Ensure Personnel Safety Research Articles

Fault detection method for electric vehicle battery pack based on improved kurtosis and isolation forest

ABSTRACT Early detection of abnormal battery characteristics is crucial for ensuring personnel safety and minimizing property damage. Conventional fault diagnosis methods often struggle to detect minor faults in their early stages. To address this challenge, this paper proposes a fault diagnosis method for lithium-ion batteries in electric vehicles that utilizes real-world operational data. Initially, a wavelet threshold denoising algorithm is used to effectively remove voltage data noise while retaining fault characteristics. Subsequently, an early fault warning method based on improved kurtosis index is proposed, which is capable of capturing the weak abnormal features of the battery and issuing warnings. Furthermore, a faulty battery cell localization method based on two-dimension feature extraction and Isolation Forest algorithm is proposed to capture minor fault features and accurately locate faulty cells. Finally, experiments conducted on four operational electric vehicles demonstrated that the early fault warning method could promptly detect faults and issue alarms, while the faulty battery cell positioning method accurately identified the faulty cell, achieving 100 % Accuracy and 0 % False Positive Rate. This confirms the effectiveness of the proposed method. Additionally, Bootstrap was used to evaluate the performance metrics, further verifying the robustness of the method.

Justification of an innovative system for the complete burial of solid, high-level radioactive waste (HLW) in spent open-pit mines

The object of research is the spent open-pit mines themselves where the proposed system could be applied. The primary reason for this studying is the following circumstance: up to the present time period, in all countries of the world, no methods of HLW disposal in a storage facility has been identified that is absolutely safe for any length of time, taking into account the impact of catastrophic natural emergencies and man-made emergencies. The research was conducted to address the problem of safely managing and storing HLW, leveraging the unique characteristics of spent open-pit mines, such as their large volume and geological stability, to prevent environmental contamination and ensure long-term safety. In the article has been justified a novel approach to the burial of sarcophagus containers with solid HLW in exhausted mining pits and studied the usabilities of the basalt sarcofagous container. Robust materials and advanced robotic systems proposed in the article aims to address the challenges associated with long-term radioactive waste disposal effectively. The robotic systems transfer the basalt container with HLW, ensuring personnel safety by minimizing human presence near radioactive materials. In the article have been established the distribution of temperature into the multi-layered composite structure of the basalt sarcophagous with HLW from 300 °C into the inner space to 50 °C onto on the its outer suffer where the thickness of each layers (from inner to outer radius) was respectively: for lead matrix: from r1=0.1 m to r2=0.2 m; for clay layer: from r2=0.2 m to r3=0.3 m; for basalt block: from r3=0.3 m to r4=0.4 m. The findings on temperature distribution are crucial as they directly affect the performance and longevity of the basalt containment system

A Tunnel Fire Detection Method Based on an Improved Dempster-Shafer Evidence Theory

Tunnel fires are generally detected using various sensors, including measuring temperature, CO concentration, and smoke concentration. To address the ambiguity and inconsistency in multi-sensor data, this paper proposes a tunnel fire detection method based on an improved Dempster-Shafer (DS) evidence theory for multi-sensor data fusion. To solve the problem of evidence conflict in the DS theory, a two-level multi-sensor data fusion framework is adopted. The first level of fusion involves feature fusion of the same type of sensor data, removing ambiguous data to obtain characteristic data, and calculating the basic probability assignment (BPA) function through the feature interval. The second-level fusion derives basic probability numbers from the BPA, calculates the degree of evidence conflict, normalizes the BPA to obtain the relative conflict degree, and optimizes the BPA using the trust coefficient. The classical DS evidence theory is then used to integrate and obtain the probability of tunnel fire occurrence. Different heat release rates, tunnel wind speeds, and fire locations are set, forming six fire scenarios. Sensor monitoring data under each simulation condition are extracted and fused using the improved DS evidence theory. The results show that there is a 67.5%, 83.5%, 76.8%, 83%, 79.6%, and 84.1% probability of detecting fire when it occurs, respectively, and identifies fire occurrence in approximately 2.4 s, an improvement from 64.7% to 70% over traditional methods. This demonstrates the feasibility and superiority of the proposed method, highlighting its significant importance in ensuring personnel safety.

Continuous charging assignment algorithm for heterogeneous robot clusters based on E-CARGO

Forming robot clusters is a common means to improve work efficiency and ensure personnel safety in intelligent logistics warehousing. However, designing a reasonable charging sequence and time for each robot in the cluster poses a major challenge for management personnel. In actual scenarios, the working and charging processes of the robots must be considered simultaneously. This study also considers multiple constraints on robot battery levels and available workstations under different parameters to ensure each team operates normally at any given time. Additionally, to enhance cooperation efficiency and alleviate the workload of management personnel, the problem is transformed from conventional one-time assignments to adaptive continuous assignments within fixed time periods. Accordingly, the ACP-GRAC problem is proposed. Firstly, a group role assignment model with conflicts is introduced to resolve assignment conflicts caused by mismatches between job skills and position requirements. Then, energy information is shared among robots during the charging process, adding adaptive cooperation concepts to address continuous charging issues. Finally, the E-CARGO model is utilized to formalize the ACP-GRAC problem, and the HCCA algorithm is designed for its solution. Furthermore, a classification backtracking strategy is proposed to simultaneously address the constraint of continuous operation at any given time and the problem of constraint violations caused by the HCCA algorithm’s pursuit of maximum team performance. Moreover, since the classification backtracking strategy can compute the theoretical minimum remaining energy of the robot before departing for charging, it further optimizes the overall work efficiency. Additionally, this study addresses the issue of low cluster work efficiency due to the unreasonable initial planning sequence of the algorithm. In the experimental section, comparisons are made with recent excellent heuristic algorithms and deep reinforcement learning algorithms. Experimental results demonstrate that B-HCCA exhibits significant advantages in both solution efficiency and solution quality when addressing adaptive cooperation problems with group role assignment conflicts.

Drone inspection path planning with self-organizing neural network of elastic Hebbian learning rule

Abstract As the scale of the power grid continues to expand, inspection scenarios become increasingly complex, and inspection and maintenance work in the power industry faces huge challenges. The traditional manual inspection mode has problems such as high cost, low efficiency, and poor safety. Using electric drones to inspect all equipment nodes in the area can effectively reduce operation and maintenance costs and ensure personnel safety. As a key technology to realize autonomous power inspection, UAV path planning needs to overcome difficulties such as long solution time and intersection of inspection paths. Therefore, this article conducts research on UAV path planning algorithms for large-scale power inspections, which is of great significance to improving the efficiency of power inspections. To avoid the occurrence of path intersections and improve the path planning effect, this paper uses the convex hull property in graph theory to design elastic Hebbian learning rules and proposes a self-organizing neural network algorithm based on elastic Hebbian learning rules.

Symptoms of Posttraumatic Stress Disorder Among Japanese Peacekeepers Deployed in South Sudan

Peacekeepers in United Nations missions experience potentially traumatic events, resulting in increased risk for posttraumatic stress disorder (PTSD). Understanding the course and risk factors of PTSD symptom severity is crucial to ensure personnel safety. To investigate the incidence of PTSD, symptom severity trajectories, and potential risk factors associated with adverse trajectories among Japanese peacekeepers deployed in South Sudan. Data for this 6-year prospective cohort study were collected from December 2011 to December 2018 from Japan Ground Self-Defense Force personnel deployed at the United Nations Mission in South Sudan, corresponding to before and up to 78 months after deployment. Of 3799 potential participants, 80 were excluded because of multiple deployments and 757 because of missing data, incomplete responses, or not providing informed consent. Therefore, 2962 participants were included in the analysis. Data analysis was performed from February 2022 to February 2024. The participants were deployed to United Nations Mission in South Sudan for 6 months. The 22-item Impact of Event Scale-Revised was used to assess PTSD symptoms, with 25 or more points indicating probable PTSD (p-PTSD). Sociodemographic data were collected, and the 30-item General Health Questionnaire was administered before deployment to identify risk factors for PTSD symptom severity. In the 2962 participants studied (2901 [97.9%] male; mean [SD] age, 33.9 [7.2] years), the incidence of p-PTSD was 3.95%. Latent growth mixture models identified 4 symptom severity trajectories: resilient (2143 [72.3%]), recovery (479 [16.2%]), protracted (182 [6.1%]), and delayed (158 [5.3%]). Multinomial logistic regression showed that sleep disturbance was a common risk factor for the 2 most severe trajectories (protracted: odds ratio [OR], 1.29; 95% CI, 1.08-1.54; delayed: OR, 1.26; 95% CI, 1.03-1.53), whereas older age (OR, 1.25; 95% CI, 1.06-1.48), anxiety and dysphoria (OR, 1.45; 95% CI, 1.20-1.75), and general illness (OR, 1.30; 95% CI, 1.06-1.59) were associated with the protracted trajectory. This cohort study found that approximately 4% of the participants developed p-PTSD and identified 4 distinct PTSD symptom trajectories. The findings suggest that addressing sleep disturbance and general health issues could effectively prevent PTSD symptoms among peacekeepers.

Experimental study on the mechanical properties of FRP reinforced shotcrete lining of tunnel in extremely fractured surrounding rock

Tunnel excavation in the southwestern area of China encounters many difficulties caused by the large deformation of extremely fractured surrounding rock, which is the product of high plate tectonic stress. Improving the initial cracking strength and ductility of shotcrete layer is one of the most important means to reduce the frequency of support structure repair, which will improve tunnel construction efficiency and ensure personnel safety. In this case, this paper introduced fiber reinforced polymer (FRP) mesh to replace the steel mesh as reinforcement to optimize the support effect of shotcrete lining. Both large-scale model test and mechanical test are conducted to compare the reinforcement effect between FRP and steel mesh. Research results indicate that replacing steel mesh with FRP can increase the ultimate deformation capacity of tunnel lining by more than four times. The parametric analysis results indicate that increasing the lining thickness, the diameter of the FRP bars and the mesh density have different degrees of effect on improving the stiffness and strength of tunnel lining. The research results can provide reference for relevant projects.

Integration of Fire Detection and Personnel Accountability System

Fire incidents in public spaces pose significant risks to life and property. Traditional fire detection systems often suffer from delays and inaccuracies, necessitating the development of advanced solutions. This paper presents a comprehensive system for integrating fire detection and personnel accountability in surveillance scenarios. With the increasing concerns about safety in public spaces, the need for effective systems to detect fires and ensure personnel safety has become paramount. Traditional methods rely on manual intervention or basic sensors, often resulting in delayed or inaccurate alerts. To address these challenges, our system employs advanced image processing and machine learning to enhance safety measures. By analyzing surveillance camera feeds, it detects changes in pixel intensity, smoke patterns, and heat signatures to identify potential fire incidents. Machine learning models, such as convolutional neural networks (CNNs), are trained to accurately classify fire and non-fire regions in video frames. Additionally, the system tracks personnel presence and movement, ensuring accountability within the surveillance area. Modular and scalable, our system integrates seamlessly with existing infrastructure, providing reliable fire detection and personnel monitoring. Experimental results demonstrate its effectiveness, contributing to enhanced safety in public spaces and facilitating prompt emergency responses.

Measurement of aero-engine feature-hierarchy fusion degradation trend based on parameter-adaptive VMD method and improved transformer model

The accumulation of operational time in aero-engines leads to irreversible mechanical wear and tear, necessitating accurate measurement of the health evolution trend for effective predictive maintenance, thus reducing the risk of accidents and ensuring personnel safety. In this paper, a parameter-adaptive variational mode decomposition (VMD) method and improved transformer model are proposed to forecast the degradation trend of aero-engine feature hierarchy fusion. Firstly, in order to quantitatively evaluate the engine health evolution process, the health state aggregate indicator (HSAI) is innovatively constructed by employing the deep blend auto-encoder and self-organizing map network, which facilitate the feature-hierarchy fusion of multi-source sensory data. Secondly, for the significant characteristics with nonlinearity and stochastic fluctuation of the HSAI sequence, the multiscale frequency features are extracted by the parameter-adaptive VMD method with the improved gray wolf optimizer, which analyzes the inherent degradation law. Finally, considering the problem of parameter sharing in the transformer model, a simplified mixture of experts routing algorithm is introduced to implement the switch transformer model to further measure the future aero-engine health trends. Extensive experiments on the multi-source dataset of aero-engine confirm that the proposed method accomplishes the more superior performance for health evolution measurement compared with other available methods.

Cross-physical field prediction method for smoke field distribution in commercial building fire based on distributed optical fiber sensor

In modern commercial building spaces, the smoke generated during indoor fires poses a substantial safety threat to individuals attempting to evacuate. This study leveraged a distributed optical fiber temperature sensing system as the technological foundation. A series of commercial building space models were constructed by employing fire dynamics simulations, and a comprehensive database of simulated fire field information was established. The study built upon this database and proposed a method to establish influence pathways from existing temperature field detection data to three fire risk assessment parameters: carbon dioxide, oxygen, and visibility. The study utilized various machine learning algorithms and focused on selecting and constructing cross-physical field prediction models that optimized the performance of fire risk assessment parameters at the fire key plane. The findings demonstrated that the Multilayer Perceptron (MLP) algorithm model yielded the most accurate fit for carbon dioxide volume fraction, with a Mean Absolute Percentage Error (MAPE) of 3.80% between the model predictions and actual values. The Artificial Neural Network (ANN) algorithm showed superior fitting effects for oxygen volume fraction and visibility, with MAPEs of 1.37% and 3.10%, respectively. Furthermore, the study validated the generalizability of these optimal models by altering spatial scales, revealing robust performance. This study utilized a single optical fiber sensor to achieve dynamic prediction across different physical fields during fire incidents, expanding the application scope of optical fiber sensors in monitoring gas and visibility fields in commercial building fires. This not only enhanced the level of fire monitoring but also effectively reduce equipment costs. It provided effective data support for planning evacuation routes for individuals and rescue arrangements for firefighters, further ensuring personnel safety.

Evaluation of Stability, Inactivation, and Disinfection Effectiveness of Mpox Virus.

Mpox virus (MPXV) infections have increased in many countries since May 2022, increasing demand for diagnostic tests and research on the virus. To ensure personnel safety, appropriate and reliable measures are needed to disinfect and inactivate infectious samples; Methods: We evaluated the stability of infectious MPXV cultures stored at different temperatures and through freeze-thaw cycles. Heat physical treatment (56 °C, 70 °C, 95 °C), chemical treatment (beta-propiolactone (BPL)) and two commercialized disinfectants (Micro-Chem Plus (MCP) and ethanol) were tested against infectious MPXV cultures; Results: The results indicated that MPXV stability increases with lower temperatures. The MPXV titer was stable within three freeze-thaw cycles and only decreased by 1.04 log10 (lg) 50% cell culture infective dose (CCID50) per milliliter (12.44%) after twelve cycles. MPXV could be effectively inactivated at 56 °C for 40 min, 70 °C for 10 min, and 95 °C for 5 min. For BPL inactivation, a 1:1000 volume ratio (BPL:virus) could also effectively inactivate MPXV. A total of 2% or 5% MCP and 75% ethanol treated with MPXV for at least 1 min could reduce >4.25 lg; Conclusions: MPXV shows high stability to temperature and freeze-thaw. Heat and BPL treatments are effective for the inactivation of MPXV, while MCP and ethanol are effective for disinfection, which could help laboratory staff operate the MPXV under safer conditions and improve operational protocols.

ЗМІНИ В СИСТЕМІ УПРАВЛІННЯ НА ПІДПРИЄМСТВАХ ЕНЕРГЕТИЧНОЇ ГАЛУЗІ УКРАЇНИ В УМОВАХ ВІЙНИ

The article examines the changes that are being implemented or should be implemented in the management system of energy enterprises in Ukraine due to the war. The changes in the management system are divided into organizational, economic and innovative ones. The organizational area of changes concerns such aspects as centralization of management, changes in its structure, ensuring personnel safety, communication and cooperation with military and security structures. The centralization of management is the most important, as it facilitates prompt decision-making and reduces approval procedures. A flexible management structure is necessary to adapt the activities of energy companies to wartime conditions. To improve security, communications and cooperation with government agencies, a number of measures are needed: safe work schemes, limited public information, and designation of critical facilities for protection. The shortage of resources dictates the need for economic management changes. Firstly, planning should include an assessment of military risks and measures to respond to them. Secondly, the analysis of the energy market should take into account: the circumstances of demand destruction due to reduced consumption; and the circumstances of demand growth due to increased energy consumption by the defense sector. Third, energy companies should take steps to restructure their growing debt obligations. Fourthly, close coordination with government agencies and an objective assessment of the losses incurred are required to attract grant funding and/or technical assistance. Innovation is a critical area of change to ensure efficient and safe management of energy companies. The author substantiates the need to introduce innovative technologies that can be quickly brought into operation and are capable of producing energy steadily even in the current war conditions. The value of intelligent and/or automated control systems for rapidly eliminating the consequences of war is revealed. Emphasis is placed on the innovativeness of solutions based on renewable energy sources.

Research on the Application of Intelligent Recognition Technology in the Prediction of Violation Behaviour at Electricity Work Sites

Abstract To realize the safe operation of electric power site, this paper proposes an intelligent recognition technology to automatically identify violations. This study successfully constructs a face detection model for power operation sites by combining deep convolutional neural networks and target detection algorithms. A three-way connected feature pyramid structure containing a neuron self-processing module is adopted, and an accuracy test is completed using a Tri-FPN-based target detection network, significantly improving recognition accuracy. In this paper, we also utilized the on-site images collected by video surveillance equipment, combined with CNN algorithm and HOG feature extraction technology to effectively identify the violations and provide early warning of the breaches of the personnel at the power operation site. MAP curves evaluated the detection performance, and the results showed that the head recognition rate was up to 0.9913, and the accuracy rate of all violations exceeded 0.9350.The high accuracy of CNN-based feature fusion extraction algorithm in the recognition of violations of personnel at the site of electric power operation provides effective technical support to ensure personnel safety.

A dynamic resilience evaluation method for cross-domain swarms in confrontation

As a typical Internet of Things system, unmanned swarms can execute missions efficiently, while reduce risks and ensure personnel safety during operations. Because swarms in different physical domains have different advantages, the cross-domain swarms received lots of attentions recently. Since swarm has the ability to resist damage and recover from damage through self-organization and self-reconfiguration, resilience has become an important indicator for swarm design and mission planning in adversarial environments. Existing resilience evaluation methods require a complete degradation-recovery-stabilization process of system performance, which is not proper for the cross-domain swarm under dynamic and uncertain environment. This paper proposes a dynamic resilience evaluation method for cross-domain swarms in confrontation scenario. First, a cross-domain swarm confrontation model is developed and the swarm confrontation performance indicator is proposed. Second, a dynamic resilience evaluation method is proposed for cross-domain swarm. Then, a confrontation strategy selection model is given based on the resilience measurement. A case study of air-ground swarm in confrontations is provided for illustration and analysis. As can be seen from the simulation results, the proposed dynamic resilience method can better capture the dynamic and uncertain nature of cross-domain swarms in confrontation. The proposed strategy selection model can improve the overall resilience of the swarm, and ultimately improve the winning percentage of the cross-domain swarm in confrontation.

Machine learning-based fault diagnosis for three-phase induction motors in ventilation systems

ABSTRACTInduction motors are crucial in industrial drive systems and nuclear and radiological facilities due to their durability and ease of maintenance. Even though the systems are reliable, they have several faults. One of these faults is the instability of the facility’s electrical network. This may lead to catastrophic consequences such as instability in HVAC and ventilation systems, which affects operational safety. Air instability in the HVAC system is a critical concern in the radio pharmaceutical industry. Controlled environments are vital for safety and regulatory compliance. Maintaining air quality and stability is crucial to protect radioactive materials and ensure personnel safety. A healthy and faulty motor condition dataset proposed and used in the study was generated based on a simulated actual supply of electric data. A fault diagnosis is necessary to increase reliability and minimise risks. This paper proposes a simple, reliable, and economical machine-learning-based fault classifier for induction motors. The method analyzes stator currents, motor speed, torque, and three-phase voltage supply to classify some faults resulting from high amplitude and zeros of voltage. A case study of an air handling unit with a 2 hp, 4-pole, 50 Hz three-phase induction motor was modelled and tested with the proposed dataset using various classifiers, including decision trees, logistic regression, discriminant analysis, Naive Bayes, Ensemble, and K-Nearest Neighbor (KNN).

Time-varying structural parameter identification for the offshore gangway

The offshore gangway is a new type of offshore transfer equipment, equivalent to an offshore passage. This device makes it convenient for people to transfer between a vessel and an offshore structure. The control algorithm of the gangway system demands adjusting its control parameters to deal with harsh sea conditions and ensure personnel safety. Thence, it is essential to foreknow its time-varying structural parameters, such as the moment of inertia, the mass, the location of the mass center, and so on. However, due to the highly complex structure, these parameters are difficult to obtain through measurement directly. To adaptively identify these time-varying parameters for better controller design, this paper proposes a novel structural parameter identification approach based on the idea of interval division. During the identification process, the entire period is uniformly divided into several intervals. The time-varying model of the offshore gangway system can be represented in each interval by a quadratic regression equation, the coefficients of which can be derived by the least-squares algorithm. Furthermore, to adapt to the different variation frequencies and amplitudes of the parameters, the divided intervals are of distinct sizes according to the characteristics of each identification parameter. In addition, the particle swarm algorithm is employed to obtain the optimal interval sizes, the cost function of which consists of a series of output error terms. Verified by simulation, the method presented in this paper can effectively estimate the structural parameters of the offshore gangway with high accuracy, with mean square errors reduced by at least 2/3 compared to other methods proposed in previous research.

Control Method of Wire Repairing Robot Based on Semantic Segmentation and Current Detection

The traditional repair of conductors in power systems, using manual work at height, carries a high level of risk. To ensure personnel safety and improve operational efficiency. Based on the development of a conductor repair robot using pre-stranded wires, the focus is on a semantic segmentation-based adaptive conductor arc sag control method, which enables the robot to adapt to conductors with different arc sags by segmentation identification of the conductor, offset calculation, and proportional-integral controller design to realize the robot hanging on the conductor. Subsequently, the winding gap is monitored according to the change of motor current, and a fuzzy controller is used to realize the robot winding operation. The robot prototype tests show that the robot is capable of adapting to the wire pitch and completing soft repair work.

Errors of non-contact temperature measurement

In recent years, there is a trend of improving the performance and efficiency of all existing measuring instruments due to a leap in technology. Almost every industry uses a variety of technologies that apply temperature control. Temperature of a heated body can be estimated by measuring the parameters of its thermal radiation, which are electromagnetic waves of different lengths. Temperature measurement is necessary for comfortable automatic control and management of production processes. The use of non-contact means makes it possible to measure the temperature of, firstly, moving objects, secondly, objects in inaccessible places, thirdly, to avoid damage to the measuring instruments when controlling large temperatures. High speed, the possibility of measuring temperature without disconnecting the object from the technological process, ensuring personnel safety, temperature measurement up to 3000 °C – these are the advantages of non-contact temperature measurement method. To obtain reliable values when measuring thermophysical quantities it is necessary to know the processes occurring in interaction of the measuring device or sensor with the object of measurement. These processes affect the magnitude of the measurement error, that is, magnitude of the result deviation from the true value of the measured parameter. This paper describes the errors of non-contact temperature measurement of pyrometers, namely total radiation pyrometer, partial radiation pyrometer, spectral ratio pyrometer, as well as shows the results of comparative calculations between them. Expressions for the evaluation of methodical errors of total radiation, partial radiation and spectral ratio pyrometers are given, as well as the results of comparative calculations of errors are shown.

Research on Unmanned Surveillance Equipment System in the Context of Urban Anti-terrorism

This article provides a comprehensive analysis and discussion on the demand, development status, existing problems, research strategies, and recommendations for ground unmanned equipment and unmanned reconnaissance equipment in urban environments. The study found that this equipment plays an important role in improving combat effectiveness, ensuring personnel safety, and reducing costs, but there are still some challenges in technology, safety, and law. The article proposes strategies and recommendations to strengthen technological research and innovation, enhance safety management and supervision, establish a sound legal system, reduce production costs, and strengthen talent cultivation and introduction to promote the healthy development of these equipment. It is hoped that this article can provide reference and inspiration for researchers and decision-makers in related fields and promote equipment modernization and national security development.

Beryllium dust generation caused by HHF tests

Beryllium will be used as a plasma-facing material of the ITER reactor components. One such component is the ITER first-wall panel, which contains a layer of beryllium tiles on the plasma-facing surface. Full-Scale Prototypes (FSP) of these panels are undergoing qualification through a series of cyclic High-Heat-Flux (HHF) tests at the HELCZA (High Energy Load Czech Assembly) facility. Such process, directly and indirectly, causes disintegration of the beryllium surface and results in beryllium dust formation. With regards to the beryllium toxicity, it is crucial to monitor the occurrence and the spread of the mentioned dust to ensure personnel safety. Moreover, it is necessary to decontaminate FSPs from the beryllium dust at the end of HHF tests. This study deals with the monitoring of the beryllium dust concentration levels on the FSP after HHF testing at HELCZA and its spread to the vicinity of the testing area, with the aim to provide a basis for safe operation.