Equipment Malfunction Research Articles

Towards Resilient Energy Forecasting: A Robust Optimization Approach

Energy forecasting models deployed in industrial applications face uncertainty w.r.t. data availability, due to network latency, equipment malfunctions or data-integrity attacks. In particular, the case when a subset of features that has been used for model training becomes unavailable when the model is used operationally poses a major challenge to forecasting performance. Ad-hoc solutions, e.g., retraining without the missing features, may work for a small number of features, but they soon become impractical, as the number of models grows exponentially with the number of features. In this work, we present a principled approach to introducing resilience against missing features in energy forecasting applications via robust optimization. Specifically, we formulate a robust regression model that is optimally resilient against missing features at test time, considering both point and probabilistic forecasting. We develop three solution methods for the proposed robust formulation, all leading to Linear Programming problems, with varying degrees of tractability and conservativeness. We provide an extensive empirical validation of the proposed methods in prevalent applications, namely, electricity price, load, wind production, and solar production, forecasting, and we further compare against well-established benchmark models and methods of dealing with missing features, i.e., imputation and retraining. Our results demonstrate that the proposed robust optimization approach outperforms imputation-based models and exhibits similar performance to retraining without the missing features, while also maintaining computational practicality. To the best of our knowledge, this is the first work that introduces resilience against missing features into energy forecasting.

Measuring methane destruction efficiency in gas flares with dual comb spectroscopy

Gas flaring is used as an alternative to venting when waste gases cannot be captured from industrial processes such as oil and natural gas production, chemical processing, and waste management. In the oil and natural gas production sector alone, an estimated 3.5 % of total global natural gas production is flared. Survey studies have shown that the methane destruction efficiency (DE) of flares is lower than expected due to real-world conditions (weather and equipment malfunction) and estimate that improving flare efficiency is a 0.5 Tg/yr methane emissions reduction opportunity. Continuous monitoring of flare DE would provide the opportunity for feedback to lower emissions; however, there are currently no technologies used at scale that can provide such a measurement. Here we present a method for measuring the operational methane DE from flares by monitoring methane levels in flares using dual-frequency comb spectroscopy. This method leverages the temperature-dependent absorption fingerprint of methane to differentiate heated and ambient methane. We assess the capabilities of this technique through a set of laboratory-based experiments utilizing a partially premixed methane flame. We estimate the limit of detection (LOD) and sensitivity of the measurements for directly monitoring a flame, and monitoring a flame from a distance of 1 km in the presence of ambient methane. For our configuration, a targeted monitoring scenario (direct flame measurement) results in the ability to distinguish methane DE up to 99.9 %; whereas in the presence of a 1 km background methane signal, the approach is able to quantify methane DE to 97.5 %. These performance metrics could be improved through an updated high-temperature spectral absorption database for methane, however the current estimated performance could already substantially impact flare emissions by closing the gap between the flare design specifications and what research studies estimate from actual in-field performance.

A Model Based on CNN-LSTM for the Remaining Life Prediction of Equipment in Aircraft Assembly Pulsation Production Line

The aircraft assembly pulsation production line is an advanced and efficient assembly method widely used in aircraft manufacturing. However, equipment malfunctions would occur and can disrupt production takt, affecting the production efficiency. Therefore, accurately predicting the remaining useful life (RUL) of equipment is very crucial. To meet the requirements of both prediction accuracy and efficiency for RUL model used in aircraft pulsation production line, this work proposed a model combining convolutional neural network (CNN) and long short-term memory (LSTM) for RUL prediction. Optimization was performed for the number of neurons in the proposed model with genetic algorithm (GA) to regulate the prediction accuracy and efficiency. Two public datasets representing typical equipment in the pulsation production line were used to validate the proposed model. The results show that the proposed model outperforms the traditional model with substantial improvements in the fitness function of 19.8%, and 30.2% for the two testing datasets. These findings demonstrate the effectiveness of the proposed model in enhancing the accuracy and efficiency of RUL prediction.

Maintenance management strategies for medical equipment in healthcare institutions: a review

A large amount of medical equipment is now extensively utilized in healthcare institutions to assist clinical practitioners in the diagnosis and treatment of diseases. And the applications of such advanced and sophisticated medical equipment have greatly improved the quality of patient care, significantly alleviated the sufferings of patients, and facilitated their rehabilitation. Nevertheless, failures and malfunctions of medical equipment have compromised its reliability and effectiveness as well as jeopardizing the safety of patients and clinical staffs. And a majority of the failures can be attributed to the insufficient and inappropriate maintenance. Therefore, it is imperative to implement effective maintenance management to ensure that medical equipment is in its optimal function, and thereby mitigating the clinical risk resulted by adverse events. The presented review mainly discussed the maintenance strategies of medical equipment including corrective maintenance, preventive maintenance and predictive maintenance. In order to replace the fixed-interval of preventive maintenance, we systematically discussed methods to adjust the maintenance period. Additionally, two strategies to predicting future failures of medical equipment through processing and analyzing the maintenance data obtained from the historical maintenance logs and condition data collected by the embedded sensors are elaborated. Besides, the classification and life cycle of medical equipment are also summarized.

АВИАТЕХПОМ: СОСТОЯНИЕ И ПЕРСПЕКТИВЫ

This paper discusses the results of a project to create an intelligent decision support system for troubleshooting aircraft failures and malfunctions by the Aviation Technical Assistant (AviaTekhPom). The main implemented algorithms, functions, architecture and prototype of the system are described, as well as the further direction of the project development. The Sukhoi Superjet power supply system was chosen as the target object for testing solutions. The current version of the system provides processing of information presented in cards of accounting of malfunctions of aviation equipment and troubleshooting manuals, based on approaches known as rule-based reasoning and case-based reasoning. For preparation and modification of knowledge bases, a tabular formalism of knowledge representation is used, which provides the possibility of using publicly available tabular editors and CSV format. The developed algorithms are implemented in the form of conceptual prototypes of a desktop application and a chatbot.

Comparison of Liquid Sodium Heparin Syringe and Preheparinised Syringe for Sample Rejection in Arterial Blood Gas Analysis: A Cross-sectional Study

Introduction: In hospital settings, Arterial Blood Gas (ABG) analysis is a routine investigation. Many errors can arise in the process, with 70% of errors in ABG analysis occurring during the preanalytical phase. Liquid heparin is commonly used as an anticoagulant in sample collection devices for Blood Gas (BG) analysis; however, inadequate heparin concentration and improper mixing of blood samples after collection often lead to incorrect results. This can be prevented by using syringes preloaded with lyophilised heparin. Aim: To monitor the prevalence of sample rejection collected by the device using liquid sodium heparin and calcium-balanced dried lithium heparin. Materials and Methods: This cross-sectional study was conducted at the ABG laboratory, Department of Biochemistry, Amrita Institute of Medical Sciences and Research Centre, Amrita Viswavidyapeetham University, Kochi, Kerala, India over a 4-month period from December 2022 to March 2023. ABG samples collected using either of the techniques were included, while venous BG samples were excluded. The sample size was 10,957 for each of the two groups- Group A and Group B. Group A included samples collected using liquid sodium heparin, and group B included spray-dried, calcium-balanced lithium heparin from different departments. The incidence of blood clots, air bubbles, and inadequate volume was studied. For all categorical variables, the results were expressed as the frequency and percentage of errors. An unpaired t-test (two-tailed) was used for data analysis. Results: In comparison with liquid sodium heparin collection, errors in the quality of the specimen such as blood clots (1.7% to 1.1%), air bubbles (0.04% to 0%), and inadequate volume (0.2% to 0.03%) were significantly reduced (p-value=0.002) with spray-dried, calcium-balanced lithium heparin (BD Preset™ Safety BG Syringe). Conclusion: The present study found that using calcium- balanced lithium heparin syringes over manually flushed liquid sodium heparin syringes reduces the risk of clots, thus preventing equipment malfunction and maintenance costs. With the introduction of preset syringes, the chance of air bubbles and insufficient volume can also be overcomed.

Диагностика электротехнических компонентов гидротехнического оборудования: эффективность комплексного подхода

Diagnostics of electrical components of hydrotechnical equipment used in agricultural production within the planned maintenance intervals with continuous analysis of residual resource is useful for reducing equipment operation costs. The use of inexpensive universal diagnostic equipment in conjunction with primary analysis of the state of units makes it possible to detect 60 to 80% of pre-failure states. To ensure efficient application of complex approach to primary diagnostics of electrical components of hydraulic equipment used in agricultural production, the authors considered the most common electric motors of pumping units, electrohydraulic distributors and switching equipment. Complex diagnostics included the use of a thermal imager, vibration signal processing systems and current signal control systems. The vibrograms of equipment control points were analyzed, the state of bearing units, cavitation characteristics of pumps were assessed by methods of acoustic and vibration diagnostics, hydraulic equipment leaks were searched, power cables breakdown zones were detected in accordance with methodical instructions for determining the breakdowns of power cables with voltage up to 10 kV RD34.20.516‑90. The article shows the possibilities of applying the diagnostics methodology based on surface thermograms to determine typical defects. Particularly discussed are the parameters of current signals from the electromagnet coils observed in case of distribution equipment malfunction. The authors give comparative evaluation of diagnostics methods with calculated efficiency determination. It is established that the method of combined diagnostics using the analysis of external thermal fields can halve the time of preliminary diagnostics of faults with a stable forecast of the failure time 2‑3 months before the critical state. This reduces the costs for detection of unit failures and simplifies the planning of maintenance procedures.

Development of an Intelligent Oil Field Management System based on Digital Twin and Machine Learning

This article introduces an innovative approach to oil field management using digital twin technology and machine learning. A detailed experimental setup was designed using oil displacement techniques, equipped with sensors, actuators, flow meters, and solenoid valves. The experiments focused on displacing oil using water, polymer, and oil, from which valuable data was gathered. This data was pivotal in crafting a digital twin model of the oil field. Utilizing the digital twin, ML algorithms were trained to predict oil production rates, detect potential equipment malfunctions, and prevent operational issues. Our findings highlight a notable 10-15% improvement in oil production efficiency, underscoring the transformative potential of merging DT and ML in the petroleum industry.

Identifying conditions leading to power quality events in Arctic Norway: Feature selection

Higher penetration of renewable energy and increased electricity consumption makes delivering a reliable power supply a complex task. Power system operators are nevertheless required to provide a reliable power supply with a power quality within specified limits. Power quality events can cause malfunctioning of sensitive equipment for consumers in the distribution network. Communities in the periphery of radial distribution networks, such as rural and island communities, often experience more challenges with power quality than other areas. This study focuses on a small island community in the Norwegian Arctic that exemplifies the challenges of power reliability. It explores an alternative approach to utilise the existing electrical infrastructure. By analysing one year of measurements from an electrical substation near a large industry complex, combined with weather forecast data, machine learning models are employed to recognise conditions leading to low power quality. Although these models show satisfactory performance, some data is missing to fully understand the causes of such events. To address this, feature selection techniques are applied to identify the most significant data features and find new important features. Through this process, a refined dataset is obtained, enabling more accurate detection of power quality events in the Arctic community. This research not only enhances the detection capability but also provides valuable insights into the specific data features that contribute to identifying power quality events, offering opportunities for targeted mitigation strategies and generalisation to other locations.

Optimizing chemotherapy medication leftover management circuit in a centralized chemotherapy preparation unit: A comprehensive FMECA risk analysis and continuous improvement approach.

Chemotherapy medications are usually having high costs, and new targeted drugs can be especially expensive, representing a challenge on healthcare, particularly in low- and middle-income countries. As cytotoxic leftover management is crucial for reducing medication wastage, the aim of this study is to evaluate and optimize leftover management circuit in NIO'S Pharmacy Centralized Chemotherapy Preparation Unit (CCPU) through a Failure Mode, Effects and Criticality Analysis (FMECA), and propose continuous improvement element to enhance the security of the process. The FMECA were conducted in NIO's CCPU from March to May 2023, then continuous improvement plan was established to enhance the security of the process. The failure modes, their causes, impact, and criticality were assessed through criticality index calculation (CI = severity × frequency × detectability), and the risk concerned safety and effectiveness disruptions in chemotherapy preparation circuit using cytotoxic leftover. Leftover management circuits were described in flowchart form, where 18 failure modes were detected in four different steps of the process from chemotherapy preparation to disposal. Failure with highest critical index were detected in the case of equipment malfunction, improper storage temperature, and humidity. Continuous improvement recommendations were proposed in a table form. FMECA analysis applied to NIO's chemotherapy leftover management process allowed us to evaluate, secure, and optimize the circuit, and to propose several actions to implement in a perspective of continuous improvement.

Characteristic study of pyroshock propagation in a structure using a pyroshock simulator

Pyroshock is a large mechanical response with a wide frequency range that occurs instantaneously in a structure owing to the detonation of pyrotechnics or explosives. This response can cause critical damage to the surrounding electronics of aerospace systems. Therefore, designing a structure to prevent damage to electronic equipment from pyroshock is necessary. However, the characteristics of pyroshock change depending on the shape of the structure, boundary conditions, and materials; hence, it must be quantitatively analyzed. In this work, a pyroshock simulator was used for the characteristic study of shock propagation in various structures. A typical aerospace structure is a connected structure that uses rivets or bolts to connect two separate structures. Therefore, a study was conducted using numerical analysis and a shock simulator to characterize the pyroshock propagation in these joint structures. First, numerical analysis and experiments were performed by varying the bolt torque to observe the shock propagation characteristics according to the bolt load. Second, to observe the shock propagation characteristics according to the angle of the joint structure, the responses of 90°, 120°, and 180° joint structures were compared. Third, the characteristics of shock propagation through SUS304 and AL 6061-T6 were compared to observe the change in propagation characteristics due to material change. The results of investigation of the shock propagation characteristics in various structures in this study could be used to mitigate electronic equipment malfunctions due to pyroshock and in designing effective shock-attenuating structures.

Time series clustering to improve one-class classifier performance

The improvement of one-class classifiers’ performance through clustering of multivariate time series is considered in this paper. Datasets arising from real processes come from the available sensors and are affected by many factors, such as aging of the process, changes in the operation region, and equipment malfunction. Despite that, one expects that the classes represented by such diverse data can be unveiled via trained classifiers. This work hypothesizes that the overall performance can be improved by training sets of one-class classifiers with subsets of data clustered by similarity. The proposed method is applied to one class classifiers since they are trained only with the target class, which is clustered based on time series similarity using Dynamic Time Warping and k-means. The advantages of the techniques are illustrated through their application to a public dataset from the oil industry with instances characterizing eight classes of data represented by five time series. Seven classes are selected to train LSTM classifiers using the variables and instances clustered using time series clustering algorithms. The results show that the increase in the similarity of training data tends to improve the performance of the LSTM classifier, achieving an increase of 10% in the overall performance. In a specific case, where the clustering model raised the similarity by 84%, the classification performance improved by 21%.

Novel procedure for measuring the saturation of the pore-pressure system in piezocone tips

Uncertainty in the measurement of the cone penetration test with pore-water pressure measurement (i.e. piezocone testing CPTU) can be caused by an equipment malfunction, poor calibration and maintenance of sensors, but also by a lack of saturation of the piezocone. The standards allow for many different options in terms of filters, saturation fluids and methodologies. The novel device described herein aims at measuring in a very simple manner the degree of saturation of the piezocone prior to testing. Because the quality of pore-water pressure measurements can currently only be assessed a posteriori – that is, after the test has been completed, or at best while testing, the methodology proposed herein has the potential to provide quality benchmarks for CPTU. The tool measures a parameter inspired by the well-established Skempton coefficient B, which is routinely used to estimate the sample saturation prior to laboratory testing. Furthermore, by performing the same measurement after a CPTU test, it is possible to assess whether saturation was lost during cone penetration. The paper describes a set of preliminary results that show a clear correlation between the saturation degree of filters and/or of the overall pore-pressure measurement system and the B parameter proposed, thus providing a proof of concept in laboratory settings.

Troubleshooting in an IVF Laboratory

In the realm of assisted reproductive technology, troubleshooting within an in vitro fertilization (IVF) laboratory is of paramount importance to ensure successful outcomes. FMEA is a proactive methodology used to identify potential failure modes in laboratory processes, assess their possible effects, and prioritize interventions to prevent or mitigate them. In the context of IVF, FMEA aids in recognizing critical steps prone to errors, such as sample mishandling or equipment malfunctions. By quantifying the probability and severity of failures, FMEA allows laboratories to allocate resources judiciously and implement corrective measures that reduce the likelihood of adverse events. Root cause analysis (RCA) is a retrospective technique employed to delve into the underlying causes of anomalies or failures that have already occurred. In the IVF laboratory, RCA helps uncover the root reasons for unsuccessful procedures, like embryo implantation failure. This analysis aids in identifying both immediate triggers and underlying systemic issues, paving the way for targeted improvements to enhance overall success rates. By plotting key performance indicators over time, IVF laboratories can detect deviations from established norms. This enables swift corrective actions before deviations result in compromised outcomes. Control Charts thus offer a dynamic tool for monitoring laboratory processes, maintaining consistency, and swiftly addressing unexpected fluctuations. By systematically preventing potential failures, investigating root causes, and dynamically monitoring processes, IVF laboratories can improve their success rates, enhance patient experiences, and contribute to the advancement of assisted reproductive technologies.

Ascertaining adequacy of fire suppression systems: the perspective of Accra central of Ghana

Purpose Fire is the fundamental element of most people’s lives, and when not controlled, the same fire can lead to several catastrophes in homes, offices, schools, lives and other public places with severe repercussions. Hence, this study aims to examine the adequacy and extent of the application of fire suppression systems in residential and commercial property in Ghana. Design/methodology/approach This study adopts a sequential mixed-mode design comprising quantitative and qualitative research strategies to analyse factors to produce findings. The target population for this study includes shop occupiers, end users of office buildings, and residents in the Accra Central of Ghana. Systematic random sampling was used for the quantitative research, and a sample size of 385 was obtained using a multi-stage and cluster sampling method. A structured survey and semi-structured interviews were used to collect the primary data. The quantitative data were analysed using descriptive and inferential statistics, whereas the qualitative data were analysed using content analysis. Findings From an empirical literature review and the analysis, the three main factors contributing to fire breakouts are equipment malfunction, improper use of heat sources and human mistakes. According to the respondents, fire suppression systems were also inadequate, as most of the suppression systems prescribed in the building code were unavailable. Regarding the ability to manually operate fire suppression systems, most property occupiers stated that they are generally unaware of these suppression systems. Practical implications This study will aid policymakers in developing interventions for fire safety enforcement by ensuring that fire safety regulations are consistently followed by design team members and property developers, resulting in a positive effect on public building structures performing their required functions. It is also critical to provide end users with education and training on how to operate the fire suppression system as well as effective handling of firefighting installations in the event of a fire. Originality/value The findings of this investigation contribute to knowledge and comprehension of the effect of fire suppression systems on building users and may serve as a precursor to the development of a “As Built” certification system for ascertaining the adequacy of fire suppression systems for new and existing residential and commercial property.

Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders.

Predictive maintenance is considered a proactive approach that capitalizes on advanced sensing technologies and data analytics to anticipate potential equipment malfunctions, enabling cost savings and improved operational efficiency. For journal bearings, predictive maintenance assumes critical significance due to the inherent complexity and vital role of these components in mechanical systems. The primary objective of this study is to develop a data-driven methodology for indirectly determining the wear condition by leveraging experimentally collected vibration data. To accomplish this goal, a novel experimental procedure was devised to expedite wear formation on journal bearings. Seventeen bearings were tested and the collected sensor data were employed to evaluate the predictive capabilities of various sensors and mounting configurations. The effects of different downsampling methods and sampling rates on the sensor data were also explored within the framework of feature engineering. The downsampled sensor data were further processed using convolutional autoencoders (CAEs) to extract a latent state vector, which was found to exhibit a strong correlation with the wear state of the bearing. Remarkably, the CAE, trained on unlabeled measurements, demonstrated an impressive performance in wear estimation, achieving an average Pearson coefficient of 91% in four different experimental configurations. In essence, the proposed methodology facilitated an accurate estimation of the wear of the journal bearings, even when working with a limited amount of labeled data.

A Few-Shot Learning Based Fault Diagnosis Model Using Sensors Data from Industrial Machineries

Efficient maintenance in the face of complex and interconnected industrial equipment is crucial for corporate competitiveness. Traditional reactive approaches often prove inadequate, necessitating a shift towards proactive strategies. This study addresses the challenges of data scarcity and timely defect identification by providing practical guidance for selecting optimal solutions for various equipment malfunction scenarios. Utilizing three datasets—Machine Sound to Machine Condition Monitoring and Intelligent Information (MIMII), Case Western Reserve University (CWRU), and Machinery Failure Prevention Technology (MFPT)—the study employs the Short-Time Fourier Transform (STFT) as a preprocessing method to enhance feature extraction. To determine the best preprocessing technique, Gammatone Transformation, and raw data are also considered. The research optimizes performance and training efficiency by adjusting hyperparameters, minimizing overfitting, and using the KERAS Early Halting API within resource constraints. To address data scarcity, which is one of the major obstacles to detecting faults in the industrial environment, Few-shot learning (FSL) is employed. Various architectures, including ConvNeXt Base, Large MobileNetV3, ResNet-18, and ResNet-50, are incorporated within a prototypical network-based few-shot learning model. MobileNet’s lower parameter count, high accuracy, efficiency, and portability make it the ideal choice for this application. By combining few-shot learning, MobileNet architecture, and STFT preprocessing, this study proposes a practical and data-efficient fault diagnosis method. The model demonstrates adaptability across datasets, offering valuable insights for enhancing industrial fault detection and preventive maintenance procedures.

Unsupervised machine learning model for predicting anomalies in subsurface safety valves and application in offshore wells during oil production

Predicting oil well behavior regarding the integrity of its equipment during production and anticipating behavioral changes and anomalies are among the main challenges in oil production. In this context, this study focuses on the development of predictive models for real-time monitoring of well behavior using sensor data from production wells. An unsupervised Novelty and Outlier Detection model has been introduced with a specific focus on predicting instances of unexpected subsurface safety valve closures in subsea wells. This model effectively classifies anomalies observed in these systems by leveraging real-world pressure and temperature data sourced from published literature. The methodology involves the implementation of a floating window for assembling training and test sets. Additionally, a comprehensive investigation is conducted into the impact of hyperparameters and the model’s threshold value (cp threshold). The results highlight the effectiveness of the developed model, observed through the accuracy achieved around 99.9% in predicting spurious closure events of the Downhole Safety Valve. On the same dataset, previous works reported 99.9% accuracy by using long short-term memory (LSTM) autoencoder, 87.1% by using random forest, and 60% with the Decision Tree method. Looking at F1-SCORE values, the developed model performs the best, followed by the LSTM model, both of which are significantly superior to the Decision Tree and random forest models. Furthermore, the model’s applicability is validated through testing in ultradeep water subsea wells within the pre-salt area of the Santos Basin. The significance lies in the potential for this research to enhance anomaly prediction in offshore wells, consequently reducing the costly interventions due to equipment malfunctions. Timely detection and corrective actions, facilitated by the model, can mitigate production loss and safeguard well integrity, addressing critical concerns in the oil and gas industry.

Design of a 20 kWth gasification reactor for harnessing energy from rice husks

Biomass energy is vital in the quest for sustainable and renewable energy sources. Despite the extended development of gasification, equipment malfunctions persist when using biomass that does not meet the expected technical specifications, requiring reactor designs based on fuel properties. Considering the limited information focused on the design of fixed-bed gasifiers for low-density biomass, this study presents the sizing of a gasification equipment to generate 20 kWth whilst taking rice husks as fuel into account. The main reactor dimensions were computed based on experimental tests performed in a low-complexity laboratory-scale gasification facility. This resulted in a throatless reactor with a diameter of 304.8 mm and 1-m length. Since the flame position using a closed-loop control strategy has not been reported for this type of reactor, a novel control methodology based on the signals of three thermocouples and a shaking system is proposed and demonstrated using modelling. This study aimed to develop widespread biomass gasification that derives environmental benefits.

Tailoring a correlation between fracture resistance improvement, elastic moduli, mechanical and nuclear radiation shielding properties for sodium-borate glasses through Gallium(III) oxide incorporation

In the event of unanticipated events or emergency situations, such as equipment malfunctions or accidents, it is crucial for radiation shielding materials to preserve their structural integrity. Enhancing the fracture resistance of glasses has a wide range of benefits that extend to safety, durability, cost savings, energy efficiency, environmental considerations, technological innovation, consumer confidence, manufacturing efficiency, and reduced downtime. This research examines the characteristics of sodium-borate glasses with a nominal composition of 25Na2O-xGa2O3-(75-x)B2O3, (where x: 5, 10, 15, 20, 25, 27.5, and 30 mol%). The aim is to enhance fracture resistant qualities and radiation absorption with the addition of Ga2O3 into the glass composition. In addition to elastic moduli and mechanical properties, gamma-ray and fast neutron removal cross section values are determined for each glass sample. In addition to enhancing mechanical characteristics and elastic moduli, the use of Ga2O3 reinforcement has shown notable improvements in the gamma-ray and fast neutron absorption properties of sodium-borate glass samples. The NGB31.4 sample demonstrated the highest level of improvement in gamma-ray and neutron absorption characteristics. For example, the mass attenuation coefficients were calculated as 1.86645 cm2/g, 1.92189 cm2/g, 1.98875 cm2/g, 2.04052 cm2/g, 2.10506 cm2/g, 2.16271 cm2/g, and 2.17266 cm2/g for NGB5, NGB10, NGB15, NGB20, NGB25, NGB30.5, and NGB31.4 at 15 MeV photon energy, respectively. This enhancement was accomplished by incorporating Ga2O3 into the baseline sample at a mole percentage of 31.4 %. Hence, it can be concluded that the incorporation of Ga2O3 into sodium-borate glasses has the potential to serve as a systematic mechanism, leading to enhancements in mechanical strength and radiation absorption characteristics, thereby making these glasses more suitable for their intended applications. Among these samples, the greatest level of integration observed was 31.4 % mole Ga2O3. The lack of ability to examine the behavioral alterations resulting from higher Ga2O3 content in sodium-borate glasses may be regarded as a limitation of the present study. However, it is very advisable to do more research among the scientific community to thoroughly explore the potential impact of Ga2O3 on sodium-borate glasses.