Maintenance Operations Research Articles

A Novel BWM-RADAR Approach for Multi-Attribute Selection of Equipment in the Automotive Industry

This study examines the application of Multi-Attribute Decision Making (MADM) techniques for selecting an optimal technical solution in the automotive industry, specifically addressing the automatic adjustment and control device of a parking brake cable. The research begins by using the Best-Worst Method (BWM) to determine the weights of criteria, such as device speed, price, weight, and calibration period, based on expert input from maintenance professionals and operators at an automotive original equipment manufacturer in automotive industry. The main objective is to identify the solution that best balances operational efficiency with long-term stability and adaptability to production requirements. In the subsequent phase, both the RADAR method and its modified version, RADAR II, are applied to rank the alternatives. The original RADAR method, which employs ratio-based normalization, tends to favor alternatives that demonstrate stable performance across all criteria, whereas RADAR II, utilizing difference-based normalization, accentuates alternatives that excel in particular aspects. Comparative analysis reveals that although both methods produce generally consistent overall rankings, nuances in the normalization process can lead to differences in the relative prominence of certain solutions. Sensitivity analysis further confirms the robustness and reliability of these approaches, underscoring the importance of selecting a method that aligns with the specific decision-making context. Ultimately, the study demonstrates that a carefully tailored MADM approach, integrating both RADAR and RADAR II techniques, provides valuable insights and supports effective decision-making in complex industrial environments.

45 years of directional wave recorded data at the Acqua Alta oceanographic tower

The dataset comprises a 45-year-long directional wave time series recorded at the Acqua Alta Oceanographic research Tower (AAOT) since 1979. The AAOT is located in the Northern Adriatic Sea and it is managed by the Institute of Marine Sciences of the National Research Council of Italy (CNR-ISMAR). The extent of the time series enables the description of the wave climate in the North Adriatic region and the identification of trends and links with large-scale climate patterns from a single and permanent observational source. Different wave gauges have been used since the start of the measurements, progressively upgraded and repositioned during maintenance operations. The recent addition of more precise instruments and the availability of the related raw dataset allowed for a re-evaluation of the previously published data, while extending the timeseries in time. This has enabled the creation of a substantially improved, yet homogeneous, measured dataset, thereby enhancing the reliability of the related long-term scenario analysis.

Real-Time Modeling of Static, Dynamic and Mixed Eccentricity in Permanent Magnet Synchronous Machines

Eccentricity faults are one of the main causes that significantly affect the performance of permanent magnet synchronous machines (PMSMs). Monitoring eccentricity in real time could prevent failures by adapting operation conditions and maintenance schedule when early signs of deterioration are detected. This article proposes making a circuit-type model of a permanent magnet machine with an easily configurable eccentricity for simulations and real-time analysis of signals under different operating conditions. The basis for the construction of the circuit model will be the simulation of the PMSM with 49 different coordinates of the rotor center, using the finite element analysis (FEA). The presence of eccentricity causes a variation in the inductances, the no-load flux and the expansion torque depending on the position of the rotor. The model proposes the use of bilinear interpolation (BI) to estimate the inductance matrix, the no-load flux vector captured by the stator winding and the cogging torque due to the presence of the magnets in the rotor, all of them for each rotor position. The validation is done by comparing the precision in the results of the machine’s self-inductances, the torque and the voltage waveform at the PMSM terminals and the static torque of the PMSM. The circuit model results are validated in two ways: (1) through experimental simulation, comparing the same results obtained using FEA and (2) through practical experimentation, producing a dynamic eccentricity in the machine of 0.3 mm. The results show that the proposed model is capable of accurately reproducing the behavior of the PMSM against eccentricity faults and presents computational time savings close to 99% compared to the response time obtained using FEA. This rapid PMSM model, parameterizable according to the degree of eccentricity, is the basis for the real-time simulation of the main machine waveforms, such as voltage, current and torque.

Tools and techniques for real-time data processing: A review

Real-time data processing is an essential component in the modern data landscape, where vast amounts of data are generated continuously from various sources such as Internet of Things devices, social media, financial transactions, and manufacturing systems. Unlike traditional batch processing methods that analyse data in intervals, real-time data processing enables the continuous intake, manipulation, and analysis of data within milliseconds of generation. This capability is critical for applications requiring instant insights and rapid decision-making, including fraud detection, predictive maintenance, real-time analytics, and autonomous operations. This paper reviews the tools and techniques that have revolutionized real-time data processing, with a focus on cutting-edge platforms such as Apache Kafka and Apache Flink, as well as cloud-native solutions. These technologies offer scalable and fault-tolerant systems capable of managing high-volume data streams while ensuring low latency and data consistency. Apache Kafka provides a highly scalable distributed messaging system, while Apache Flink combines stateful and stateless processing to support complex event-driven applications. This review highlights the. This paper reviews key techniques and tools used in real-time data processing, including stream processing, complex event processing, in-memory computing, micro-batching, and real-time dashboards. In addition, it highlights advancements in real-time data processing frameworks, their capabilities, and their impact on modern business applications. Additionally, the paper explores various tools used in real-time data processing, including Apache Kafka for data ingestion, Apache Flink and Spark Streaming for stream processing, Redis and Apache Druid for real-time storage, and Grafana and Kibana for data visualization. By examining these techniques and tools, this paper highlights the importance of real-time data processing in enabling businesses to make data-driven decisions with minimal latency, ultimately gaining a competitive edge in the rapidly evolving digital world.

Comparison of diesel and petrol cars operational costs in the long term

The choice between diesel and petrol vehicles is often influenced by both initial purchase costs and long-term operational expenses, including fuel and maintenance. This paper presents a comprehensive cost analysis of various Indian car company variants, specifically examining the diesel and petrol models across different trims (NY2, NY3, X6, X8, X8 (O), BY5, and BY5L) over 1, 5, 10, and 15-year periods. Using a detailed financial model, this study accounts for initial vehicle cost, fuel consumption based on varying fuel prices, and routine maintenance costs. The research applies real-world data on fuel efficiency and prices, considering both minor and major servicing costs.

IoT and AI-Based Predictive Maintenance System Design for Express Auto Repair Shops

The purpose of this paper is to present a modular system that acquires data from various machines used in an express mechanical workshop. These data are classified and processed using artificial intelligence and IoT (internet of things), enabling the creation of predictive maintenance plans and operational schemes. This approach helps reduce operational costs, maintenance expenses, and energy consumption throughout the process

Monitoring and Improving Aircraft Maintenance Processes Using IT Systems

Aircraft maintenance is a complex, multifaceted process that greatly benefits from IT systems designed to improve supervision, record keeping, and task management. This study focuses on the role of a dedicated mobile application, integrated into the broader IT Aircraft Maintenance Support System, which supports maintenance operations for the M-346 BIELIK training aircraft. Aircraft maintenance is a highly intricate and multifaceted process that significantly benefits from advanced IT systems designed to enhance supervision, streamline record keeping, and optimize task management. This study explores the pivotal role of a dedicated mobile application integrated into the broader IT Aircraft Maintenance Support System, specifically tailored to support the maintenance operations of the M-346 BIELIK training aircraft. By focusing on the analysis of Intelligent Transportation Systems (ITSs), the research highlights how the application contributes to maintenance reliability and operational efficiency, with sustainability considerations in mind. The ITS-based approach assesses maintenance scheduling, tracking, and resource optimization, thereby enhancing the reliability of aircraft operations while reducing unnecessary resource consumption. This alignment with sustainable practices not only improves reliability characteristics and exploitation rates but also positively impacts the efficiency and effectiveness of aviation training. By accurately estimating the time requirements of specific maintenance tasks during periodic inspections, the application aids in identifying and addressing organizational bottlenecks, ultimately supporting both operational sustainability and improved task reliability across maintenance activities.

Vibration-Based Anomaly Detection for Induction Motors Using Machine Learning

Predictive maintenance of induction motors continues to be a significant challenge in ensuring industrial reliability and minimizing downtime. In this study, machine learning techniques are utilized to enhance fault diagnosis through the use of the Machinery Fault Database (MAFAULDA). A detailed extraction of statistical features was performed on multivariate time-series data to capture essential patterns that could indicate potential faults. Three machine learning algorithms—deep neural networks (DNNs), support vector machines (SVMs), and K-nearest neighbors (KNNs)—were applied to the dataset. Optimization strategies were carefully implemented along with oversampling techniques to improve model performance and handle imbalanced data. The results achieved through these models are highly promising. The SVM model demonstrated an accuracy of 95.4%, while KNN achieved an accuracy of 92.8%. Notably, the combination of deep neural networks with fast Fourier transform (FFT)-based autocorrelation features produced the highest performance, reaching an impressive accuracy of 99.7%. These results provide a novel approach to machine learning techniques in enhancing operational health and predictive maintenance of induction motor systems.

Design and Fabrication of Dryer for Zeolite Synthesis: Engineering Solution for Enhanced Material Processing

The design and fabrication of the electric dryer from locally sourced materials represent a cutting-edge advancement in drying technology, harnessing the unique properties of Zeolite materials to enhance energy efficiency and performance. Zeolites are microporous, aluminosilicate minerals known for their high adsorption capacity and thermal stability. Drying is one operation required in Zeolite synthesis and as a developing Nation, there is need to invest in design and fabrication of dryers instead of relying on the traditional method of drying where the Zeolite are allowed to dry natural in open air. The Electric dryer is engineered to provide precise temperature and speed control, uniform heat distribution and efficient air circulation thereby enhancing drying efficiency, reducing operational costs and promoting local technology. The dryer consists of a mild steel chamber, heating element, fan, thermocouple, temperature and speed control system. The fabrication process involved machining, welding and assembly of the various components. The dryer performance was evaluated by drying Kaolin (a precursor for Zeolite synthesis) samples under a certain temperature and fan speed to obtain percentage reduction in moisture content to 20% from 60%. The temperature range of the dryer is 28 to 300 degrees Celsius, dimensions of the designed and fabricated dryer are Width of 0.2286m, Height of 0.3048m and Breadth/depth of 0.3048m, Volume of dryer is 0.0 , Insulation thickness is 0.04m. while area of dryer door is 0.110 . For panel dimension: Height is 0.254m, Width is 0.152m and area of panel is 0.0386 , Tray Dimension is 0.241m by 0.330m and area of tray 0.0795 . Standard Operating Procedure and maintenance of the electric dryer was also captured in this work. The regulation of the fan speed is a novel approach to our design. This innovation represents a significant leap in the development of energy-efficient dryer, promising enhanced performance and reduced environmental impact.

A Network Architecture for Scalable End-to-End Management of Reusable AI-Based Applications in 6G Networks

This article presents a comprehensive network architecture for managing reusable AI-based applications in 6G networks, addressing the critical challenge of AI silos in current implementations. It introduces a unified approach to data collection, feature extraction, model management, and application integration across network domains. By implementing standardized workflows and shared resources, the architecture enables efficient end-to-end management while promoting reusability and scalability. The solution incorporates a unified data collection layer, shared feature repository, model management framework, and application integration layer, all designed to support the demanding requirements of next-generation networks. Through multiple use cases including RAN optimization, network security, and service quality management, the article demonstrate the architecture's effectiveness in real-world scenarios. The results show significant improvements in development efficiency, resource utilization, scalability, and maintenance operations. It contributes to the evolution of 6G networks by providing a structured approach to integrating AI capabilities while preventing the formation of isolated solutions.

Relationships Between Common Distresses in Flexible Pavements and Physical Properties of Construction Materials Using an Ordinal Logistic Regression Model

Analytical models to predict distresses and service conditions of road pavements can greatly contribute to the development of an effective pavement management system. These models allow the transportation agencies to monitor and track the deterioration of pavements and consequently determine the needed maintenance operations to preserve the performance of the network. In this research, the pavement distresses and service conditions of the Indus Highway N-55 located in Karak district, Pakistan were examined. Distresses were identified by visual observation, and then their severity and extent were measured individually by using a Vernier caliper and a measuring scale. For each distress type, the corresponding PCR was calculated. The compaction densities of the base and wearing courses were considered as input parameters to develop an ordinal logistic regression model for two dominant distresses, namely rutting and potholes. Rutting severity and extent were divided into three levels, while pothole severity was divided into four levels. Bulk and maximum specific gravity were found to have a significant impact on the models of both distresses. The model can be used to predict their development in terms of severity and extent. The proposed formulation provides valuable insights into monitoring and predicting pavement distresses by assessing the densities of road construction materials.

An open-source GIS app for digitalising bridge inspections and automating BIM model integration

Ensuring the safety and functionality of civil infrastructure, especially bridges, is critical for sustainable transportation networks. To streamline maintenance operations, on-site inspections are periodically conducted. The routine inspections of bridges frequently depend on manual processes, which diminish efficiency and data accessibility, leading to errors that jeopardise accurate decision-making. This paper advocates for the digitalisation to address these issues, developing a low-cost Geographical Information System (GIS)-based app for streamlined bridge inspections. The digital protocol automatically transfers georeferenced on-site inspection information into the bridge’s Building Information Model (BIM) using a parametric programming tool. This integration aims to improve decision-making in maintenance operations. The developed approach is validated through a real bridge inspection of a concrete bridge in Spain.

Occupational Injuries and Fatalities in Norwegian Fish Farming

ABSTRACT Objective People working in the Norwegian fish farming industry work in a high energy environment, where there are many hazards in the daily work. An important part of mitigating hazardous situations is to keep track of the characteristics of the accidents that have already happened and to learn from these, when planning future work. The objective of this study was to strengthen the knowledge of factors and conditions influencing personnel safety in Norwegian fish farming, based on analyses of registered occupational fatalities and injuries. Methods We gathered reported injuries and fatalities from vessels and fish farms from three different registries. Two of these are based on mandatory reporting to authorities, and one is a privately maintained registry. The accidents from the three registries are analyzed separately, and different types of characteristics are presented. Results The results demonstrate that fall and crush/impact are the most common types of accidents both on vessels and fish farms. Other characteristics described are the type of vessels involved and during which type of operations injuries happened. During the last 10 years, fatalities have happened mainly in relation to lifting and maintenance operations. The reported accidents are discussed in relation to the quality of accident reports, the development in the industry towards more specialized and outsourced operations, and it points to some of the major challenges that the industry faces when planning for safe working places and operations. Conclusion This overview can be used in the fish farming industry as a basis for going into further details about how accident prevention should be planned.

Framework for Design and Construction of Long-Life Concrete Pavements

In the past, concrete pavements were routinely designed and constructed to provide low-maintenance service life of 20 to 25 years. In fact, the majority of the U.S. interstate and the primary system were designed on the basis of the 20 to 25 year initial service life. Experience has shown that pavements in high volume traffic corridors need to be designed and constructed to provide longer service life because of the difficulties in performing effective repair and rehabilitation activities along these high volume highway corridors. In addition, the public is no longer tolerant of frequent extended lane closures to perform repair activities. It is becoming an established practice in the U.S. to require that concrete pavements provide low-maintenance service life of 40 plus years. PCC pavements can meet this specific requirement if proper considerations are incorporated in the design and good construction practices, including use of sound concrete making materials, are followed. This paper provides an overview of current U.S. practices and efforts at optimizing pavement design features and construction practices to minimize early age failures and provide long-term low-maintenance service in excess of 40 plus years. The paper also provides a framework for ensuring that the critical design and construction features that impact long-term service are recognized and accounted for. The design and construction features discussed include features that improve slab cracking and deflection responses, features that minimize maintenance operations, and construction features that assure long-life pavement.

Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance

This paper proposes a sustainable model for integrating robotic process automation (RPA) and machine learning (ML) in predictive maintenance to enhance operational efficiency and failure prediction accuracy. The research identified a key gap in the literature, namely the limited integration of RPA, ML, and sustainability in predictive manufacturing, which led to the development of this model. Using the PICO methodology (Population, Intervention, Comparison, Outcome), the study evaluated the implementation of these technologies in Alpha Company, comparing results before and after the model’s adoption. The intervention integrated RPA and ML to improve failure prediction accuracy and optimize maintenance operations. Results showed a 100% increase in mean time between failures (MTBF), a 67% reduction in mean time to repair (MTTR), a 37.5% decrease in maintenance costs, and a 71.4% reduction in unplanned downtime costs. Challenges such as initial implementation costs and the need for continuous training were also noted. Future research could explore integrating big data and AI to further improve prediction accuracy. This model demonstrates that integrating RPA and ML leads to operational improvements, cost reductions, and environmental benefits, contributing to the sustainability of industrial operations.

The worksites of the Duomo di Milano: extending the life of a Cathedral

Construction, damage and conservation are intertwined in the life of Milan Cathedral. The evolution of the structural system and the restorations carried out throughout the centuries are described with a particular focus on the case study of the tiburio, with the supporting pillars; these underwent dramatic damage in the XX century, close to collapse, followed by repair and strengthening interventions and the rebirth of the Cathedral. This same part of the monument is currently the object of the latest ongoing restoration interventions, related to service state conditions. The organization of the Veneranda Fabbrica del Duomo di Milano is outlined by the worksite management. The inspection and maintenance are set in relation to the technical activities of other institutions. The aim of all operations for the preservation of the structural safety and cultural identity of the monument up to the present day, is discussed. A discussion is provided for monumental heritage buildings, encompassing structural assessment, reliability evaluation, damage survey, maintenance and repair operations, in relation to the underlying social values.

Holographic Storage for the Cloud: advances and challenges

Holographic Storage is an old idea that has always promised high density and fast random access, but has never been commercially competitive with Hard Disk Drives (HDDs) and Solid State Devices (SSDs). In Project HSD at Microsoft Research we asked the question: “Does holographic storage finally make sense for cloud storage?” This article describes our journey toward answering this question. We achieved 1.8× higher density than the previous state-of-the-art, using commodity components available today and leveraging machine learning to compensate for the noise and distortions introduced by commodity components. This uncovered two new challenges which are the focus of this article: achieving high end-to-end energy efficiency without sacrificing capacity, and spatial multiplexing without mechanical movement. Improving end-to-end energy efficiency requires joint optimization across low-level media parameters and higher-level system parameters that govern background maintenance operations such as read refresh and garbage collection. We developed new physics models of the media; analytic and simulation models of the media access and background media maintenance; and workload-driven optimization to find optimal parameter combinations. These techniques resulted in a 14× improvement over the previous approach for typical workloads without sacrificing capacity. We also designed the first scalable and mechanical movement free spatial multiplexing system for holographic storage. Despite these advances, we conclude that currently, holographic storage is still far from the combination of density, capacity scaling, and energy efficiency needed to compete with the incumbent technologies. We need fundamental advances in the physical media that improve energy efficiency by another 1–2 orders of magnitude without reducing data density. Further advances in optics are also required to achieve spatial multiplexing that is simultaneously scalable, low-loss, and high-density.

A GPS- enabled Fuel Sensor based Vehicle Tracking System for Fleet Management using the Internet of Things

The key objective of this paper is to monitor the Fuel consumption, route deviations, driving habits, breakdown details, vehicle maintenance conditions, and other vital performance details of a Heavy vehicle especially a college bus using a pre-fitted fuel sensor needed for an extensive fleet management system as operational costs and maintenance are escalating day by day. In particular, vehicle owners are confronting fuel break-ins, spare parts theft and illegal parking or route diversions in transport vehicles. In addition to this, vehicle proprietors don't ascertain operational details daily if they are illiterate. Various models are available which Standalone, Onboard diagnostics-based, GPS GPS-based fuel tracking Systems. This research project leverages IoT technology, GPS-based sensors, and a GSM tracking system to provide vehicle owners with real-time information on fuel consumption. This cloud-based and mobile application tracks the vehicle in real time. Most of the models are web-based and custom-built only. When the driver initiates fuel filling, the Fuel sensor in the tank automatically activates and senses the amount of fuel in the diesel tank by transmitting fuel input data to a connected cloud web server for storage and analysis along with other data. The gathered values are cross-verified with the database, and an alert message is promptly sent to the vehicle owner based on consumption patterns. This mobile app-based Vehicle Tracking System (VTS) application, using SMS and Vehicle tracking features offers numerous advantages to the vehicle owner, primarily focusing on preventing fuel theft.

"4R" FRENCH TECHNIQUES AND PERFORMANCES

This study presents an overview of the techniques and strategies implemented for the maintenance of concrete pavements in France, with a focus on practices developed since 1975. Initially, the French experience demonstrated that, despite their reputation for durability, concrete pavements require maintenance, particularly in heavily trafficked areas. This necessitated the development of a comprehensive maintenance strategy, introduced in the late 1970s. Key components of the methodology include diagnostic procedures that rely on understanding the pavement's condition through detailed investigations, including the use of innovative equipment like the collograph. The collograph is particularly noted for its ability to evaluate concrete pavements by measuring deflections under load, aiding in the accurate assessment of pavement conditions. The maintenance strategies discussed encompass various techniques to restore and enhance pavement performance, such as maintaining surface roughness, restoring load transfer at joints, and reconstructing heavy-traffic lanes. The effectiveness of these strategies is illustrated through specific maintenance operations that France has pioneered, highlighting both their practical implementation and performance outcomes. Furthermore, the study delves into the specific maintenance operations employed, such as continuous reinforced concrete overlays and their adaptation to the French road network's needs. It also discusses the experimental and operational phases of these techniques, providing insights into the successes and challenges encountered. In summary, this paper encapsulates the evolution of concrete pavement maintenance in France, emphasizing the integration of diagnostic techniques and maintenance operations tailored to the demands of heavily trafficked routes, thereby contributing to the prolonged durability and safety of the road network. (Abstract generated by AI tool ChatGPT 4)

Bioinspired nanofilament coatings for scale reduction on steel.

Scaling of steel surfaces, prevalent in various industrial applications, results in significant operational inefficiencies and maintenance costs. Inspired by the natural hydrophobicity of springtail (Collembola) skin, which employs micro- and nanostructures to repel water, we investigate the application of silicone nanofilaments (SNFs) as a coating on steel surfaces to mitigate scaling. Silicone nanofilaments, previously successful on polymers, textiles, and glass, are explored for their hydrophobic properties and stability on steel. Our study demonstrates the successful coating of stainless steel with SNFs, achieving super-hydrophobicity and resilience under high shear stress and explosion/decompression tests. Scaling experiments reveal a 75.5% reduction in calcium carbonate deposition on SNF-coated steel surfaces. This reduction is attributed to altered flow dynamics near the super-hydrophobic surface, inhibiting nucleation and growth of scale. Our findings highlight the potential of bioinspired SNF coatings to enhance the performance and longevity of steel surfaces in industrial environments.