially reduces false alarms, laying the groundwork for long, continuous chip detection systems.

CONFORMIDADE DE PAINÉIS ELÉTRICOS COM A NR-10 E NBR 5410

As modern industry integrates into the era of automation 4.0, characterized by information exchange and connectivity, there are still industries with outdated processes and equipment. The NBR 5410 and NR-10 standards play crucial roles, stipulating requirements and safety measures for low-voltage electrical installations and protecting the health and safety of workers. Electrical panels, fundamental in this context, regulate industrial processes automatically and without human intervention, ensuring not only efficiency and productivity, but also safety and effective maintenance of operations. In view of the above, the study aims to analyze how electrical panels can be configured and adjusted to comply with Brazilian standards NR-10 and NBR 5410. This study adopts bibliographic methodology, extensively reviewing academic literature, articles, technical standards and relevant related documents the installation of electrical panels. A careful selection of primary and secondary sources will be made to ensure a comprehensive understanding of current and emerging practices in the field. The process includes an analysis of extracted data to identify patterns, challenges and opportunities for innovation, providing a solid basis for discussion and conclusions in the study. In this way, the application of these standards contributes to a cycle of continuous improvement in the electrical industry. By establishing safe and efficient procedures, standards not only protect the individuals directly involved, but also benefit society as a whole by ensuring the integrity and security of critical infrastructures.

Gas Leakage Risk Management Of Biogas Powerplant Using Aloha Gas Dispersion Modelling And Bowtie Analysis

Biogas, a fuel derived from organic waste, has gained popularity as a sustainable energy source. The adaptability of biogas allows its utilization for electricity and heating. This study aims to evaluate the risk of biogas leakage using ALOHA gas dispersion modeling and Bowtie analysis to identify hazard zones and evaluate the effectiveness of preventive and mitigation measures. The research method using Gas dispersion modeling in ALOHA software can simulate the spread of leaked biogas, identify potential hazard zones, and inform emergency response planning. The results show that gas dispersion reaches up to 296 meters from the leak source in a worst-case scenario without ignition, with significant implications for safety. The ignition scenario produces high thermal radiation, further exacerbating the risk. In conclusion, these findings highlight the intolerable risk of biogas leakage, requiring comprehensive risk mitigation measures. This study recommends the implementation of a robust control system, routine maintenance, and emergency response plan to ensure the safety and sustainability of biogas facility operations

An operational risk assessment method for petrochemical plants based on deep learning

The petrochemical industry is an important guarantee for the development of people’s lives, and the operational risk assessment method of personnel in the operation of petrochemical enterprises is the most important. Based on a deep learning algorithm, a new method based on the micro-Doppler effect and fuzzy analytic hierarchy process is proposed to evaluate the operational risk of personnel in petrochemical enterprises. The original monitoring image of petrochemical equipment is invoked, and micro-Doppler analysis is performed based on the original image to identify the activity target of the personnel on the job site and generate the activity map of the petrochemical plant operators. Based on the analysis data of the micro-Doppler effect, the fuzzy function and hierarchical analysis method are combined. The fuzzy theory is introduced into the analytic hierarchy process, and the inherent expert evaluation scores are transformed into fuzzy numbers. It makes the expert evaluation of petrochemical plants more accurate in the subsequent coupling and improves the objectivity of the traditional analytic hierarchy process. The scheme proposed in this paper can monitor real-time operation safety and provide a guarantee for the personal safety of field operators. This method plays an important role in improving the safety of petrochemical plants.

Enhancing Tram Safety in Mixed-Traffic Systems: A Case Study of Melbournes Tram Collision Factors and Improvement Strategies

Abstract: Trams, a vital component of public transportation, operate within diverse environments ranging from fully segregated tracks to mixed-traffic systems. This study investigated tram collisions in Melbournea city renowned for its extensive and historic tram networkand explored both systemic and behavioral factors contributing to these incidents. The analysis identified key factors such as the arrangement of tram platforms, intersection design, right-of-way (ROW) regulations, tram driver behavior, and the actions of other road users. The study revealed that inadequate tram platform arrangements and suboptimal intersection designs are major contributors to tram collisions. Recommendations for improvement include implementing elevated, standalone tram platforms with protective barriers and accessible ramps, expanding the use of Hook Turn intersections, and integrating intelligent transportation systems to enhance traffic management. Additionally, addressing behavioral factors is crucial. Enhanced training for tram drivers and educational campaigns for pedestrians and other road users are proposed to improve situational awareness and adherence to safety norms. By addressing both objective systemic issues and subjective behavioral factors, this study aimed to enhance the safety of tram operations in mixed-traffic environments, ultimately reducing collision risks and improving public confidence in tram systems.

Applying FMEA for Operational Risk Management: Lessons from Minestar Implementation at PT. Borneo Indobara

PT Borneo Indobara (BIB) is planning to implement Minestar, an integrated mining management system, in 2025 to enhance operational efficiency and safety. However, the implementation of this system poses several operational risks that require careful management to ensure success. The aim of this research is to develop a comprehensive risk matrix and propose effective mitigation strategies for the Minestar implementation at BIB, using the SNI ISO 31000:2018 risk management framework. The research employs a mixed-method approach, combining qualitative techniques such as semi-structured interviews and focus group discussions (FGD) with quantitative analysis using Failure Mode and Effect Analysis (FMEA). Data was collected from key stakeholders directly involved in the Minestar project to assess the severity, occurrence, and detection of each identified risk, which were then used to develop a risk matrix. The results of the study identified and prioritized several critical operational risks, with a three-dimensional risk matrix providing a clear visualization of their severity, occurrence, and detection levels. This structured approach allows BIB's management to prioritize risks and allocate resources effectively for risk mitigation. The findings of this research contribute to operational risk management in the mining industry, particularly for the implementation of advanced technologies like Minestar. The practical insights provided can serve as a reference for similar projects in the industry, helping organizations to address operational challenges proactively.

Viscoelastic modeling based force control framework for enhancing operation safety of soft tissue for orthopedic surgical robots

In robot-assisted laminectomy, precise control of touch force on the spinal cord during the dissection of ossified ligaments is critical to prevent dura mater tearing and ensure surgical safety. However, the complex viscoelastic characteristics of tissue at the lesion site pose a significant challenge for accurately applying touch force. This paper proposes a safe touch force servo framework based on tissue model identification and preoperative optimization of the force controller. First, a soft tissue model is established using elastic and viscoelastic elements, and the transfer function from tissue deformation to touch force is derived. Subsequently, the path and parameters of a safe pre-touch are designed, and force information required for tissue model identification is obtained through the pre-touch experiment. The model order and specific parameter values are then identified using a differential evolution algorithm. Next, force control simulation is conducted based on the identified model, and a specifically designed loss function is introduced to achieve preoperative tuning of the force controller. Finally, step force control experiments on sheep spines validate the effectiveness of the proposed method. This paper provides a safe and available touch motion control framework that can be expandable to impose precise force on various vulnerable soft tissues, with the potential for widespread application.

A novel simulation of space charge decay dynamics after removing the voltage in epoxy resin composites

Abstract Epoxy resin serves as a critical insulating component in ultra-high voltage dry direct current bushings. However, the accumulation of space charges within the epoxy resin, a byproduct of charge mobility, poses a significant risk to the reliability and operational safety of bushings. Conventional space charge attenuation models, especially after voltage removal, have limited use in simulations aimed at understanding this phenomenon. This study introduces an improved model integrating the bipolar charge transport mechanism with space charge decay based on the hopping conduction mechanism and Schottky’s theorem, establishes a theoretical framework for predicting the space charge behavior following voltage removal, and conducts a simulation to investigate the decay process within the internal structure of epoxy resin and measure the residual charges after voltage removal using the pulsed electro-acoustic method. The experimental data validate the accuracy of the proposed model and theoretical assumptions. The findings show that the remaining negative charges after voltage removal are not enhanced by the field enhancement; the anodic positive and cathodic positive charges are distributed in two-segment discontinuous traps, whereas the negative charges are distributed in one-segment traps. The model identifies two distinct trapping sites for anodic and cathodic positive charges, and one trapping site for negative charges. After voltage removal, when the field strength exceeded 40 kV mm−1, positive charges exist near the upper and lower electrodes, and negative charges exist near the center of the specimen. The consistency between the simulated predictions and experimental data proves the effectiveness of the proposed model in accurately simulating the space charge decay in epoxy materials after voltage removal.

Research on anti-swing control system of slewing crane based on fuzzy PID.

The slewing crane is easily affected by the wind and the manipulation level of the operator when it is working, which in turn impacts its swing angle, affects the working efficiency and safety of the crane. Toimprove the operation safety andreduce the swing angle, this study design and research the anti-swing control system for the slewing crane. Firstly, based on the Lagrange equation, the dynamic model of the crane hoisting system is established. Then, the mechanical anti-swing mechanism driven by a four-motor rope is established. Finally, based on fuzzy PID control, an anti-swing platform is established, and the performance of anti-swing control is verified. Compared to no anti-swing control, the in-plane swing angle θ1 and out-of-plane swing angle θ2 of the lifting arm are reduced by 88% and 75% respectively. The results show that the four-motor rope-driven anti-swing mechanism can achieve better anti-swing effect, which provides an effective swing suppression method for the slewing crane in the actual operation.

Impact of rogue waves on semi-submersible platforms: An experimental investigation of wave run-up and air-gap responses

The wave run-up and air-gap responses of semi-submersible platforms under extreme sea conditions are crucial design considerations for ensuring the structural integrity and safety of marine engineering operations. This study investigated the impact of rogue waves on the wave run-up and air-gap responses of semi-submersible platforms using a transient focusing wave group to accurately simulate irregular seas. Comparative model tests were conducted on a semi-submersible platform under both rogue and non-rogue-wave conditions. The platform motions and air-gap responses were analysed using spectral analysis, extreme-value statistics, and wavelet methods. Rogue waves were found to significantly affect the air-gap response spectrum, increasing the nonlinearity of both the wave run-up and air-gap responses. Different locations on the platform exhibited varying statistical and spectral response characteristics. Rogue waves modified the instantaneous properties of wave elevation, thereby increasing the energy levels in the system. This study confirmed that rogue waves significantly influence the air-gap responses and wave run-up of semi-submersible platforms, suggesting that they should be considered when designing and planning the operations of these platforms. This study offers a comprehensive understanding of the complex interactions between rogue waves and large maritime structures and provides insights for improving safety and performance under extreme sea conditions.

Visual-Inertial Fusion-Based Five-Degree-of-Freedom Motion Measurement System for Vessel-Mounted Cranes

Vessel-mounted cranes operate in complex marine environments, where precise measurement of cargo positions and attitudes is a key technological challenge to ensure operational stability and safety. This study introduces an integrated measurement system that combines vision and inertial sensing technologies, utilizing a stereo camera and two inertial measurement units (IMUs) to capture cargo motion in five degrees of freedom (DOF). By merging data from the stereo camera and IMUs, the system accurately determines the cargo’s position and attitude relative to the camera. The specific methodology is introduced as follows: First, the YOLO model is adopted to identify targets in the image and generate bounding boxes. Then, using the principle of binocular disparity, the depth within the bounding box is calculated to determine the target’s three-dimensional position in the camera coordinate system. Simultaneously, the IMU measures the attitude of the cargo, and a Kalman filter is applied to fuse the data from the two sensors. Experimental results indicate that the system’s measurement errors in the x, y, and z directions are less than 2.58%, 3.35%, and 3.37%, respectively, while errors in the roll and pitch directions are 3.87% and 5.02%. These results demonstrate that the designed measurement system effectively provides the necessary motion information in 5-DOF for vessel-mounted crane control, offering new approaches for pose detection of marine cranes and cargoes.

Enhancement of Operational Safety in Marine Cargo Cranes on a Container Ship Through the Application of Authenticated Wi-Fi Based Wireless Data Transmission from Multiple Sensors

The use of wireless technology in common marine engineering applications as a medium for data transaction in measurement and control systems, is not as popular as it should be. This article aims to demonstrate the advantages of using wireless technology in maritime engineering applications through a proposed Wi-Fi based wireless system dedicated to performance and safety monitoring in marine cargo cranes. The system is based on some concepts that were suggested in the earlier literature to perform authenticated data transmission from multiple sensors through using both the ESP-NOW protocol and the WebSerial remote serial monitor. The introduced system will be integrated with an already installed system in order to render the means for implementing effective principles in automation and control engineering, such as functional safety and predictive maintenance. Additionally, this article will highlight the economic efficiency of adopting wireless technology instead of cabling as a medium for data transaction in measurement and control systems in marine engineering applications such as cargo cranes.

Dynamic Temporal Denoise Neural Network with Multi-Head Attention for Fault Diagnosis Under Noise Background

Fault diagnosis plays a crucial role in maintaining the operational safety of mechanical systems. As intelligent data-driven approaches evolve, deep learning (DL) has emerged as a pivotal technique in fault diagnosis research. However, the collected vibrational signals from mechanical systems are usually corrupted by unrelated noises due to complicated transfer path modulations and component coupling. To solve the above problems, this paper proposed the dynamic temporal denoise neural network with multi-head attention (DTDNet). Firstly, this model transforms one-dimensional signals into two-dimensional tensors based on the periodic self-similarity of signals, employing multi-scale two-dimensional convolution kernels to extract signal features both within and across periods. Secondly, for the problem of lacking denoising structure in traditional convolutional neural networks, a temporal variable denoise (TVD) module with dynamic nonlinear processing is proposed to filter the noises. Lastly, a multi-head attention fusion (MAF) module is used to weight the denoted features of signals with different periods. Evaluation on two datasets, Case Western Reserve University bearing dataset (single sensor) and Real aircraft sensor dataset (multiple sensors), demonstrates that the DTDNet can reduce the useless noises in signals and achieve a remarkable improvement in classification performance compared with the state-of-the-art method. DTDNet provides a high-performance solution for potential noise that may occur in actual fault diagnosis tasks, which has important application value.