Maintenance Methods Research Articles

Modelling the Prioritisation of Technical Objects Using the EPN Indicator

The objective of this article is to analyse and evaluate the effectiveness of predictive maintenance for machines performing key functions within a production structure. This article presents a methodology for determining the Equipment Priority Number (EPN), calculated based on parameters such as energy consumption, the criticality of machines in the value stream, and their impact on the continuity of the supply chain. The experimental implementation of a system for monitoring operational parameters—including current consumption, vibrations, and torque moments—enabled the prediction of potential failures and the planning of maintenance actions, which contributed to improving operational stability and reducing the risk of unplanned downtime. The obtained results confirm the effectiveness of the proposed methodology and demonstrate that a predictive maintenance system supported by the EPN indicator enables accurate prioritisation of maintenance activities in an actual production system. The findings also show that implementing the EPN algorithm allows for more precise prioritisation in highly customised production environments. Furthermore, the analysis of the collected data suggests the potential for further optimisation through the integration of data-driven diagnostics and artificial intelligence methods, which could enhance the efficiency and competitiveness of the system. This study’s conclusions provide a foundation for advancing predictive maintenance methods in industrial production.

Efficient Task Scheduling Using Constraints Programming for Enhanced Planning and Reliability

This paper presents an efficient schedule method for maintenance, repair, and overhaul (MRO) tasks for aircraft engines using a constraint programming algorithm. Using data obtained from Korean Air’s MRO maintenance logs, we analyze and predict the optimal scheduling of regular inspections and fault repairs for various engine types. By proposing a proper modeling of the problem and preparing data for the constraint programming algorithm, we demonstrate superior performance in scheduling efficiency and resource utilization. The experimental results show an average utilization of 99.35%, and the method can even achieve 100% utilization in some cases.

Fabrication of a novel “all in one glove”- a functional tool for oral hygiene maintenance and the assessment of its effectiveness on plaque control in spastic cerebral palsy patients

Background & objectiveSpastic Cerebral Palsy (CP) is a neurological disorder from non-progressive brain injury or malformation during development, severely impairing movement, coordination, reflexes, posture, balance, and motor skills. Individuals with spastic CP experience limb spasticity, complicating tasks like precise gripping, particularly with toothbrushes. This study proposed a novel “all-in-one glove” for oral hygiene, assessing its efficacy and comparing it to conventional toothbrushes for spastic CP patients.Methods & materialsThis randomized controlled trial was conducted on 30 clinically diagnosed spastic CP patients with Group 1 consisting of 15 Subjects with clinically diagnosed spastic CP with generalized gingival diseases using conventional oral hygiene techniques with weekly oral hygiene reinforcement and Group 2 consisting of 15 Subjects with clinically diagnosed spastic cerebral palsy with generalized gingival diseases using the novel “all in one glove” method for oral hygiene maintenance with weekly oral hygiene reinforcement. The Clinical Parameters assessed includes Full mouth plaque scores, Full mouth bleeding scores and OHI scores recorded at baseline, 3 and 6 months.ResultsA statistically significant reduction was observed in Full-mouth Plaque, Bleeding, and Oral Hygiene Index (OHI) scores in both the groups (48.87% & 43.285%, 31.57% & 26.66%, 57.29% & 47.37% respectively). The Group 2 exhibited a marginally higher percentage reduction in all clinical parameters compared to the Group 1 at the measured time points.ConclusionThe utilization of the “All in one glove” has improved the effectiveness of oral hygiene maintenance and gingival health in spastic CP individuals. Clinical Trial Registration[ClinicalTrials.gov], identifier, [REF/2022/06/055641].

Noise robust classification of carbide tool wear in machining mild steel using texture extraction based transfer learning approach for predictive maintenance

Acoustics based smart condition monitoring is a viable alternative to mechanical vibrations or image-capture based predictive maintenance methods. In this study, a texture analysis based transfer learning methodology was applied to classify tool wear based on the noise generated during mild steel machining. The machining acoustics were converted to spectrogram images and transfer learning was applied for their classification into high/medium/low tool wear using four pre-trained deep learning models (SqueezeNet, ResNet50, InceptionV3, GoogLeNet). Moreover, three optimizers (RMSPROP, ADAM, SGDM) were applied to each of the deep learning models to enhance classification accuracies. Primary results indicate that the InceptionV3-RMSPROP obtained the highest testing accuracy of 87.50%, followed by the SqueezeNet-RMSPROP and ResNet50-SGDM at 75.00% and 62.50% respectively. However, SqueezeNet-RMSPROP was determined to be more desirable from a practical machining quality and safety perspective, owing to its greater recall value for the highest tool wear class. The proposed acoustics-texture extraction-transfer learning approach is especially suitable for cost effective tool wear condition monitoring involving limited datasets.

Detection of Damage Motor and Coal Crusher in Power Plant Tanjung Enim 3 X 10 MW Using Vibration Analysis

Motor and coal crushers are critical in coal size reduction for efficient energy production. Continuous operation often impacts their functionality, necessitating effective maintenance methods. This study employs vibration analysis to monitor the health and detect potential faults in the motor and coal crusher. Vibration values, measured under load and no-load conditions, were analyzed in vertical, horizontal, and axial directions using ISO 10816 standards. Results show that while vibration levels remained within safe limits, increased readings in specific areas, particularly at the Crusher NDE in axial direction under load conditions, suggest potential issues like misalignment or bearing wear. Prominent vibration peaks at lower frequencies in the spectrum further support these findings. This research underscores the importance of routine vibration monitoring to preemptively address machine faults, ensuring optimized operation and minimized downtime. Vibration analysis proves effective for predictive maintenance and operational reliability in coal processing systems.

The Integrated Development of Wireless Networks and AI

This paper combines the current research status of wireless networks and artificial intelligence, delving into the state of wireless network research and its impacts, and highlights the importance and necessity of the integrated development of wireless networks and AI. The aim is to provide readers with an understanding of the current situation and challenges of wireless networks. With the increasing number of mobile communication users, spectrum resources are becoming increasingly scarce, network security issues are more prominent, and the complexity of network architecture and business processes has significantly increased, making it difficult for traditional operations and maintenance methods to meet these demands. Therefore, leveraging AI to build a new mode of communication network operation has become an inevitable choice. The intelligent evolution of wireless networks is an unstoppable trend. In the future, full intelligence will be achieved through digital infrastructure, knowledge introduction, and digital twins, driving the efficient development of wireless networks.

Review on Semi-Autonomous Robots for Satellite Maintenance and Refueling

As the number of satellites in Earth's orbit continues to grow, the need for efficient satellite servicing solutions becomes increasingly pressing. Currently, about 9,900 satellites are in orbit, with over 3,300 of these being inactive, posing a threat as space debris. Traditional methods for satellite maintenance and refueling are complex, expensive, and risky. The development of semi-autonomous robots, powered by advancements in artificial intelligence and robotics, presents a more efficient alternative. These robots can perform complex maintenance and refueling tasks with minimal human intervention, reducing costs and risks associated with satellite servicing. This paper investigates the design, technological advancements, and operational strategies behind these robots, and how they can mitigate space debris while enhancing the sustainability of satellite operations. Key Words: Maintenance, Satellites, Refuelling, Robots.

A dynamic maintenance planning methodology for HVAC systems based on Fuzzy-TOPSIS and failure mode and Effect Analysis

Maintenance is critical to the efficiency of Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings. The conventional maintenance methods are planned for ideal situations and cannot easily be adapted to prevailing management/operational conditions, resulting in increased use of resources and energy. This study developed a dynamic maintenance planning framework for HVAC systems employing Fuzzy-TOPSIS and FMEA and aimed at reducing and stabilising maintenance costs while enhancing system reliability. Fuzzy-TOPSIS technique was used to establish maintenance priorities based on a carefully identified set of criteria, including the risk priority number (RPN), which is adaptable to changing conditions and ensures continuous monitoring. This is a novel application to HVAC systems. A real-world implementation in an HVAC company involving data collection, fuzzification of expert evaluations, priority ranking of components, and development of maintenance ranking validated the framework's efficiency. The running conditions of the HVAC system under the new approach, expressed by the air quality and air leakages (by extension energy saving), showed a significant improvement at the various test points in the 600-h running. The results demonstrated significant cost savings, enhanced system reliability, improved energy efficiency, and better indoor air quality. This dynamic maintenance planning framework offers a concise and adaptable solution for optimising HVAC maintenance operations.

Repairing smarter: Opportunistic maintenance for a closed-loop supply chain with spare parts dependency

Adopting a closed-loop supply chain enhances spare part provisioning through repair, remanufacturing, and recycling. However, poor maintenance of components can have severe consequences. Unlike traditional opportunistic maintenance methods that assume regular inspections or precise degradation monitoring, we propose a model that leverages historical repair data to replace worn components preventively. It considers the real-world workflow where parts are often restored only to a functional level. We study maintenance strategies for repeatedly repaired multi-component systems by applying preventive operations only during corrective repairs. Our model considers component ages, failure time distributions, and structural and economic dependencies, favoring collective over individual replacements for cost efficiency. Stochastic dependencies are mapped using Nataf transformation for component subsets, and a genetic algorithm identifies optimal maintenance strategies to reduce long-term operational costs by balancing maintenance against potential failure penalties. We demonstrate the effectiveness of our approach with a case study on MRI power supply machines, showing that preventive actions can cut early life failures by up to 50% and extend useful life by over a year. Sensitivity analysis reveals that logistic costs, interest rates, and planning horizons influence decisions. Opportunistic maintenance can reduce logistic costs and increase the lifetime of spare parts after repair. Integrating stochastic dependency is computationally efficient for industrial systems and can help predict failures more accurately.

Virtual reality in the rehabilitation of motor disorders in diseases and injuries of the musculoskeletal system

Modern rehabilitation is constantly evolving, the latest methods of recovery and maintenance of patients are emerging, giving maximum efficiency at relatively low economic costs. Virtual reality is a powerful and proven effective tool for modern rehabilitation of patients with various pathologies compared to standard physical therapy. The immersive environment created by the program is able to simulate the necessary conditions for the formation of the correct movement pattern, keeping the patient’s attention, keeping him interested in further classes, and motivating him to achieve better results. Also, by influencing the emotional sphere and the full involvement of the patient in the process, the positive effect of such activities on the intensity of pain was noted. Published works describing the high effectiveness of the virtual reality method during the rehabilitation of neurological patients, patients with injuries and diseases.

Digitalization of Adjustment and Maintenance Processes for Optimizing Energy Consumption in the Oil and Gas Sector

This article examines the impact of digitalization on adjustment and maintenance processes in the oil and gas sector aimed at optimizing energy consumption. Modern technologies such as big data analysis, the Internet of Things (IoT), and Machine Learning (ML) algorithms are discussed, highlighting how they assist companies in reducing energy consumption and enhancing operational efficiency. Examples of the implementation of these solutions in major companies are provided, demonstrating significant reductions in energy costs and equipment downtime. The advantages of contemporary approaches over traditional adjustment and maintenance methods are emphasized.

Maintenance Methods and Strategies for Existing Bridges in China

Abstract. In China, bridges constructed in the past century have deteriorated due to environmental exposure and increasing loads, necessitating attention from researchers on assessment, maintenance, and reinforcement to ensure safety. Appropriate design and construction techniques are key to improving load-bearing capacity and extending service life. This paper summarizes China's bridge challenges, introduces the development of bridges in China since the 20th century and visual inspection methods, and outlines strengthening techniques for bridge superstructures and substructures. Furthermore, the paper concludes with a discussion of future research directions, highlighting the importance of continued efforts to ensure the safety, reliability, and longevity of these vital transportation structures.

A Magnetic Climbing Robot for Steel Bridge Inspection

Abstract. The maintenance of traditional steel bridges presents many significant challenges due to the fact that bridges of different ages and standards vary in design, structural complexity and technical difficulty. In light of these considerations, it is clear that maintenance personnel must possess a wealth of professional expertise and experience in order to effectively address the diverse and intricate maintenance challenges that arise. Moreover, the maintenance process entails high-risk operations such as high-altitude work and welding, which pose a threat to the safety of construction personnel. Therefore, this study introduces a magnetic adsorption robot designed to address the low efficiency and high costs of traditional maintenance methods. This robot features a dual robotic arm mechanism and modeled using the Denavit Hartenberg (D-H) parameter method. Electromagnetic adsorption is utilized to achieve wall adhesion, and direct power supply is provided through DC power to control weight. The robot can perform flexible remote control operations through the HC-05 Bluetooth module. The experimental results show that the robot exhibits robust adhesion and obstacle crossing ability on steel surfaces, thereby enabling unimpeded movement within the steel structure. In addition, the robot is now capable of performing overhaul procedures via remote control.

Advanced Machine Learning Algorithms for Predictive Maintenance in Industrial Manufacturing Systems

In industrial manufacturing systems, predictive maintenance is the process of increasing the rate of productivity and minimizing the time that equipment takes to be out of order through early identification of the equipment that is likely to fail. The main focus of this research is to analyze the possibility of using modern approaches in machine learning to enhance the methods of predictive maintenance. We compare multiple current approaches of deep learning, ensemble methods, and anomaly detection to determine their effectiveness in predicting the maintenance requirements utilizing the sensor and operational data. With the help of a large amount of data, we consider the results of the work of each algorithm for the assessment of the predictive accuracy, the ranking of features, and the detection of anomalies. The findings highlight disparities in the effectiveness of the algorithms in terms of accuracy, precision, and recall, and the deep learning models’ ability to grasp intricate and anomalous patterns. The performance of the maintenance predictions is depicted by the use of visualizations of the performance metrics and feature importance. It also describes the drawbacks of the existing models, such as the problem of data quality and generalization. The study draws attention to the possibility of applying sophisticated machine-learning methods to improve the effectiveness of PM in industrial environments. Possible directions of future research are to enhance the generalization ability of the developed models and to expand the usage of modern trends in the machine learning field to enhance maintenance strategies.

Best IDEAS: Special Issue of the International Database Engineered Applications Symposium

Database engineered applications cover a broad range of topics including various design and maintenance methods, as well as data analytics and data mining algorithms and learning strategies for enterprise, distributed, or federated data stores [...]

Improved machine learning models with a similarity-based approach for remaining useful life prediction

Abstract Cost efficiency is a critical factor in the competitive aviation sector. These efficiency factors force airline operators to develop new approaches in their organizations. Predictive maintenance helps to build scheduling maintenance programs for airline operators or MROs. Scheduled maintenance programs benefit cost efficiency in the aviation sector. Predictive maintenance methods predict the failure time of any equipment. Predictions can be made by analyzing the sensor values from equipment. In this paper, we predicted the remaining useful life (RUL) of turbofan engines using machine learning models and a similarity-based approach. Sensor datasets from the Prognostics Data Repository of NASA, called CMAPPS, were utilized. Using the FD0002 sub-dataset, a health index (HI) was created, and models were trained. Once the models were trained, train and test HIs were estimated. The predicted test HI was matched with the predicted train HI based on a similarity-based approach, and then a RUL prediction was made. The results obtained were compared with the actual results to calculate the accuracy, and the algorithm that resulted in the maximum accuracy was identified. We selected six machine learning algorithms and also created an ensemble model by averaging the predictions of six machine learning algorithms for comparing prediction accuracy. The different algorithms were compared to obtain the prediction model with the closest prediction of remaining useful lifecycle in terms of the number of life cycles. This experiment showed us the effect of the similarity-based approach on the basic version of machine learning models for RUL prediction.

Reliability analysis of the vehicle door system EDCU based on Weibull distribution

The paper established a two-parameter Weibull distribution model to analyze the failure law of key components of metro vehicles and employed the least squares method to fit the parameters of the Weibull function. The paper provided a detailed introduction of the model's parameter fitting method based on actual historical failure data. Taking the EDCU (Electrical Door Control Unit) as an example, the failure rate, cumulative failure rate, and reliability of EDCU for 3 different vehicle models were calculated, and the calculation results were verified to conform to the Weibull distribution through Q-Q diagrams. The results show that although the designed service life of EDCU of vehicle door system is 15 years, the reliability of the EDCU decreases significantly around 10 years. The EDCU reliability of the three vehicle models declines by 22%, 4.2%, 10.6% and the failure rates are 2.7%, 0.57%, 1.13%. Based on the reliability of EDCU mentioned above, while ensuring the reliability of components, the maintenance methods of components can be optimized to reduce maintenance costs.

Innovative technology for microalgal cell preservation through immobilization in polylactic acid nanofibers

Microalgae are of great biotechnological importance. Thus, it is essential to apply maintenance methods for the utilization of microalgae at any time. Facilitating microalgae adsorption processes on nanofibers may be a promising approach for microalgae preservation. Thus, the objective of this study was to apply poly (lactic acid) nanofibers in the preservation of microalgae Chlorella fusca LEB 111 cells. The nanofibers were characterized regarding their morphology, thermal properties, structural characteristics and wettability. The microalgae cells were immobilized on the nanofibers and stored for 30 days at room temperature, refrigeration and thermostated chamber. Free microalgae cells were also maintained for the same period under the same conditions of the traditional method of microalgae preservation, continuous replication. The cell viability of the free and immobilized cells on the nanofibers was analyzed by Neutral Red (NR) and Trypan Blue (TB). At the end of the experiment, the immobilized cells showed greater viability (94 and 100 %) compared to the free cells (84 %). The cultivation of immobilized cells showed significant cell growth on the 25th day of cultivation for the evaluated storage conditions (3.6, 3.6 and 2.8 g L−1 for refrigeration, room temperature and thermostatted chamber, respectively). Therefore, poly (lactic acid) nanofibers (PLA) are characterized as an innovative technology for microalgae maintenance.

DESIGN OF COMBINED SCALAR INDICATORS FOR FAULT DETECTION OF ROTATING MACHINES

The machine health monitoring becomes crucial for any company using rotating machines. A rotating machine is subject to developments that induce changes in its vibration behaviour. These changes can be due either to a change in its parameters (load or speed), or to degradations, or to defects affecting its intrinsic characteristics and behaviour. Several maintenance methods are currently used to detect defects in rotating machines. Time-domain analysis is one of the methods for detecting defects that use well-known scalar indicators such as Root Mean Square (RMS), Kurtosis, peak-to-peak value, crest factor etc. However, these indicators may have limitations when it comes to detecting multiple defects in machines. In this article, we propose new innovations in the field of the monitoring of rotating machine by vibration analysis, specifically in time-domain analysis. We propose new indicators for detecting defects by linear combination of scalar indicators We evaluate these indicators in order to assess their capacity to detect a single or multiple defects, their capacity to assess the severity of a defect, and their capacity to detect a healthy gear or a defective gear in different configurations of defects created on the gear and on the bearing mounted on an experimental test bench.

PATOLOGIAS EM PAVIMENTOS FLEXÍVEIS: TRECHO AVENIDA TORQUATO TAPAJÓS COM AVENIDA CONSTANTINO NERY NA CIDADE DE MANAUS - AM

This study aimed to conduct a case analysis through the evaluation and assessment of pathologies in flexible pavements at a stretch of Avenida Torquato Tapajós with Avenida Constantino Nery, in the city of Manaus - AM. The research sought to identify the causes and origins of these pathologies, relating solutions to each diagnosed situation based on theses, articles, and consulted literature. The main objective of this article is to analyze the most common pathologies in flexible pavements. The research is justified by the need to assess the maintenance of damages and pathologies in pavements, a constant factor in the infrastructure sector that incurs high costs and requires thorough analysis to determine the procedures to be adopted. The method used was a descriptive qualitative exploratory study, grounded in existing literature and field documentation. Based on the analysis of the pathologies, the necessary maintenance methods were defined in accordance with federal regulations and current standards in Brazil. The results revealed different classifications of pathological manifestations in flexible pavements. It was found that the traffic anticipated for the road project does not align with its actual usage, contributing to the noticeable occurrence of cracks in the analyzed roadway. Implementing best practices can mitigate the negative impacts of these pathologies, ensuring the longevity of pavements and the safety of users. Adherence to established guidelines is crucial to ensure that flexible pavements meet required standards, avoiding future problems arising from inadequate interventions. With the increasing digitization in the construction sector, the integration of new technologies becomes increasingly viable for managing pathologies in flexible pavements in heavily trafficked highway segments.