Engineering Guidance Research Articles

Requirements for the automated loading and unloading of autonomous trucks: an interoperability perspective

PurposeWith the introduction of autonomous trucks, loading and unloading (L/UL) can no longer be performed by the driver and new requirements on automated L/UL are imposed. Compared with many other applications of automation, automated L/UL entails multiple actors, including senders, recipients, and transport providers, and thus several requirements, as explored in this paper.Design/methodology/approachA multiple-case study method is applied consisting of three cases to explore requirements for automated L/UL across four layers of interoperability: organisational, legal, semantic, and technical.FindingsKey requirements identified include organisational adjustments to automate or eliminate drivers’ tasks, legal aspects on load securing and liabilities, semantic alignment for common understanding among the actors, and technical infrastructure needed for automated L/UL.Research limitations/implicationsThis paper emphasises the importance of automated L/UL for fully realising the benefits of autonomous trucks and considering organisational, legal, and semantic aspects beyond technical ones. The study is set in a context of stable transport systems as regards transport network and standardised unit loads.Originality/valueDelving beyond technical aspects, it highlights crucial organisational challenges in automating L/UL and shifts in legal responsibilities among the actors of the supply chain. The paper also provides insights into actual industrial settings of automated L/UL. The development of a conceptual framework for identifying requirements and insights into interoperability provide guidance for engineers, managers, and researchers in designing automated L/UL.

Foundational guidelines for enhancing neurotechnology research and development through end-user involvement.

Neurotechnologies are increasingly becoming integrated with our everyday lives, our bodies and our mental states. As the popularity and impact of neurotechnology grows, so does our responsibility to ensure we understand its particular implications on its end users, as well as broader ethical and societal implications. Enabling end-users and other stakeholders to participate in the development of neurotechnology, even at its earliest stages of conception, will help us better navigate our design around these serious considerations, and deliver more impactful technologies. There are many different terms and frameworks to articulate the concept of involving end users in the technology development lifecycle, for example: 'Public and Patient Involvement and Engagement' (PPIE), 'lived experience', 'co-design', 'co-production'. Here we utilise the PPIE framework to develop clear guidelines for implementing a robust involvement process of current and future end-users in neurotechnology, with emphasis on patient involvement. We present best practice guidance for researchers and engineers who are interested in developing and conducting a PPI strategy for their neurotechnology. We consolidate advice from various online sources to orient individual teams (and their funders) to carve up their own approach to meaningful involvement. After an introduction that coveys the tangible and conceptual benefits of user involvement, we first guide the reader to develop a general strategy towards setting up their own process. We then help the reader map out their relevant stakeholders and provide advice on how to consider user diversity and representation. We also provide advice on how to quantify the outcomes of the engagement, as well as a check-list to ensure transparency and accountability at various stages. The overall aim is the establishment of gold-standard methodologies for ensuring that patient and public insights are at the forefront of our scientific inquiry and product development.

Investigating Stability and Structural Behavior of Steel Pipe Column-Berger Beam Systems

The steel pipe column-Berger beam support system is widely utilized in bridge construction due to its simple structure, convenient installation, high load-bearing capacity, and excellent stability. Stability research on this system is therefore essential. This study employs ANSYS finite element software to conduct a comprehensive analysis, including buckling behavior, node stiffness, and initial defects in the Berger beam support system. The research identifies the first four buckling modes, examines load-displacement curves corresponding to node stiffness values ranging from 0 to 400 kN·m/rad, and evaluates load-displacement curves under varying initial defect ratios. These findings contribute to improving the design and application of the support system. This study not only addresses gaps in current research but also serves as a valuable reference for scholars in related fields. Additionally, it provides practical guidance for engineers involved in bracket design, significantly enhancing their efficiency and effectiveness. In conclusion, the research presented in this paper holds considerable importance for both academic and practical advancements in the field.

Evaluating the impact of rock damage and induced fracture evolution on heat extraction using supercritical carbon dioxide as the working fluid: Semi-analytical model, numerical model, workflow, economic analysis, and field case

Geothermal energy, a clean and renewable source, has garnered significant attention. The excellent physical properties of supercritical carbon dioxide make it an ideal fluid for geothermal development. Fractures act as channels for the efficient seepage and heat transfer of supercritical carbon dioxide. Consequently, the macroscopic fracture damage and evolution induced by the flow of supercritical carbon dioxide impact heat extraction efficiency. Here, pseudo-pressure (multi-stage induced fracture and non-Darcy multi-stage fractured) and temperature well-testing (multi-stage induced fracture thermal and non-Darcy multi-stage fractured temperature) models are developed to monitor the fractures evolution during heat transfer. The finite element method, superposition principle, and Duhamel principle are utilized to establish the proposed models. Numerical models that either consider or disregard rock damage and fracture evolution are developed to simulate and compare the efficiency of heat extraction. The COMSOL Multiphysics 6.1 software is used to solve a heat extraction transient evaluation model. A workflow for utilizing the developed models is proposed, followed by an economic sensitivity analysis. Finally, the models are applied to field cases in the Babbage Oilfield to guide the extraction of geothermal energy. Core displacement experiments are conducted to validate its accuracy, and a differential economic analysis method demonstrates its economic benefits. Results show that the well-testing models invert parameters related to reservoir and fracture evolution. Rock damage and fracture evolution during a heat extraction process are primarily influenced by thermal stress, thermal shock, and pressure-driven effects. As production progresses, fracture permeability and porosity increase, rock elastic modulus decreases, and production temperature drops. Comparing the results with versus without considering rock damage and fracture evolution reveals that their maximum pressure difference is 14% and their maximum temperature difference is 5%. The production pseudo-pressure has the greatest impact on fracture evolution. The injection temperature has the most significant effect on heat extraction efficiency, followed by injection pressure, fracture permeability, and a mass flow rate, with elastic modulus having the least impact. The maximum differential economic benefit for a single horizontal well injection-production system reaches $293,000. Field case studies from the Babbage Oilfield demonstrate the practicality of the developed models and workflow. In summary, the models, workflow, and economic analysis methods proposed in this work provide guidance for engineers in formulating more optimized heat extraction schemes.

A Comparative Review of Dynamic Analysis Techniques for Elevated Water Tanks: Time History Method Versus Response Spectrum Method

This paper presents a comprehensive review of dynamic analysis techniques for elevated water tanks, with a specific focus on comparing the Time History Method (THM) and Response Spectrum Method (RSM). Drawing upon a wide range of literature sources, the review synthesizes existing research efforts dedicated to understanding structural behavior, seismic performance, bracing configurations, material considerations, and soil-structure interaction in the context of elevated water tanks. Despite the extensive body of literature in this field, a notable research gap exists in the systematic comparison of dynamic analysis techniques, particularly between THM and RSM. This paper highlights the need for a comparative review to evaluate the advantages, limitations, and applicability of THM and RSM for dynamic analysis of elevated water tanks, providing valuable guidance for engineers and researchers in selecting the most suitable method based on project requirements and constraints.

The Integration of Thermal Energy Storage Within Metal Hydride Systems: A Comprehensive Review

Hydrogen storage technologies are key enablers for the development of low-emission, sustainable energy supply chains, primarily due to the versatility of hydrogen as a clean energy carrier. Hydrogen can be utilized in both stationary and mobile power applications, and as a low-environmental-impact energy source for various industrial sectors, provided it is produced from renewable resources. However, efficient hydrogen storage remains a significant technical challenge. Conventional storage methods, such as compressed and liquefied hydrogen, suffer from energy losses and limited gravimetric and volumetric energy densities, highlighting the need for innovative storage solutions. One promising approach is hydrogen storage in metal hydrides, which offers advantages such as high storage capacities and flexibility in the temperature and pressure conditions required for hydrogen uptake and release, depending on the chosen material. However, these systems necessitate the careful management of the heat generated and absorbed during hydrogen absorption and desorption processes. Thermal energy storage (TES) systems provide a means to enhance the energy efficiency and cost-effectiveness of metal hydride-based storage by effectively coupling thermal management with hydrogen storage processes. This review introduces metal hydride materials for hydrogen storage, focusing on their thermophysical, thermodynamic, and kinetic properties. Additionally, it explores TES materials, including sensible, latent, and thermochemical energy storage options, with emphasis on those that operate at temperatures compatible with widely studied hydride systems. A detailed analysis of notable metal hydride–TES coupled systems from the literature is provided. Finally, the review assesses potential future developments in the field, offering guidance for researchers and engineers in advancing innovative and efficient hydrogen energy systems.

Impact of cutting parameters on surface roughness in aluminum alloys machining: a review of machine learning models for key parameter identification

This paper provides a comprehensive review of research on the influence of machining parameters—depth of cut, feed rate, and cutting speed—on surface roughness, with a focus on aluminum alloys. Surface quality in CNC machining is significantly affected by these parameters, with numerous studies highlighting their impact on achieving desired surface roughness. The review analyzes findings from ten studies, emphasizing that feed rate is generally identified as the most influential parameter for surface roughness. While feed rate shows a dominant effect, cutting speed and depth of cut also contribute, though to a lesser extent. The research includes a discussion of various methodologies, including ANOVA, the Taguchi method, and more simple machine learning regression models, which demonstrate strong alignment with experimental results and highlight the effectiveness of advanced regression-based models in predicting surface roughness. The study concludes that optimizing feed rate is crucial for achieving high surface quality values, while cutting speed and depth of cut should be managed appropriately. The findings underscore the importance of machine learning tools in analyzing and optimizing machining parameters, offering practical guidance for CNC machine operators and engineers.

Simulation and testing analysis of the vibration characteristics of the panoramic sight platform during the driving of a certain tank

Abstract To study the vibration characteristics of the panoramic sight platform during the driving of a certain tank, the vibration transmission path of the tank was analyzed, modal simulations were performed on the turret, the carrier platform of the panoramic sight platform, the vibration characteristics of the panoramic sight platform during the driving of tank under different working conditions were tested. The results showed that the vibration modes of the turret were located at the location of panoramic sight platform in the third, sixth, and eighth modes, with natural frequencies of 52.439 Hz, 65.012 Hz, and 87.064 Hz, the main response frequency of each structural component of the panoramic sight platform ranged from 46.5 Hz to 52.5 Hz, which was consistent with the main excitation frequency of the turret and the simulated natural frequency. Therefore, resonance was prone to occur in the panoramic sight platform. The simulation and test results have certain engineering guidance significance for the design and overall layout research of tank panoramic sight platform.

Characterizing lower tail components of multivariate non-Gaussian wind loads under typhoon climate: using the Huang-Cui triangular pulse function model

Wind loads at the edge and corner areas of the roof surface of low-rise buildings under extreme winds exhibit intensive non-Gaussianity, especially a high degree of negative skewness. The cumulative effect of negative wind loads below a specific threshold u0 in the time domain is the direct cause of structure destruction under extreme winds. Therefore, this study proposes a triangular pulse function model to quantitatively characterize lower tail components of wind loads with negative skewness via equal cumulative integral on time. In this model, the two critical factors are the intensity for the absolute value of the threshold u0 and the minimum continuous duration T0. Additionally, this article investigates the dependence structures of the lower tail components from multivariate wind loads, and their values vary from 0.65 to 0.80 at quantiles (0.25,0.25). In characterizing the peak locations, a position function combining Poisson pulses and periodic pulses is established, and the period T for generating periodic pulses is a constant. Through analysis, the median value of storm lengths varies from 7.0 to 8.5, and 95% are in the interval (5.0, 15.0). Finally, an example is given to illustrate the effectiveness of the continuous integral by the proposed model. For wind loads measured from different locations, the dependence of pulse locations, lower tail storm length, and intensity are investigated comprehensively. In the end, a physical perspective to understand the generation mechanism of lower tail storms is provided via the fractal dimension. The proposed model and analyzed results are of significant engineering guidance value in time-domain numerical simulation of multi-point synchronous non-Gaussian wind loads to improve simulation accuracy.

A comprehensive analysis of the tail-dependent structure of wind loads and its engineering applications

Wind load field characteristics on the roof surface of low-rise buildings based on field measurements have crucial engineering guidance value. In the background, tail-dependence structure from measured wind loads can assist in modeling the joint tail distribution of multivariate wind loads. In this study, the non-parameter estimation method, multi-copula function model and multivariate peak over threshold model are applied to estimate its precise value. The upper tail dependence coefficient's value is around 0.20 when the quantile for p(y) = 0.75. When p(y) = 0.95, the Gumbel copula overestimates the tail-dependence value slightly. Additionally, the lower tail-dependence values estimated by the Clayton copula are around 0.19 at 0.25 quantile. For extreme tail-dependence coefficients, little difference exists in the estimated results by the non-parameter estimation method and the peak over threshold model, in which its value varies from 0.80 to 0.90. Under roof pitch = 5°, its value exhibits some dispersion at different locations. An area weight function is applied to estimate the extreme tail-dependence value between critical regions. Then, the expectation function of tail dependence reaches the peak value in p(x) = 0.50, p(y) = 0.50 and it has different gradients for different variables and tail regions. Additionally, the lower tail dependence structure is significantly stronger than the upper tail dependence for wind loads with negative skewness. For application in wind engineering, potential applications in risk analysis for roof structures, extreme value estimation for local region block wind load, and wind load forecast for unmonitored locations are proposed.

Structural Assessment Material and Fire Induced Strength Degradation Material of Multistory Building

This paper presents a detailed case study of a multistory building near Bombay Motors in Jodhpur that suffered severe fire damage, with temperatures reaching up to 900°C. The fire brigade arrived 30 minutes after the incident. Under the guidance of MBM University, research scholars conducted site investigations to analyze the impact of the fire on the building's materials, particularly concrete and reinforcement. The study reveals how the fire weakened these materials, leading to a significant reduction in the building's strength, especially in the columns. Notably, Floor 4 retained more strength than Floor 3, with strength differences ranging from +26.67% in one column to -20% in another. This highlights the variation in damage, indicating that while some columns on Floor 4 remained stronger, others experienced considerable strength loss. The findings emphasize the vulnerabilities exposed by fire and suggest ways to enhance building resilience. The research advocates for updates to building codes to incorporate better fire safety measures and provides valuable insights for improving firefighting and evacuation plans during emergencies. While the study is focused on this particular building, its results can also inform urban planning and fire risk management in other high-risk areas. By identifying weaknesses in design and materials, the study offers guidance for architects, engineers, and policymakers. Overall, the case study contributes to the goal of creating safer, stronger buildings in similar environments, promoting better construction practices and fostering safer cities. Additionally, the evaluation of residual strength was conducted using ISO 834 and Euro Code standards, and simulations performed through Abaqus software yielded results closely aligned with experimental techniques.

CFD analysis of the impact of corner adjustments in tall square buildings for wind load mitigation

Abstract Tall buildings face significant challenges due to wind loads. Therefore, it is crucial to accurately assess wind loads in the design of these structures. One effective strategy for tackling this issue is through aerodynamic modifications. This research paper explores the effectiveness of aerodynamic modifications in mitigating wind loads on tall square-shaped buildings through computational fluid dynamics (CFD) simulations using ANSYS Fluent software. Four corner modification strategies—chamfering, rounding, recessing, and double recessing—are applied to a square model with corner reductions of 5%, 10%, 15%, and 20%. Analysis of force and pressure coefficients for along-wind load, accompanied by the lift coefficient for across-wind load, reveals crucial insights. Chamfering proves optimal at a 5% corner reduction, while rounded and recessed corners excel at a 10% reduction. At a 15% reduction, rounded and chamfered corners demonstrate the highest effectiveness. Notably, rounded corners exhibit exceptional efficacy at a 20% reduction. These findings highlight the potential of corner adjustments as efficient strategies for substantial wind load mitigation on square-plan tall buildings. This research provides essential guidance for architects and engineers in optimizing the design of such structures, contributing to improved resilience against wind-induced forces.

Numerical solutions and stability analysis of hybrid Casson nanofluid flow with MHD and heat transfer effects

AbstractThis research addresses the complex dynamics of hybrid Casson nanoliquids in flow and heat transfer applications, focusing on the interaction between fluid dynamics and thermal phenomena in the presence of magnetohydrodynamics (MHD), viscous dissipation, and Joule heating. The motivation behind this study stems from the need to enhance the efficiency of heat transfer processes in various engineering applications, such as cooling systems and electronic devices, where hybrid nanofluids can offer superior thermal performance compared to conventional fluids. The novelty of this work lies in its comprehensive numerical investigation of a hybrid Casson nanoliquid over a moving permeable surface, incorporating a detailed analysis of MHD effects, viscous dissipation, and Joule heating. Using the Tiwari and Das model to formulate the governing equations, the study employs similarity transformations to convert these equations into a system of ordinary differential equations (ODEs). MATLAB is then used to derive numerical solutions, with a stability analysis ensuring the physical dependability of these solutions. Key findings reveal that the temperature distribution of the hybrid nanoliquid shows a positive correlation with both Prandtl and Hartmann numbers. Additionally, a positive relationship between temperature and the Eckert number is observed. These insights offer valuable guidance for engineers aiming to optimize heat transfer processes using hybrid nanofluids, highlighting their potential for improved thermal management in practical applications.

Insights into local wall erosion characteristics and prevention measures for cyclone separators

Cyclone separators are separation devices that use the principle of inertia to remove particulate matter from flue gases. The present study mainly focuses on wall erosion in cyclone separators and associated research. The main locations of erosion in gas–solid cyclone separators, including the entrance impact section, cyclone roof corner, vortex finder outer surface, spiral-type erosion strip, and lower cone section, are examined in detail. The main factors influencing wall erosion are discussed, including inlet flow velocity, solid particle properties and loading, geometrical structure, and manufacturing quality. Finally, several practically preventive measures against wall erosion are presented, including adjustment of operating conditions, the use of erosion-resistant materials, optimization of geometrical structures, and the addition of auxiliary devices, all of which are essential for ensuring operational efficiency, equipment reliability, safety, and environmental protection in various industrial applications. This paper aims to provide a basis for further research into erosion in cyclone separators as well as guidance for engineers involved in their industrial applications.

Cloud-Native ETL: Integrating Databricks and Azure Data Factory for Scalable Data Processing in Enterprise Environments

This article examines the implementation of cloud-native ETL solutions leveraging Databricks and Azure Data Factory (ADF) for scalable data processing in enterprise environments. The article presents a comprehensive analysis of the architectural design, integration strategies, and performance optimization techniques for combining Databricks' powerful data transformation capabilities with ADF's robust workflow orchestration. Through a series of case studies and empirical evaluations, we demonstrate how this integrated approach addresses the challenges of big data processing, including scalability, flexibility, and cost-effectiveness. Our findings reveal significant improvements in processing efficiency and resource utilization, with observed reductions in ETL pipeline execution times by up to 40% and overall cloud infrastructure costs by 25%. The article also highlights best practices for data governance, security, and quality management within this framework. These insights provide valuable guidance for data engineers and IT professionals seeking to modernize their data processing infrastructure and harness the full potential of cloud-native ETL solutions.

Comprehensive Analysis of Major Fault-to-Failure Mechanisms in Harmonic Drives

The present paper proposes a detailed Failure Mode, Effects, and Criticality Analysis (FMECA) on harmonic drives, focusing on their integration within the UR5 cobot. While harmonic drives are crucial for precision and efficiency in robotic manipulators, they are also prone to several failure modes that may affect the overall reliability of a system. This work provides a comprehensive analysis intended as a benchmark for advancements in predictive maintenance and condition-based monitoring. The results not only offer insights into improving the operational lifespan of harmonic drives, but also provide guidance for engineers working with similar systems across various robotic platforms. Robotic systems have advanced significantly; however, maintaining their reliability is essential, especially in industrial applications where even minor faults can lead to costly downtimes. This article examines the impact of harmonic drive degradation on industrial robots, with a focus on collaborative robotic arms. Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) approaches are discussed, highlighting how digital twins and data-driven models can enhance fault detection. A case study using the UR5 collaborative robot illustrates the importance of fault diagnosis in harmonic drives. The analysis of fault-to-failure mechanisms, including wear, pitting, and crack propagation, shows how early detection strategies, such as vibration analysis and proactive maintenance approaches, can improve system reliability. The findings offer insights into failure mode identification, criticality analysis, and recommendations for improving fault tolerance in robotic systems.

A Numerical Simulation of the Effect of Drilling Fluid Rheology on Cutting Migration in Horizontal Wells at Different Drilling Fluid Temperatures

In recent years, significant breakthroughs have been made in the exploration of deep to ultra-deep oil and gas reserves onshore in China. These conventional deep to ultra-deep reservoirs are typically buried at depths exceeding 4500 m, with bottom-hole temperatures surpassing 150 °C. The high temperatures at the bottom of the well are more likely to cause deterioration in drilling fluid properties, altering its rheological properties and reducing cutting transport efficiency, which can lead to wellbore cleaning issues. In this paper, the numerical simulation method is used to analyze the influence of cutting particle size, drilling fluid flow rate, drill pipe rotation speed, and drill pipe eccentricity on the annular cutting concentration under different wellbore drilling fluid temperature conditions. The results show that at the same cutting particle size, as the drilling fluid temperature increases, the cutting concentration in the annulus increases sharply. The increase is the largest when the particle size is 3 mm, and when the drilling fluid temperature is 220 °C, the cutting concentration increases by 79.7% compared to at 200 °C and by 279% compared to at 180 °C. When the flow rate increases from 0.5 m/s to 1.0 m/s, the annular cutting concentration at drilling fluid temperatures of 220 °C and 200 °C decreases by 70.5% and 50.4%, respectively. The higher the drilling fluid temperature, the better the cutting removal effect when increasing the drill pipe rotation speed. However, when the rotation speed exceeds 120 rpm, the change in cutting concentration with increasing rotation speed becomes insignificant. When the drill pipe eccentricity is small, an increase in drilling fluid temperature leads to a significant rise in annular cutting concentration. However, when the drill pipe eccentricity is large, changes in drilling fluid temperature have a smaller impact on cutting concentration. The research findings can provide engineering guidance and theoretical support for the design of drilling fluid hydraulic parameters for cutting transport and rheological parameters in high-temperature wellbores.

Research on Integrated Optimization of Design Parameters and Process Simulation for Throughput Efficiency

This thesis presents a comprehensive approach to enhance the production throughput of aluminium bumper beams, a critical component in automotive manufacturing. Using CATIA, an innovative bumper beam design was developed, focusing on simplicity of design and manufacturability. The design parameters considered included material selection, cross-sectional area optimization, and welding techniques to ensure both performance and ease of manufacturing. Transitioning to Tecnomatix Plant Simulation, a virtual environment was constructed to simulate the bumper beam production process. The simulation incorporated the identified design parameters, process routes, layout considerations, and machine arrangements to optimize throughput without inflating costs. By leveraging advanced simulation techniques, the study aimed to identify the most efficient production strategies while maintaining design integrity and minimizing resource consumption. Through systematic scenarios experimentation and analysis, the research yielded insights into the complex relationship between design decisions and manufacturing processes. The results highlight the potential for significant throughput enhancements achievable through collaborative optimization of design and production parameters. The optimization was able to achieve an increase in throughput of about 12.64% of its initial throughput. Moreover, the study underscores the importance of holistic approaches integrating design engineering with manufacturing simulation to drive continuous improvement in industrial operations. This thesis contributes to advancing automotive manufacturing practices by offering a methodology for enhancing production efficiency while preserving product quality and cost-effectiveness. The findings provide valuable guidance for engineers and practitioners seeking to optimize manufacturing processes through the use of simulation and design parameters

Thermal Performance Analysis of Electronic Components on Different Substrate Materials

This study numerically analyzed different substrate board materials, including FR4, silicon, and copper, for electronic component cooling. Ten diverse components were mounted on these boards and subjected to uneven heat distribution. Both natural and forced air cooling were tested at various speeds. Copper cladding significantly lowered component temperatures by 34-54 degrees Celsius compared to FR4 or silicon at 7 m/s. Moreover, copper allowed for lower fan speeds (5 m/s) while keeping component temperatures below 100 degrees Celsius, reducing energy consumption. These results offer valuable guidance for thermal engineers in selecting optimal substrate boards for efficient electronic cooling.

Intelligent fault diagnosis for tribo-mechanical systems by machine learning: Multi-feature extraction and ensemble voting methods

Timely fault detection is crucial for preventing issues like worn clutch plates and excessive friction material degradation, enhancing fuel efficiency, and prolonging clutch lifespan. This study focuses on early fault diagnosis in dry friction clutch systems using machine learning (ML) techniques. Vibration data is analyzed under different load and fault conditions, extracting statistical, histogram, and auto-regressive moving average (ARMA) features. Feature selection employs the J48 decision tree algorithm, evaluated with eight ML classifiers: support vector machines (SVM), k-nearest neighbor (kNN), linear model tree (LMT), random forest (RF), multilayer perceptron (MLP), logistic regression (LR), J48, and Naive Bayes. The evaluation revealed that individual classifiers achieved the highest testing accuracies with statistical feature selection as 83% for both MLP and LR at no load, 90% for MLP at 5 kg, and 93% for KNN at 10 kg. For histogram feature selection, KNN and MLP both reached 85% at no load, MLP achieved 91% at 5 kg, and RF attained 97% at 10 kg. With ARMA feature selection, KNN reached 93% at no load, LR achieved 94% at 5 kg, and RF reached 86% at 10 kg. The voting strategy notably improved these results, with the RF-KNN-J48 ensemble reaching 98% for histogram features at 10 kg, the KNN-LMT-RF ensemble achieving 94% for ARMA features at no load, and the SVM-MLP-LMT ensemble attaining 95% for ARMA features at 5 kg. Hence, a combination of three classifiers using the majority voting rule consistently outperforms standalone classifiers, striking a balance between diversity and complexity, facilitating robust decision-making. In practical applications, selecting the optimal combination of feature selection method and classifier is vital for accurate fault classification. This study provides valuable guidance for engineers and practitioners implementing robust load classification systems in industrial settings.