Maintenance Operations Research Articles

Optimizing silt density index prediction in water treatment systems using pressure-based gradient boosting hybridized with Salp Swarm Algorithm

This study introduces a novel approach for predicting Silt Density Index (SDI) values in desalination systems by combining Gradient Boosting with the Salp Swarm Algorithm (GBSSA). The integration of GBSSA enhances predictive accuracy over a one-month period, addressing critical challenges related to water scarcity exacerbated by climate change. Data were collected from two desalination plants in western Algeria, specifically the Benisaf and El Mactaa plants, to train and validate the models. Pressure measurements were chosen as input features due to their ease of measurement and the use of simple, cost-effective equipment, minimizing the need for complex sensors and reducing manual intervention. The study compared the performance of three Gradient Boosting variants: the standard Gradient Boosting model (GB), Gradient Boosting optimized with Particle Swarm Optimization (GBPSO), and the proposed GBSSA model. Results demonstrated that the GBSSA model significantly outperformed both GB and GBPSO, achieving superior accuracy in predicting SDI values. This enhanced predictive capability is crucial for effective monitoring and management of fouling in desalination plants, facilitating better operational adjustments and maintenance decisions. The findings underscore the potential of combining advanced machine learning techniques with optimization algorithms to improve predictive modeling in critical environmental contexts.

The influence of various layouts of internal baffles on air distribution in heat pump cabinet dryers

Cabinet dryers are commonly employed in the food industry due to their simplistic design and low operational maintenance. Nevertheless, uneven internal airflow remains a critical issue affecting efficiency. This study developed nine models of heat pump cabinet dryers, based on traditional designs, and conducted a numerical investigation using Computational Fluid Dynamics (CFD) modeling. Modifications were made to the positions of the air inlet, outlet, and the baffle angle to determine the configuration that achieves the most uniform drying airflow, aiming to improve overall dryer performance. Key factors such as airflow velocity, temperature, humidity, and heat and mass transfer were analyzed. The results demonstrated that the optimized configuration, featuring a top inlet, middle outlet, and 4° baffle angle, improved flow and temperature uniformity by 52.70 % and 85.32 %, respectively, compared to the original design. The effectiveness of this configuration was validated through experimental trials involving the loading and unloading of figs. Physical property assessments indicated satisfactory drying uniformity, while sensory evaluations showed the final product was crisp, easy to chew, and exhibited enhanced sweetness. Overall, this study highlights the effectiveness of structural modifications in improving airflow and temperature uniformity, thereby enhancing the overall drying performance.

2D MOF‐Based Filtration‐Sensing Strategy for Trace Gas Sensing Under Intense F‐Gas Interference at Room Temperature

AbstractThe detection of trace impurity gases in fluorinated gas (F‐gas) that are widely used in the industry offers a significant avenue for equipment status monitoring and mitigating unnecessary emissions. However, the formidable electron affinity (EA) and adsorption propensity of F‐gas molecules render the identification of trace impurities within a high‐concentration F‐gas atmosphere exceptionally challenging. Herein, the filtration‐sensing strategy is proposed to realize highly sensitive and selective Room Temperature (RT) sensing of trace gases in the F‐gas environment. Through the innovative construction of a bilayer structure, comprising Co3(HITP)2 as the overlayer and SnO2 nanofibers (NFs) as the sensing layer, remarkably sensitive detection of trace impurity gases under intense F‐gas interference conditions is achieved. The efficacy of the Co3(HITP)2 overlayer is further corroborated through the incorporation of Pd‐SnO2 and MoS2‐SnO2 sensors, concurrently facilitating targeted quantitative identification within a complex gas mixture environment. The underlying sensing mechanism is predominantly attributed to interatomic adsorption interactions and the modulation of gas diffusion by microporous structures. This work provides pioneering insights into trace impurity detection within high‐concentration F‐gas atmosphere while presenting a potentially viable solution for the operational maintenance of F‐gas‐based industrial equipment (F‐equipment) in industrial applications.

Short-term effects of a research-scale oyster cage aquaculture system on sediment transport, water quality, and seagrass meadow health in Copano Bay, TX, USA

Competing uses for nearshore coastal space, such as aquaculture, have complex environmental and ecological interactions with surrounding seagrass meadows. Oyster aquaculture is among the fastest-growing aquaculture segments in the United States, and it brings concerns such as increased sedimentation from farm maintenance operations and altered water quality through oyster filtration. Changes in seagrass coverage and growth are common indicators of ecological health used to determine anthropogenic impacts on nearshore environments. This study characterized the effects of a research-scale adjustable long-line oyster aquaculture system on the health of adjacent seagrass meadows in Copano Bay, TX. Four Halodule wrightii meadows were identified at various distances from the research site: 100 m upstream (upstream), directly adjacent (0 m), 30 m downstream (30 m), and 60 m downstream (60 m). Sites were monitored for 1) seagrass health, 2) water quality parameters, and 3) sediment deposition. Over the 18-week sampling period, no significant differences (p>0.05) were found for water quality variables or sediment grain-size analysis using a one-way ANOVA. A linear mixed-effects model was used for repeated measures of seagrass data, with no effect of the site found on mean seagrass length, coverage, or maximum length (p>0.05). These results suggest that seagrass health indicators were unaffected by proximity to the oyster system. It was concluded that an oyster research aquaculture system of the type and size at the project location had no major negative or positive impact on seagrass meadow health due to high background variability and the overall minor footprint of the oyster farm across a comparatively large spatial extent.

WEAR OF FUNCTIONAL PAIRS OF A GASOLINE ENGINE COMBUSTION CHAMBER

Gasoline combustion engine is generally the most important part of a car. With this driving unit the car achieves the best ratio between its fuel utilization and weight. In order to comply with required parameters, such as performance, ecology and economy of combustion engines, their regular service inspections and associated maintenance operations must be followed. For testing the operability of these engines, especially for testing the wear of functional pairs of the combustion chamber, we use various methods. The paper deals with detection of wear of combustion engine functional pairs and with subsequent comparison of obtained values with the values declared by the manufacturer. The measurements were carried out on a Suzuki gasoline engine G13BA with a cubature of 1298 cm3 and a total mileage of 160,000 km.

Monitoring System Design Using GPS Tracker and Operational Vehicle Maintenance Case Study of PT. Pertamina (Persero)

The Utilization of GPS and GSM technology in operational vehicle monitoring offers convenience and efficiency in management. This research designs a GPS-based vehicle monitoring system that is integrated with vehicle maintenance management to assist companies in making decisions related to fuel efficiency, predictive maintenance scheduling, and safety aspects. By monitoring vehicle position and condition in real-time, companies can optimize travel routes, reduce travel time and reduce operational costs. The system also includes tire pressure and fuel percentage monitoring, which serve as important indicators in an accurate and sustainable vehicle maintenance strategy. This research uses quantitative methods to analyze various aspects in designing a GPS-based monitoring system that is integrated with operational vehicle maintenance management. With the resulting data, managers are able to identify trends that support predictive maintenance as well as preventive steps to reduce the risk of sudden damage. The research results show that the integration of GPS and GSM provides significant benefits in improving performance and reliability for vehicles, especially in the distribution and logistics sectors. The implementation of this system is expected to support the company in maintaining optimal vehicle performance and contribute to operational sustainability.

Application of Traffic Cone Target Detection Algorithm Based on Improved YOLOv5

To improve the automation level of highway maintenance operations, the lightweight YOLOv5-Lite-s neural network was deployed in embedded devices to assist an automatic traffic cone retractor in completing recognition and positioning operations. The system used the lightweight shuffle Net network as a backbone for feature extraction, replaced convolutional layers with focus modules to reduce computational complexity, and reduced the use of the C3 layer to increase network speed, thereby meeting the speed and accuracy requirements of traffic cone placement and retraction operations while maintaining acceptable model inference accuracy. The experimental results show that the network could maintain recognition accuracy and speed values of around 89% and 9 fps under different working conditions such as varying distances, lighting conditions, and occlusions, meeting the technical requirements for deploying and retrieving cones at a speed of 30 cones per minute when the operating vehicle’s speed was 20 km/h. The automatic traffic cone placement and retraction system operated accurately and stably, achieving the application of machine vision in traffic cone retraction operations.

Analysis of the features of wheel out-of-roundness and construction of the wheel roughness spectrum of high-speed trains

ABSTRACT Wheel out-of-roundness (OOR), especially wheel polygonal wear, occurs frequently in high-speed wheels in China and has a significant influence on vehicle operation and wheel maintenance. The authors’ team has measured wheel OOR for more than 30,000 wheels with high-precision measuring instruments on nine types of high-speed EMUs over the past decade. In this study, the features of the wheel OOR are analyzed in detail and the wheel roughness spectrum is determined in both the narrow band and the 1/3 octave band. A method for inverting the wheel polygonal with the sine and catenary type is proposed. The analysis of the wheel OOR features, the establishment of the wheel roughness spectrum, and the resampling of wheel out-of-roundness profile from a roughness spectrum are of great importance for the simulation analysis of the dynamic wheel – rail interaction and rolling noise, as well as for the design of vehicle and track components.

Structural Health Monitoring and Failure Analysis of Large-Scale Hydro-Steel Structures, Based on Multi-Sensor Information Fusion

Large-scale hydro-steel structures (LS-HSSs) are vital to hydraulic engineering, supporting critical functions such as water resource management, flood control, power generation, and navigation. However, due to prolonged exposure to severe environmental conditions and complex operational loads, these structures progressively degrade, posing increased risks over time. The absence of effective structural health monitoring (SHM) systems exacerbates these risks, as undetected damage and wear can compromise safety. This paper presents an advanced SHM framework designed to enhance the real-time monitoring and safety evaluation of LS-HSSs. The framework integrates the finite element method (FEM), multi-sensor data fusion, and Internet of Things (IoT) technologies into a closed-loop system for real-time perception, analysis, decision-making, and optimization. The system was deployed and validated at the Luhun Reservoir spillway, where it demonstrated stable and reliable performance for real-time anomaly detection and decision-making. Monitoring results over time were consistent, with stress values remaining below allowable thresholds and meeting safety standards. Specifically, stress monitoring during radial gate operations (with a current water level of 1.4 m) indicated that the dynamic stress values induced by flow vibrations at various points increased by approximately 2 MPa, with no significant impact loads. Moreover, the vibration amplitude during gate operation was below 0.03 mm, confirming the absence of critical structural damage and deformation. These results underscore the SHM system’s capacity to enhance operational safety and maintenance efficiency, highlighting its potential for broader application across water conservancy infrastructure.

A resilience-driven emergency maintenance operation scheme optimization method based on risk

System failure caused by unexpected disruptions is unavoidable and can lead to catastrophic consequences. To complete the emergency maintenance operation safely and efficiently, a well-formulated emergency maintenance scheme is crucial. A resilience-driven optimization methodology for the emergency maintenance operation scheme with considering maintenance operational risk is proposed. The maintenance operational risk is assessed by combining Job Safety Analysis, Bayesian networks and the matter element theory. A graphical evaluation and review technique (GERT) model is established based on the informed maintenance operational risk to evaluate the emergency maintenance scheme. According to derived results of the GERT model, the expected time and the expected risk are preliminarily optimized. A re-optimization model focusing on the system resilience is established to optimize the suitable maintenance operation time for each equipment and the reasonable equipment maintenance sequence for the system. The emergency maintenance scheme optimization for the subsea production system is utilized to demonstrate the proposed method.

A remote monitoring method for gateway electric energy meters based on electric energy, voltage and current conservation decomposition

Abstract A remote monitoring technique for gateway electricity meters is introduced that leverages conservation principles of electric energy, voltage, and current. It aims to tackle issues related to errors in electric energy meters and transformers that are challenging to distinguish, as well as the high costs associated with on-site testing. The ridge regression is used to solve the formulas for bus power and current conservation to obtain gateway metering points’ error and current loops’ error. The voltage loop error is determined through an averaging method, which is then used to calculate the error in electric energy meters. The voltage consistency method is used to synchronize the time bias between gateway electricity meters, ensuring accurate calculations. The analysis results indicate that the proposed method effectively monitors the operating status of gateway electric energy meters and offers greater accuracy compared to benchmarks. This method can timely discover suspected inaccurate electricity meters, aiding in their maintenance and efficient operation.

DIAGNOSIS OF ROLLING BEARING DEFECTS BASED ON WAVELET ANALYSIS

Purpose. Development and improvement of a method for analyzing vibration signals from rolling bearings based on wavelet analysis for the detection and identification of equipment defects. Research methods. Wavelet analysis was employed for processing vibration signals from rolling bearings. Threshold wavelet filtering was applied to highlight weak impulse components in the signals, and Morlet wavelet was used to ensure effective filtration. Results. The research results indicate that the proposed method, utilizing wavelet filtering, enhances the speed and reliability of vibration diagnostics for bearings. This allows for the efficient extraction of characteristic frequencies associated with various types of rolling bearing defects. In comparison with other signal analysis methods, the use of the developed method based on continuous wavelet analysis has proven to be particularly effective in extracting characteristic diagnostic frequencies. This method not only allows for the identification of specific types of defects in rolling bearings but also ensures universality, enabling its successful application for analyzing other types of non-stationary signals. Experimental studies have confirmed the high efficiency of the developed method, especially in the early stages of defect development. The application of this method is evident not only in its ability to effectively highlight the characteristic frequencies of bearings but also in its capacity to conduct signal analysis for the identification of equipment defects as a whole. This makes the proposed method a promising and versatile tool for the diagnosis and monitoring of the condition of technical systems. Scientific novelty. Application of the proposed method for processing vibration signals from rolling bearings based on wavelet analysis to improve the effectiveness of defect detection and identification in equipment. Practical value. The developed method can be applied in the industrial sector for the analysis and diagnostics of rolling bearings in equipment. It enables the timely detection of defects, reduces the risk of equipment failure, and lowers operational maintenance costs. Thus, this method has practical value in enhancing the reliability and productivity of industrial equipment.

Development of a Hybrid AI Model for Fault Prediction in Rod Pumping System for Petroleum Well Production

Rod pumping systems are widely used in oil wells. Accurate fault prediction could reduce equipment fault rate and has practical significance in improving oilfield production efficiency. This paper analyzed the production journal of rod pumping wells in block X of Xinjiang Oilfield. According to the production journal, oil well maintenance operations are primarily caused by five types of faults: scale, wax, corrosion, fatigue, and wear. These faults make up approximately 90% of all faults. 1354 oil wells in the block that experienced workover operations as a result of the aforementioned factors were chosen as the research objects for this paper. To lower the percentage of data noise, wavelet threshold denoising and variational mode decomposition were used. Based on the bidirectional long short-term memory network, an intelligent model for fault prediction was built. It was trained and verified with the help of the sparrow search algorithm. Its efficacy was demonstrated by testing various deep learning models in the same setting and with identical parameters. The results show that the prediction accuracy of the model is the highest compared with other 11 models, reaching 98.61%. It is suggested that the model using artificial intelligence can provide an accurate fault warning for the oilfield and offer guidance for the maintenance of the rod pumping system, which is meant to reduce the occurrence of production stagnation and resource waste.

Effect of Progressive Wetting on Permanent Deformation of Fouled Ballast under Cyclic Loading

Climate change in recent decades has increased the frequency and intensity of extreme rainfall events, causing varied moisture contents in the ballasted track, which greatly challenges railway operation and track maintenance. Currently, most research about permanent deformation of fouled ballast are under dry condition or with a moisture content in the individual tested sample, which could not fully represent varied moisture conditions in the field induced by varying rainfall intensities. In this paper, permanent deformation of field-sourced fouled ballast under progressive wetting condition was investigated using large-scale triaxial cyclic tests. The results indicate that, with progressively rising water contents (0% to 12%), the fouled ballast sample maintained their stability; however, the rate of permanent strain increases to a peak value before experiencing a slight decline, aligning with classified shakedown ranges. This observed phenomenon can be attributed to the interplay between wetting, densification, and interlocking under conditions of cyclic loading and progressive wetting, an aspect that existing research have not elaborated. To encapsulate the identified deformation characteristics, this study proposes and validates a new predictive model for the permanent deformation of fouled ballast, demonstrating high prediction accuracy for fouled ballast with varying water contents.

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

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

New Approach for Tracking, Monitoring, and Diagnosing Faults in a Photovoltaic System in Real Time: An Experimental and Case Study

Photovoltaic installations have emerged as a cornerstone of sustainable energy production, playing a pivotal role in the global transition towards renewable sources of electricity. As the demand for clean energy surges, so too does the need for robust monitoring and diagnostic strategies to ensure the efficient operation of these systems. This study presents a novel methodology for the real-time tracking, monitoring, and diagnosing of faults in photovoltaic systems (PVSs), emphasizing their crucial role in sustainable energy production amid the global shift to renewable energy. The study was conducted in Guelma, Algeria, during the spring and summer seasons. The investigation utilized WatchPower simulation software over a 24-hour performance analysis of the photovoltaic system. On June 19, 2024, with a high temperature of 32°C, the system achieved a peak output power of 600W under optimal conditions, validating its efficiency in energy generation. The study also analyzed the effects of shading on energy output by comparing data from a shaded day on May 14, 2024, at 25°C, to an unshaded day on June 21, 2024, at 32°C, during peak sunlight hours from 9:00 AM to 2:15 PM, the period when sunlight is at its strongest. The results showed a significant drop in output power from 600W to 450W due to shading, underscoring the importance of real-time monitoring to detect performance inefficiencies. This research not only enhances operational reliability and maintenance strategies for PV systems but also demonstrates the effectiveness of integrating real-time data analytics to support decision-making in similar environments.

Hazard Identification and Risk Assessment During Simultaneous Operations in Industrial Plant Maintenance Based on Job Safety Analysis

The risk of accidents during simultaneous operations (SIMOPS) in plant maintenance has been increasing. However, research on methods to prevent such accidents has been limited. This study aims to develop a novel framework, hazard identification and risk assessment of simultaneous operations (HIRAS), for identifying and evaluating potential hazards during concurrent tasks. The framework developed herein is expected to be an effective safety management tool that can help prevent accidents during these operations. To this end, the job location and hazard information in job safety analysis (JSA) were standardized into four attributes. The standardized information was then synchronized spatially and temporally to develop a HIRAS model that identifies and assesses the impact of hazards between operations. The model was tested using 40 JSA documents corresponding to maintenance operations at Company P, a South Korean steel-making company. The model was tested in two scenarios: one with planned operations and the other with unplanned operations in addition to planned operations. The performance evaluation results of the first scenario showed an F1-score of 98.33%. In this case, a recall of 97.52% means that the model identified 97.52% of the hazard-inducing factors. The second scenario was compared with the results of a review by six subject matter experts (SMEs). The comparison of the results identified by the SMEs and the model showed an accuracy of 89.3%. This study demonstrates the potential of JSA, which incorporates the domain knowledge of workers and can be used not only for individual tasks but also as a safety management tool for surrounding operations. Furthermore, by improving the plant maintenance work environment, it is expected to prevent accidents, protect workers’ lives and health, and contribute to the long-term sustainable management of companies.

Advancements and Challenges in Antenna Design and Rectifying Circuits for Radio Frequency Energy Harvesting.

The proliferation of smart devices increases the demand for energy-efficient, battery-free technologies essential for sustaining IoT devices in Industry 4.0 and 5G networks, which require zero maintenance and sustainable operation. Integrating radio frequency (RF) energy harvesting with IoT and 5G technologies enables real-time data acquisition, reduces maintenance costs, and enhances productivity, supporting a carbon-free future. This survey reviews the challenges and advancements in RF energy harvesting, focusing on far-field wireless power transfer and powering low-energy devices. It examines miniaturization, circular polarization, fabrication challenges, and efficiency using the metamaterial-inspired antenna, concentrating on improving diode nonlinearity design. This study analyzes key components such as rectifiers, impedance matching networks, and antennas, and evaluates their applications in biomedical and IoT devices. The review concludes with future directions to increase bandwidth, improve power conversion efficiency, and optimize RF energy harvesting system designs.

Implementation of a community-based LC-UV drug checking service: promising preliminary findings on feasibility and validity

BackgroundThe increasing diversity of psychoactive substances on the unregulated drug market poses significant health, psychological, and social risks to people who use drugs (PWUD). To address these risks, various harm reduction (HR) policies have been implemented, including drug checking services (DCS). Many analytical methods are used for DCS. While qualitative methods (e.g., thin layer chromatography, spectroscopy) are easier to implement, they are not as accurate as quantitative methods (e.g., LC-UV, LC-MS). Some HR programmes have implemented high-performance liquid chromatography coupled with UV detection (LC-UV). This article presents the cross-validation of this quantitative method with a reference liquid chromatography coupled with high resolution mass spectrometry (LC-HRMS) method.MethodsDrug samples were provided by PWUD to a DCS called DrugLab in Marseille, France. The samples were weighed and prepared through dissolution in methanol, followed by ultrasonic bathing. Samples were analysed onsite using LC-UV analysis. They were then subsequently analysed with the reference LC-HRMS method. The LC-UV instrument in DrugLab was calibrated after being purchased; analysis of standard solutions was routinely performed once a month and after maintenance operations. For the LC-HRMS instrument, calibration and quality control procedures followed European Medicines Agency (EMA) guidelines. Statistical analyses were conducted including Spearman correlation tests using IBM® SPSS® Statistics version 20.ResultsA total of 102 samples representing different product classes and cutting agents were cross-validated. Differences between both analyses methods for each molecule analysed were ≤ 20%, with significant correlations between both methods’ results for most substances. Notably, LC-HRMS provided lower concentration values for cocaine and acetaminophen, whereas it provided higher values for other substances. Correlations were significant for cocaine, ketamine, MDMA, heroin, amphetamine, caffeine, acetaminophen, and levamisole.ConclusionsThis study demonstrates that the results provided by DrugLab were accurate and reliable, making LC-UV an adaptable, stable, and suitable analytical method for simple matrices like drugs in a DCS context. However, this cross validation does not guarantee accuracy over time. A proficiency test project in HR laboratories across France is currently under development in order to address potential drifts in LC-UV accuracy.

Unravelling supply chain complexity in maintenance operations of battery production

Europe’s emerging lithium-ion battery production sector faces immense challenges with Supply Chain Complexity (SCC). This article explores sources of and responses to SCC within maintenance operations of battery production. Using an engaged scholarship approach within automotive original equipment manufacturers (OEMs) and battery cell manufacturers in Sweden, qualitative data were collected and analysed using SCC theory. Our core findings reveal 37 sources of SCC classified by origin and type and 40 responses across four practice clusters. This study offers in-depth insights into the potential impact of SCC on maintenance operations in battery production and provides actionable guidance for managing complexity. It also identifies five future research avenues: investigating complexity interactions, matching sources and responses, exploring complexity-inducing responses, identifying internal-external interfaces and examining social aspects of SCC. In effect, the study sets the agenda for research on battery production maintenance and positions SCC as a versatile theoretical lens for understanding the emerging battery sector.