Maintenance Engineers Research Articles

Digitalize Training Administration Application to Support Aviation Polytechnic towards Smart Vocational Training Centre

This research aims to develop a "Digitalize Training Administration" application to support Medan Aviation Polytechnic in realizing the vision of "Smart Vocational Training Centre". This application is designed to replace the conventional education administration system with an integrated system that is easy to use. The research method used is a qualitative approach with a case study as the research design. Data were collected through observation, interview, and documentation. As part of the Medan Aviation Polytechnic, the Aircraft Maintenance Technology Study Program (TPPU) plays an important role in vocational education and has been certified as an Approved Aircraft Maintenance Training Organization (AMTO) by DKPPU. However, the management of AMTO currently faces various challenges that need to be addressed, including issues in administration and accessibility. The implementation of this application has proven to be able to improve the quality of AMTO management in a systematic and organized manner, achieving the expected efficiency. This application simplifies the management of schedules, registration, and monitoring and evaluation of education administration. It is hoped that this application can assist Medan Aviation Polytechnic in improving the efficiency and effectiveness of education implementation, and encourage the formation of a modern and innovative "Smart Vocational Training Centre".

Developing a framework for generating production-dependent failure rate through discrete-event simulation

Over the last decades, maintenance has experienced a transition from being a necessary evil to being a pivotal resource to create value for enterprises. Within the process of maintenance planning, distinct decisions could be responsible for different outcomes concerning profit and equipment reliability. Consequently, maintenance optimization has become pivotal to achieving relevant business goals. One of the most popular approaches to conduct maintenance optimization is simulation-based optimization, especially Discrete-Event Simulation (DES). Most works related to DES for maintenance optimization purposes focus on modeling imperfect maintenance or imperfect inspection and prognosis, while failures are often generated through a Weibull distribution. However, failure strongly depends on the production rate or the stress level, defining a Dynamic Non-Homogeneous Poisson Process (DNHPP). To this end, this paper proposes an algorithm for scheduling such DNHPP failure events in a DES framework model and, as a first implementation to apply it, an open-access library capable of generating stress level-dependent failures within the Rockwell ARENA© simulation environment. The developed package, that in the future will be ported to other relevant off-the-shelf simulation environments, provides a more realistic tool for maintenance engineers and researchers to optimize or compare maintenance strategies from an economic perspective.

Анализ состояния инфраструктуры систем сигнализации, централизации и блокировки Мурманского региона Октябрьской железной дороги

: Given the prospects for the future development of interstate transportation in the Northwest region and the positive experience of operating the St. Petersburg — Helsinki line, the article conducts an analysis of the state of signaling, centralization, and blocking systems on the main cross-border railway routes of the Oktyabrskaya Railway. The conducted analysis revealed that there are two most promising cross-border railway sections with direct access to the main railway line and the ports of the Barents and White Seas. From the Finnish side: the section from Alakurtti station to Ruchii Karelskiye station, which can potentially be extended to the nearest Finnish station, Salla (approximately 70-75 km). From the Norwegian side: the section from Nickel-Murmansk station to Kola station, with plans to extend it to the Norwegian station, Kirkenes (approximately 50-60 km). On these sections, there is a need for the modernization of signaling, centralization and blocking systems. Additionally, the article proposes a technical solution for the modernization of these systems, with simultaneous reduction in operational costs for their maintenance. The essence of the solution lies in the utilization of a microprocessor-based centralization system with a distributed control architecture, the principles of coordinate train traffic management, and the application of minimally-manned maintenance technologies for signaling, centralization and blocking devices.

Laboratory proportion design and performance evaluation of Fiber-Reinforced rubber asphalt chip seal based on multiple properties enhancement

Chip seal is an effective preventive maintenance technology for the pavement distress repair and pavement service life extension. Adding fiber or rubber powder alone has been shown to improve chip seal performance. However, the full potential of chip seal could not be attained by utilizing only fiber or rubber powder singularly. In this study, a fiber-reinforced rubber asphalt chip seal was prepared and evaluated based on multiple properties enhancement. Analysis of Variance (ANOVA) was conducted to assess the level of significant influence of rubber powder content and shearing time on the results of the penetration, softening point, ductility, and elastic recovery tests. The optimal process parameters for rubber asphalt were further analyzed and determined based on the ANOVA results. Bending beam rheometer (BBR) and rotational viscosity tests were executed to evaluate the low-temperature crack resistance and workability of the rubber asphalt. Cover, sweep, 45-degree skew shear and texture depth tests were performed to identify the optimum proportion of fiber-reinforced rubber asphalt chip seal. Tensile, temperature stability, water stability and three-point bending tests were conducted to evaluate chip seal performance. Cost analysis for mature preventive maintenance technology was carried out to evaluate equivalent annual costs. The results show that essential parameters for preparing rubber asphalt were the rubber powder content of 22.5% and the shearing time of 60 min. Moreover, the optimum proportion of fiber-reinforced rubber asphalt chip seal was determined to be 9.55 kg/m2 of aggregates, 2.3 kg/m2 of rubber asphalt, and 80 g/m2 of fiber. This study also confirmed that the fiber-reinforced rubber asphalt chip seal outperformed the rubber asphalt chip seal in terms of crack resistance, skid resistance, bonding performance, temperature stability, water stability and fatigue resistance. Combined with cost analysis, it was found that the fiber-reinforced rubber asphalt chip seal was a type of cost-effective preventive maintenance technology.

MEASURING MAINTAINABILITY INDEX BEFORE AND AFTER CODE REFACTORING

Measuring the maintainability index of software is crucial to ensure bettermaintenance and improve quality. Refactoring code is important in improving software quality and increasing maintainability. However,understanding the relationship between refactoring code and the maintainability index is crucial for software developers and maintenance engineers. An assistive software tool called MMIBAR was developed, which provides a set of metrics for calculating the maintainability index of the source code before and after refactoring code that contains cloned programming code. This research contributes to the understanding of the relationship between refactoring code and the maintainability index, and proposes a software tool that can help software developers and maintenance engineers improve software quality and increase maintainability. The research involved the development of the MMIBAR software tool and the application of its metrics to measure the maintainability index of the source code before and after refactoring code that contains cloned programming code. The results of the study demonstrate that refactoring code can significantly improve software quality and increase maintainability. The MMIBAR software tool provides a useful set of metrics for measuring the maintainability index of source code, and can be used to identify areas of code that need further refactoring. The research shows that refactoring code is crucial in improving software quality and increasing maintainability. The MMIBAR software tool can help software developers and maintenance engineers identify areas of code that need further refactoring, and ultimately improve the overall quality of software systems

Practical Improvement and Performance Evaluation of Road Damage Detection Model using Machine Learning

The social infrastructure, including roads and bridges built during period of rapid economic growth in Japan, is now aging, and there is a need to strategically maintain and renew the social infrastructure that is aging. On the other hand, road maintenance in rural areas is facing serious problems such as reduced budgets for maintenance and a shortage of engineers due to the declining birthrate and aging population. Therefore, it is difficult to visually inspect all roads in rural areas by maintenance engineers, and a system to automatically detect road damage is required. This paper reports practical improvements to the road damage model using YOLOv5, an object detection model capable of real-time operation, focusing on road image features

Gas-Dynamic Multiple-Mirror Trap GDMT

This work is devoted to the project of a new-generation open trap, gas-dynamic multiple-mirror trap (GDMT), proposed at the Budker Institute of Nuclear Physics, Siberian Branch, Russian Academy of Sciences. The aim of the project is to substantiate the possibility of using open traps as thermonuclear systems: a source of neutrons and, in the future, a thermonuclear reactor. The main objectives of the project are to develop technologies for long-term plasma maintenance in an open trap, optimize neutron source parameters based on the gas-dynamic trap, and demonstrate methods for improving plasma confinement. The magnetic vacuum system of the facility consists of a central trap, multiple-mirror sections that improve the longitudinal plasma confinement, and expanders designed to accommodate plasma flux absorbers. The facility is to be built in several stages. The starting configuration is broadly similar to the GDT facility and includes a central trap with strong magnetic mirrors and expanders. It solves two main problems: optimization of the parameters of the neutron source based on the gas-dynamic trap and study of the physics of the transition to the configuration of a diamagnetic trap with a high relative pressure β ≈ 1, which significantly increases the efficiency of the system. This work describes the technical design of the starting configuration of the facility and outlines the physical principles on which the GDMT project is based.

Structural evaluation by reverse engineering with 3D laser scanner

AbstractIn the modern era, there has been a surge of interest in researching the integration of high‐tech in construction. Many different technologies are currently being explored as potential solutions for future construction projects. BIM technology using high‐tech equipment is spotlighted as promising for future construction projects. As structures age, there is a growing demand for safety inspections. However, current safety inspection methods are traditional and rely heavily on the subjective opinions of workers, leaving much room for error. To address this issue, there is a need to develop existing maintenance technology. Recently, there has been a significant increase in research on building safety inspections using 3D laser scanners, which use LIDAR technology to allow for precise and rapid acquisition of building information. This technology even allows for reverse engineering through point cloud data modeling. In light of this, a new evaluation system for building stability using 3D technology is proposed in this study, which aims to revolutionize the way that safety inspections are carried out.

Joint Optimization of Maintenance and Spare Parts Inventory Strategies for Emergency Engineering Equipment Considering Demand Priorities

To respond to emergencies in a timely manner, emergency engineering equipment has been an important tool to implement emergency strategies. However, random failures of the equipment may occur during operation. Therefore, appropriate maintenance and spare parts inventory strategies are crucial to ensure the smooth operation of the equipment. Furthermore, the urgency degree of emergencies varies in practice. Nevertheless, existing studies rarely consider the impact of urgency degree and demand priorities on the service order of the equipment. To bridge the research gaps, this paper establishes a joint optimization model of maintenance and spare parts inventory strategies for emergency engineering equipment considering demand priorities. The proposed model includes two types of emergency engineering equipment with different service rates. The more urgent demand can be fulfilled by the equipment with a higher priority. Corrective maintenance and spare parts inventory policies are simultaneously performed for the equipment. The Markov process imbedding method is utilized to derive the probabilistic indexes of the system. To maximize the system availability, the number of maintenance engineers and the spare parts inventory strategy is optimized via the construction of the joint optimization model. The optimal solution for the optimization problem is obtained using the branch-and-bound method. Finally, this study presents practical examples to verify the effectiveness of the model and methods.

A hydrophobic and sustainable anti-icing sand fog seal for asphalt pavement: Its preparation and characterization

Ice can seriously affect the skid resistance of pavement, potentially leading to traffic accidents. To mitigate this problem, this study developed a hydrophobic and sustainable anti-icing sand fog seal (HSSFS) that combines anti-icing functionality with maintenance technology. The HSSFS was prepared using hydrophobic emulsified asphalt (HEA), a thickening agent, corundum, and a sustained-release deicing agent (SRDA). Its composition was determined by single-factor experiments and the Box-Behnken Design (BBD) method, ensuring both stability, anti-icing effects and skid resistance. Subsequently, its anti-icing effect and mechanism, conventional pavement performance, and environmental impact were investigated. Through optimization using single-factor experiments and the BBD design method, it was determined that the optimal composition of the thickening agent, corundum, and SRDA was 26%, 38%, and 36%, respectively. Freezing time, pullout, shear, immersion and freeze–thaw cycle tests indicated that the HSSFS exhibited good anti-icing effects, which can be attributed to the hydrophobic surface and the slow-release deicing agent's capacity. Additionally, pavement function tests indicated that the HSSFS maintained its original functions, such as sealing cracks and pores in the asphalt pavement, thereby reducing water seepage. When the HSSFS spray quantity was 0.6 kg/m2, both the BPN and TD met the specification requirements, indicating good skid resistance for pavement. Furthermore, environmental impact analysis demonstrated that the HSSFS had minimal effects on metals and plants. In conclusion, applying HSSFS to asphalt pavement can provide effective anti-icing effects and preventive maintenance without environmental harm.

Risk Assessment Analysis of Multiple Failure Modes Using the Fuzzy Rough FMECA Method: A Case of FACDG

With the increasing operating mileage and ownership of high-speed electric multiple units (EMU), a reasonable operation and maintenance strategy is the key to ensure their safe and reliable operation. As a key component of recombined EMU, creating a reasonable and effective risk assessment method for the fully automatic coupler draft gear (FACDG) is the first task. Therefore, based on fuzzy rough number theory, combined with the analytic hierarchy process (AHP), entropy weight method (EWM), technique for order performance by similarity to ideal solution (TOPSIS) and grey relational analysis (GRA), a risk priority indicator of comprehensive nearness degree is developed. Furthermore, a novel multi-criteria decision making (MCDM) failure modes, effects and criticality analysis (FMECA) assessment method is proposed. The effectiveness and rationality of the risk assessment method proposed are verified by the analysis of data and failure modes of a certain FACDG at fourth-level engineering maintenance.

Exploration of mechanical engineering equipment management and maintenance strategies

The management and maintenance of mechanical engineering equipment is critical to the proper operation and productivity of a business. The purpose of this paper is to discuss strategies for the management and maintenance of mechanical engineering equipment, including aspects such as preventive maintenance, breakdown repair and performance monitoring. Through reasonable management and maintenance methods, the reliability and extended service life of equipment can be improved, failure rates and maintenance costs can be reduced, production efficiency can be improved and sustainable development of enterprises can be supported.

GMDNet: An Irregular Pavement Crack Segmentation Method Based on Multi-Scale Convolutional Attention Aggregation

Pavement cracks are the primary type of distress that cause road damage, and deep-learning-based pavement crack segmentation is a critical technology for current pavement maintenance and management. To address the issues of segmentation discontinuity and poor performance in the segmentation of irregular cracks faced by current semantic segmentation models, this paper proposes an irregular pavement crack segmentation method based on multi-scale convolutional attention aggregation. In this approach, GhostNet is first introduced as the model backbone network for reducing parameter count, with dynamic convolution enhancing GhostNet’s feature extraction capability. Next, a multi-scale convolutional attention aggregation module is proposed to cause the model to focus more on crack features and thus improve the segmentation effect on irregular cracks. Finally, a progressive up-sampling structure is used to enrich the feature information by gradually fusing feature maps of different depths to enhance the continuity of segmentation results. The experimental results on the HGCrack dataset show that GMDNet has a lighter model structure and higher segmentation accuracy than the mainstream semantic segmentation algorithms, achieving 75.16% of MIoU and 84.43% of F1 score, with only 7.67 M parameters. Therefore, the GMDNet proposed in this paper can accurately and efficiently segment irregular cracks on pavements that are more suitable for pavement crack segmentation scenarios in practical applications.

Intelligent Monitoring and Maintenance Technology for Next-Generation Power Electronic Systems

Next generation power converters are expected to be reduced in size, weight and cost while providing highly reliable operation over an extended lifetime with minimal ecological footprint. This work investigates how intelligent monitoring and maintenance technologies (IMMs) play a key role in realizing these typically conflicting objectives. It uniquely analyzes the economic and ecological potential, i.e. the increased average operational lifetime of converters, that can be realized via intelligent maintenance using extracted state-of-health data. Consequently, newest monitoring and maintenance technologies that address thermally induced stress and strain in power modules are discussed and categorized regarding their level of complexity and cost of implementation. This facilitates finding the necessary implementation effort and cost to achieve the defined reliability and lifetime requirements of specific applications. Finally, this work reviews the impact of artificial intelligence and cloud technologies on increasing the effectiveness of maintenance technology. Both technologies reveal a path to new business opportunities, such as “power-as-a-service”, and have the potential to transform the “hardware-oriented” power converter industry as we know it into a “data-driven” industry.

Analytical and numerical study of rectangular reinforced concrete beam using robot structural analysis software

This simulation is significant in providing value for the fields of civil engineering, architecture and structure maintenance. While the studies of reinforcement concrete beam focus on the bending moment, shear force deflection and cracks, this has demonstrated its importance in ensuring the stability of structures. This study aims at delimiting and comparing the analytical and numerical results examined through Robot Structural Analysis software. That is, it is applied to investigate the rectangular reinforced concrete beam by uniformly load in order to effectively, intuitively, rapidly and simply modify and deal with any physical and geometrical parameter by means of this software. The use of this software can ensure non-linear analyses and take into account plastic behaviors of materials as well as big deformation and instability of structure element. The numerical results are compared with the analytical results in the middle of the span. It is found that the maximum bending moment and the maximum shear force have the same values in the case of a live load, but in the case of the combination load, there is a small difference. As regards the maximum deflection, there is a small difference which is linear for the load range. As for cracks, it is observed that the width of cracks is in accordance with Eurocode 2 standards and the approximative results are found experimentally in comparison with other studies.

E-Defense Shake Table Experiments Implemented by NIED and Collaborative Research Projects in 2005–2022

The three-dimensional full-scale earthquake testing facility, “E-Defense” was constructed using the 1995 Great Hanshin-Awaji Earthquake as a lesson. Until October 2022, this facility has conducted 123 experiments as part of the research and development on earthquake engineering for various purposes (e.g., the clarification of destruction processes, evaluations of seismic structural performances, functional maintenance, collapse margins, and damage control technologies). Even after its commencement, large earthquakes that significantly affected human life and socioeconomic activities occurred such as the 2011 off the Pacific coast of Tohoku Earthquake caused the Great East Japan Earthquake Disaster. These earthquakes revealed some new challenges for disaster prevention and mitigation. Such issues have also been incorporated as subjects to be addressed by the facility. This article introduces the researches and developments using E-Defense, chronologically linking them with the major earthquakes occurred in Japan after 1995. By summarizing the activities, its near-future direction is also briefly indicated.

Shale oil and gas exploitation in China: Technical comparison with US and development suggestions

The shale oil and gas exploitation in China is technically benchmarked with the United States in terms of development philosophy, reservoir stimulation treatment, fracturing parameters, fracturing equipment and materials, oil/gas production technology, and data/achievements sharing. It is recognized that the shale oil and gas exploitation in China is weak in seven aspects: understanding of flow regimes, producing of oil/gas reserves, monitoring of complex fractures, repeated stimulation technology, oil/gas production technology, casing deformation prevention technology, and wellbore maintenance technology. Combined with the geological and engineering factors of shale oil and gas in China, the development suggestions of four projects are proposed from the macro- and micro-perspective, namely, basic innovation project, exploitation technology project, oil/gas production stabilization project, and supporting efficiency-improvement project, so as to promote the rapid, efficient, stable, green and extensive development of shale oil and gas industry chain and innovation chain and ultimately achieve the goal of “oil volume stabilizing and gas volume increasing”.

Design of Information System for Whole Life Cycle Management of Medical Equipment

In order to record the whole life process information of medical equipment and improve the management efficiency, this study proposes an informatization scheme for the whole life cycle management of medical equipment. Relying on cloud-native, based on the software architecture of front-end and back-end separation, a mode of sub-account and sub-authority management of medical equipment was designed to integrate maintenance work orders, operation reports and other functions, and manage medical equipment data in a structured and platform-based manner. Comparing the equipment failure rate, maintenance response time, and average inventory time per device before and after the system operation, the differences are statistically significant (P<0.05). The system can realize the work collaboration of maintenance engineers, equipment users, and equipment management personnel, and can trace the information of the whole life cycle of the equipment.

IoT-based data-driven predictive maintenance relying on fuzzy system and artificial neural networks

Industry 4.0 technologies need to plan reactive and Preventive Maintenance (PM) strategies for their production lines. This applied research study aims to employ the Predictive Maintenance (PdM) technology with advanced automation technologies to counter all expected maintenance problems. Moreover, the deep learning based AI is employed to interpret the alarming patterns into real faults by which the system minimizes the human based fault recognition errors. The Sensors Information Modeling (SIM) and the Internet of Things (IoT) have the potential to improve the efficiency of industrial production machines maintenance management. This research work provides a better maintenance strategy by utilizing a data-driven predictive maintenance planning framework based on our proposed SIM and IoT technologies. To verify the feasibility of our approach, the proposed framework is applied practically on a corrugated cardboard production factory in real industrial environment. The Fuzzy Logic System (FLS) is utilized to achieve the AI based PM while the Deep Learning (DL) is applied for the alarming and fault diagnosis in case the fault already occured.

A Data Mining Framework for Maintenance Prediction of Faulty Products Under Warranty

Predicting the sequential patterns of maintenance activities (replacement or repair) with the needed spare parts for faulty products becomes the main challenge to maintenance engineers. This research, therefore, develops a data mining framework for predicting the integrated sequential patterns of maintenance activities and identifying the classification of the frequent components’ spare parts for faulty products under warranty. In this framework, a large data set was mined for products under warranty including product attributes, maintenance activities, and spare parts. Then, data mining techniques were performed to determine the frequent sequence pattern of maintenance activities, involving: the selection of monthly maintenance activities, generalized sequential patterns (GSP), generation of association rules, and rule-based classification with/without considering product attributes. The frequent sequential patterns were validated using testing data. Further, the GSP was applied to determine the frequent sequential patterns of spare parts. Finally, integrated sequential patterns were generated for maintenance activities and spare parts. A case study of water-cooled chiller products was deployed to illustrate the developed framework. The effectiveness of the framework was illustrated with the warranty data mining for a water-cooled chiller. In conclusion, the proposed framework allows maintenance engineers to extract hidden knowledge regarding sequential patterns of maintenance planning and provides valuable information for maintenance prediction.