Maintenance Engineers Research Articles

Synergistic effect of artificial intelligence and new real-time disassembly sensors: Overcoming limitations and expanding application scope

Abstract With the acceleration of industrialization and the increase in the complexity of equipment, it is particularly important to accurately predict and effectively maintain equipment failures. In response to the problem of difficulty in accurately achieving predictive maintenance and the severely limited application range of new real-time disassembly sensors, this article conducts research on the synergistic effect of the new real-time disassembly sensors and artificial intelligence technology. First, relevant parameter data of a certain enterprise’s generator can be collected on-site, and the Pearson correlation coefficient method can be used to calculate the correlation between the generator data parameters and the target fault type, ensuring the degree of correlation in extracting data features. Then, based on the gated recurrent unit (GRU) model, the article applied the particle swarm optimization (PSO) algorithm to optimize the parameters of the GRU network and used the support vector machine (SVM) model to optimize the classification function of the network output. Finally, the optimized GRU model can be applied to predict the fault types of generators, and based on this, it can be applied to the energy industry, agriculture, medical and health, and transportation industries to verify the application scalability of artificial intelligence and new real-time disassembly sensors. The experimental results show that the optimized GRU model combined with the new real-time disassembly sensor achieved an average accuracy of 0.94 in generator fault prediction, with a cost loss rate of only 3.03%, a decrease of 5.91% compared to the single new real-time disassembly sensor. The combination of artificial intelligence technology for precise predictive maintenance of generators greatly reduces maintenance costs and overcomes the limitations of individual real-time sensor disassembly. This has to some extent expanded the application scope and promoted the intelligent development of various industries.

Super-resolution Crack Image Generation for Fixed PTZ-Camera Based Highway Pavement Crack Recognition

Abstract Among the various diseases that inevitably occur on expressways, cracks are the most common and are significant indicators of highway pavement damage. Timely and accurate crack recognition is urgently required for highway maintenance. Highway pavement crack recognition depends mainly on human visual inspection and expressway maintenance vehicles. These approaches are very time-consuming, labor-intensive, and difficult to implement in civil engineering. Cost-effective fixed PTZ (pan/tilt/zoom)-camera based crack recognition was investigated in our previous work. However, for pavement cracks captured at long camera distances, the limited resolution of the PTZ-vision results in low-resolution crack images. In addition, the cracks are weak features, such that the crack pixels density and distribution are significantly affected by the background noise, making it difficult to recognize these cracks. Aiming at to solve these problems, a high-order kernel based modified bicubic interpolation is proposed to typically reveal and characterize discrete pixel variations, obtaining high-quality super-resolution crack images, and improving the recognition performance of cracks. Extensive experiments in relation to crack datasets captured by PTZ-cameras on G4/highways in China are conducted to verify the performance of the proposed method. Two measurement parameters Just Noticeable Blue (JNB) and Structural Similarity Index (SSIM) confirm the high-quality of the super-resolution crack images. Experimental comparisons demonstrate that super-resolution crack images based crack recognition achieves out-performance, such that the mAP, precision (P), recall (R), and F1-score are increased to $95.3\%, 97.3\%, 96.1\%$, and $97.4\%$, respectively. This method proves the feasibility of high-efficiency crack recognition using modified bicubic interpolation for fixed PTZ-vision based expressways maintenance engineering.

Issues of modern maintenance of feed industry equipment

The article examines the evolution of maintenance methods in the feed industry, in particular, the transition from reactive to proactive strategies. It examines the implementation of preventive maintenance technologies, equipment condition-based monitoring, and data-driven decision-making to optimize equipment uptime, minimize downtime, and improve product quality. The role of automation, remote diagnostics, and advanced training programs for maintenance personnel is also discussed.

Comparative Study of Device Lifecycles in Different Healthcare Settings

This research targets the performances and lifetime of medical devices with regard to preventive maintenance (PM) practices in large and small health settings. A new maintenance effectiveness metric (MEM) has been developed and applied in this research for quantifying effectiveness due to PM activities using a synthetic data set. Descriptive and comparative analyses can show that larger hospitals tend to have higher values of MEM and longer lifespans for devices, probably because of more frequent and organized schedules for maintenance. On the other hand, clinics tend to present lower MEM values and longer device downtime, which may indicate a lack of proper practices for maintenance. Regression analysis indicates that the major factors contributing to the longevity of a device are PM schedules and duration, while the number of repairs and the amount of device usage rate are minor. The inference that seems to emerge is that, in many cases, clinics can be helped more by a more regular and predictive approach, taking proper maintenance strategies like periodic training for staff and resource allocation. The study emphasizes the fact that effective PM practices are of high relevance for the performance and operational efficiency of devices within healthcare facilities. Future recommendations proposed for this study included continuing to research advanced predictive maintenance technologies and performing, based on real-world data, a cost-benefit analysis of enhanced PM protocols.

Construction characteristics and reinforcement and maintenance technology of native earth buildings in Liling area

ABSTRACT Traditional earth dwellings in Weishan Village, Liling City, Hunan Province, China, have experienced hundreds of years of wind and rain. In order to asses their seismic performance, a field survey was conducted on representative traditional rammed earth buildings in Liling Kiln area of Weishan Village. The research uses a variety of methods, including on-site investigation, 3D laser scanner tilt analysis, deformation chromatography analysis, wood material detection, and stress wave analysis, to obtain the main structural forms and material characteristics of local earth buildings. The main structural problems of rammed earth buildings were summarized through the investigation: the foundation and roof were no longer stable, the problems of sag, tilt and cracking were common in rammed earth walls, and the wood parts were gradually rotting and splitting. According to the investigation results, the seismic measures to improve rammed earth buildings were proposed from different parts of the damaged houses, in order to provide guidance for the local villagers’ residential construction, house reinforcement and renovation design.

Overview of Offshore Wind Power Technologies

Optimizing offshore wind power technology and reducing the levelized cost of electricity throughout the lifecycle are key measures for the large-scale development of offshore wind power, contributing significantly to the transition toward sustainable energy systems. However, compared to onshore wind power, the internal flow dynamics of offshore wind farms are more complex, which poses challenges for operation and maintenance. Therefore, there is an urgent need for updated, smarter, more efficient, and economic offshore intelligent operation control technologies to facilitate the large-scale development and utilization of offshore wind power. This paper approaches the topic from two perspectives, offshore wind turbines and offshore wind farms, introducing popular research directions and technical bottlenecks in these two related fields. This includes offshore wind turbine capacity development and fundamental technologies, offshore wind power forecasting technology, and offshore wind power operation and control technology, offshore intelligent operation and maintenance technology, as well as offshore wind power and integrated marine area utilization technology. Firstly, the challenges faced by the intensive development of offshore wind resources and operational environments are analyzed. Secondly, the challenges encountered in the aforementioned technological areas and their potential solutions are summarized. Finally, a systematic reflection and outlook on the large-scale development of offshore wind power are provided, reinforcing its critical role in achieving global sustainability goals.

Transnational learning and teaching activities: Smart Healthcare Engineering.

The new maintenance engineers become self-taught and learn from daily dedication, from solving repetitive problems, from exchanging experiences with a colleague, from reading a recommended book and little else. Their own experience has been the main reference for maintenance professionals who have not given up and have become experts. And within the maintenance activities, sanitary engineering plays a very important role. Through the Smart Healthcare Engineering project we have facilitated a close connection between 3 universities and 3 hospitals from 3 countries (Spain, Turkey and Romania), which benefit from each other's experience, transferring the know-how available in a single country. The study involves 7 students from each university, meaning that 21 students have worked and studied together for 2 weeks in Spain in the field of healthcare engineering. The chosen hospital was Dr. Negrin Hospital from Gran Canaria. Results at the organizational level include the modernization of content and the provision of resources for healthcare engineering training, the introduction of innovative educational concepts in healthcare engineering and the integration of blended learning methodologies in practical teaching and training activities. A tighter connection and transnational collaboration were established between European hospitals and universities in healthcare engineering, together with unrestricted access to routinely updated information. The specific results for each participant indicated their capacity to engage in the intricate processes of repair and maintenance within a practical context, making real-time judgments on-site.

Quantification of lifecycle costs for porous asphalt life-extension maintenance methods under managerial uncertainties

ABSTRACT The objective of this research was to evaluate the lifecycle costs associated with emerging pavement maintenance technologies, namely, in-situ rejuvenation and very open emulsion asphalt concrete (ZOEAB+), and scrutinise their suitability over corrective resurfacing maintenance using a stochastic approach. A rational lifecycle inventory was developed by conducting interviews and questionnaire surveys with experts and referring to standard guidelines and international databases. The net present value (NPV) was found sensitive to 12 different inputs with traffic growth rate and discount rate causing the highest uncertainty followed by gasoline and diesel prices. Monte Carlo simulations suggested that the median uncertainty in NPV by using in-situ rejuvenation and ZOEAB+ was 13% and 4% lower than resurfacing. It is envisioned that the research outcomes will assist decision-makers in understanding the uncertainties and costs associated with different maintenance alternatives in the early stages of the project to foster procurement of sustainable and circular pavement maintenance strategies.

Approach Towards the Development of Digital Twin for Structural Health Monitoring of Civil Infrastructure: A Comprehensive Review

Civil infrastructure assets’ contribution to countries’ economic growth is significantly increasing due to the rapid population growth and demands for public services. These civil infrastructures, including roads, bridges, railways, tunnels, dams, residential complexes, and commercial buildings, experience significant deterioration from the surrounding harsh environment. Traditional methods of visual inspection and non-destructive tests are generally undertaken to monitor and evaluate the structural health of the infrastructure. However, these methods lack reliability due to the need for instrumentation calibration and reliance on subjective visual judgments. Digital twin (DT) technology digitally replicates existing infrastructure, offering significant potential for real-time intelligent monitoring and assessment of structural health. This study reviews the existing applications of DTs across various sectors. It proposes an approach for developing DT applications in civil infrastructure, including using the Internet of Things, data acquisition, and modelling, together with the platform requirements and challenges that may be confronted during DT development. This comprehensive review is a state-of-the-art review of advancements and challenges in DT technology for intelligent monitoring and maintenance of civil infrastructure.

Building Cloud Skills: Teaching Innovation and Practice of Automated Operation and Maintenance Courses for Cloud Computing Majors in Higher Vocational Colleges

In the rapid development of cloud computing technology, automated operation and maintenance as its core support is crucial to ensure the efficient and stable operation of cloud services. Given this, higher vocational education must pay attention to the training of automated operation and maintenance skills in the curriculum setting of cloud computing majors. Aiming at the problems existing in automated operation and maintenance teaching in higher vocational colleges, this paper puts forward a series of innovative teaching methods to improve students’ mastery of automated operation and maintenance technology. This paper first analyzes the development trend of cloud computing and automated operation and maintenance technology, clarifies the necessity for vocational students to master automated operation and maintenance skills, and then introduces innovative teaching strategies including case-driven, project-oriented, flipped classroom, virtual simulation experiment, and school-enterprise cooperation in detail. These methods aim to improve the interaction of teaching and students’ practical operation ability through the close combination of practice and theory. Finally, this paper proposes a mechanism to evaluate and feedback on these innovative teaching methods to ensure continuous improvement and optimization of teaching activities. Through the implementation of these innovative teaching methods, it can be expected that students will better adapt to the technical requirements of the cloud computing era and lay a solid foundation for their future careers.

A System Dynamics Stability Model for Discrete Production Ramp-Up

Manufacturing companies are increasingly challenged to deliver customizable products with shorter time to market and higher quality while adhering to sustainability requirements. To meet these challenges, the frequency and importance of production ramp-ups will increase in the future. However, most ramp-ups still fail to meet targets due to unpredictable equipment failures, operator errors, and system complexity. We propose a system dynamics model that captures the unique dynamics of ramp-up phases by integrating stability and disturbance factors that influence the key performance indicators overall equipment effectiveness, process capability, and production output. A systematic literature review informed the identification of stability factors, which were validated through expert interviews in the automotive industry. Our system dynamic simulation results indicate that control factors realistically influence production system behaviour during different ramp-up phases. Despite some limitations regarding the effects of maintenance personnel and engineering changes on key performance indicators, our model effectively simulates realistic ramp-up behaviour. The findings highlight the need for tailored models that consider specific ramp-up contexts and emphasize the importance of data acquisition for enhanced performance prognosis in future research.

Nonlinear interaction and compounding factors of vehicle parameters influencing exhaust pollution.

One of the main causes of air pollution, particularly in large cities, is vehicles due to it continued use of hydrocarbon fuels. The understanding of nonlinear interactions of vehicle parameters uncovers more realistic relationships for enhancing formulation of strategies to address vehicle-related pollution. Thus, the study aims to evaluate the interaction and quadratic effect of vehicle parameters on Hydrocarbon (HC), Carbon dioxide (CO2), Carbon monoxide (CO), and Nitrogen oxide (NOx) emissions. The SV-5Q Vehicle Exhaust Gas Analyzer was used to collect emission concentrations data from one thousand and two (1002) light-duty petrol vehicles at three (3) government-accredited vehicle inspection sites in Accra, Ghana. Pollution control devices, maintenance frequency, and vehicle age were also collected. The linear regression analysis revealed that vehicle age showed a positive linear relationship with CO emissions. Maintenance frequency, on the other hand, demonstrated a negative linear relationship with both CO and HC emissions. The interaction between vehicle age and maintenance frequency positively impacted CO and HC emissions, while the interaction between vehicle age and emission technology had a negative effect on CO. Additionally, the combined effect of frequency of maintenance and emission technology significantly reduced CO emissions but increased HC emissions. Notably, the quadratic effect of vehicle age positively influenced CO emissions. Similarly, CO, HC, and NOx emissions were positively correlated with the squared effect of emission technology. Stricter emissions standards, encouraging frequent maintenance and testing of vehicular exhaust emissions, and doing away with over-aged vehicles are recommended to control and reduce vehicular exhaust emissions.

An intelligent predictive maintenance system based on random forest for addressing industrial conveyor belt challenges

This study introduces a sophisticated intelligent predictive maintenance system for industrial conveyor belts powered by a random forest machine learning model. The random forest model was evaluated against established models such as logistic regression, neural networks, decision trees, and gradient boosting, demonstrating superior performance. The model achieved 100% accuracy in classifying gearbox lubricant levels and sprocket conditions, highlighting its potential for addressing critical challenges in predictive maintenance, such as avoiding unexpected downtime. However, further validation with larger datasets and varied operational environments is recommended to confirm robustness. This performance highlights its effectiveness in multiclass fault detection and overfitting mitigation, establishing a new standard in predictive maintenance technology. The system, enhanced by a comprehensive sensor array, not only adeptly captures but also intelligently analyzes critical operational data, providing proactive and data-driven insights for maintenance decision-making. This study not only affirms the dominance of the random forest model in predictive analytics but also underscores its pivotal role in optimizing maintenance strategies, enhancing operational efficiency, and ensuring the reliability of conveyor systems in industrial settings.

Opportunities and Risks of Artificial Intelligence for Industry 5.0 in the context of Reliability and Maintenance Engineering

Opportunities and Risks of Artificial Intelligence for Industry 5.0 in the context of Reliability and Maintenance Engineering

Life Time Prediction of an Electromagnet Relay using Clustering based Principal Component Analysis with Hybrid Deep Learning Model

The estimation of the electromagnet relay remaining useful life is highly crucial to maintain reliability and avoid unscheduled breakdowns in various applications. The objective of this research work will be to design a model with much higher precision and efficiency utilizing PCA coupled with a hybrid deep learning architecture of Bi-LSTM along with Bi-GRU. The C-MAPSS dataset was of reduced dimensionality, since PCA has been applied to eliminate data redundancy while retaining crucial characteristics, and then K-means clustering is applied to classify the data; afterwards, the Bi-LSTM and Bi-GRU models are implemented for RUL relay prediction. The proposed method in comparison with typical deep learning models has a Mean Absolute Error of 0.021 and an R² of 0.996. Results developed reflect how the model can produce some very powerful prediction, however; what it really shows is great potential for this approach with respect to predictive maintenance of electromagnet relays. PCA may well amalgamate with Bi-LSTM and Bi-GRU models to achieve great scalability according to the maintenance engineering, which offers practical applications in improving the lifetime of the electromagnet relays.

Research on the Stress Characteristics of Reuse of Semi-Rigid Base.

Semi-rigid bases are widely used in road construction due to their excellent properties, high rigidity, and frost resistance, and they have been in service for many years. However, as the service life increases, the maintenance demands also grow, with traditional maintenance methods still being the primary approach. Based on a typical case using ground-penetrating radar (GPR) technology, this study explores the issue of cracks in semi-rigid bases and their impact on overlay layers. The findings indicate that the overlay layer at semi-rigid base cracks struggles to withstand significant tensile and shear stresses, leading to reflective cracking and reducing pavement durability. To address this problem, this paper investigates the application potential of crushing technology in maintaining semi-rigid bases. Crushing technology has been widely employed in the maintenance of cement concrete panels, effectively eliminating reflective cracks and extending the service life of overlays. Given that semi-rigid bases share similar high-strength characteristics with cement concrete panels, crushing technology shows considerable applicability in semi-rigid base maintenance. This study employs a finite element analysis method to establish a semi-rigid base model under the impact load of multi-hammer equipment. It examines its dynamic mechanical response and evaluates the feasibility and effectiveness of crushing technology for semi-rigid base maintenance. Additionally, this study investigates the influence of the crushed layer's modulus and thickness on key mechanical design indicators of the overlay and proposes recommendations for optimal design parameters. The research results provide valuable references for the design of the thickness and modulus in maintaining and repairing semi-rigid bases, contributing to improving pavement performance and durability.

Maintenance in aviation enabled by Industry 4.0 technologies: exploring the current research trends using a topic modeling approach

PurposeSince passenger safety is critical, aviation maintenance is essential. Aviation maintenance management is changing due to Industry 4.0 (I4.0). According to earlier research, I4.0 technologies improve aircraft manufacturing efficiency and responsiveness through automation, predictive maintenance and process self-optimization. Thus, this study examines I4.0 research and aircraft maintenance's potential interaction.Design/methodology/approachUsing a text-mining methodology, this paper looks at the state of the art in aviation maintenance research in the I4.0 era. We used the topic modeling approach and Latent Dirichlet Allocation (LDA) technique to analyze the abstracts and indexed keywords of 929 research articles on the intersection of aviation maintenance and I4.0, subsequently clustering them into eight topics.FindingsWe have mapped out the emerging research trends at the intersection of “aviation maintenance” and “I4.0 technologies”, and presented suggestions for theoretical frameworks, applied frameworks and future lines of inquiry. This paper makes a theoretical contribution to the systematization of literature on I4.0 technologies in aviation maintenance. It provides valuable insight for managers by exploring the implications and opportunities that arise in light of recent innovations brought by I4.0 in aviation maintenance.Originality/valueThis study focuses on the use of Industry 4.0 technologies in aircraft maintenance processes, contributing to the growing research on digital technology in maintenance and maintenance, repair and overhaul (MRO). Furthermore, the study's analysis of the LDA topic model provides valuable insights for future research on using I4.0 technologies to investigate specific areas of application in the context of digital maintenance.

Assessing digitalization maturity in port environments: a comprehensive tool for logistics, operations, and planning management

ABSTRACT This research presents a tool for assessing the digitalisation maturity of ports, focusing on logistics, operations, and planning and management. Through a systematic literature review and the MethodiOrdinatio methodology, a theoretical model was developed to evaluate digital maturity in port environments. The review revealed critical technologies in port operations, leading to the creation of a maturity assessment tool. This tool was validated by domain experts and applied to a major Brazilian port. The assessment showed that the port achieved a maturity level of 1, indicating high proficiency in operations, especially in technology for maintenance. Key implementations included advanced cybersecurity measures and effective integration of systems for gate management, scheduling, and traffic control. The study underscores the tool’s practicality and efficiency, with findings confirming its capability to enhance port digitalisation strategies.

Three benefits of “Caution (Yellow Tag)” in SHM-driven condition assessment of buildings: Eight years experience with market-based SHM

In Japan, structural health monitoring (SHM) for building structures has received increased attention since the 2011 Tohoku earthquake, particularly regarding business continuity and functional recovery. Most applications are market-driven, and building owners deploy an SHM system at their own expense so that they can promptly assess the condition of their buildings as one of the tags denoted “Green,”“Yellow,” or “Red.” In 2015, the authors’ group began installing such an SHM system, and as of December 2023, 553 privately owned buildings have been equipped with this SHM system. This opinion paper presents the authors’ experiences to date on monitoring, utility of data, and interaction with building owners. It conveys three opinions that the authors believe to be most interesting, particularly in the context of the benefits of the “Caution (Yellow)” tag. The article briefly introduces SHM, followed by the specific system in question and the data accumulated over the years in Japan. The first opinion is that the damage criteria, that is, the boundaries among the three tags, should not be considered permanent but subject to updates based on experience and newly acquired data. The second opinion is that SHM is one of the best means to characterize the damage (and fragility) of major nonstructural elements, while “Caution (Yellow)” has inherent relevance to the nonstructural damage, particularly for initial damage states. The third opinion is that “Caution (Yellow)” is a good message for building owners and managers with a solid contingency plan that includes readily available building maintenance workers and engineers. On the contrary, it tends to frighten building owners and managers when such a contingency plan is absent, discouraging them from installing SHM. Overall, it presents some valuable insights of interest to those wishing to further such SHM systems on a broader scale.

РОЗРОБКА ТА ВИРОБНИЦТВО ЛЕГКИХ ЛІТАЛЬНИХ АПАРАТІВ СІЛЬСЬКОГОСПОДАРСЬКОГО ПРИЗНАЧЕННЯ

Issues of protecting plants from diseases and pests are becoming more and more urgent. Climate change causes an increase in the activity of weedy plants and insects harmful to agriculture. Because of this, the chemical treatment of fields by aviation in modern agriculture should serve the purpose of preserving the future harvest in both a qualitative and quantitative sense. An important role in this is played by the biological method of plant protection, which is quite effective and environmentally friendly. It is based on the use of living organisms, their waste products and biologically active substances that regulate the development and reproduction of harmful plant organisms. Scientists have developed technologies for the breeding of such zoophages to protect plants from plant pests: Trichogramma, predatory mite Phytoseiulus, encarzia, Aphidimyza gallica, etc., as well as the production of microbiological preparations to protect plants from diseases and pests, the high efficiency of their use has been proven. Aircraft (planes, helicopters, ultralight aircraft) that are registered in the State Register of Civil Aviation Aircraft of Ukraine and have a certificate for the right to perform aerochemical work are allowed to carry out aerial work on the application of pesticides during agrochemical activities on agricultural and forest crops. The requirements for the characteristics of the aircraft, directive requirements for the technology of production, operation and maintenance of airworthiness were formulated. The evaluation of the manufacturability of the considered products (agricultural LA) cannot be evaluated without taking into account the operational manufacturability, which largely determines the costs when performing work in the field. Along with the requirements for the manufacturability of the aircraft design, the so-called operational requirements are of great importance. These requirements determine the suitability of the structure and equipment for its maintenance and repair during the operation of the aircraft, taking into account the high quality of work, the least consumption of time, labor and materials. Satisfying operational requirements ensures reliability, durability of the aircraft, high labor productivity during maintenance, reduction of operational costs and efficiency of use of the aircraft fleet. The appearance of the trichogram required the development of special means for its introduction. An attempt to use existing means showed their unsuitability due to their low functional efficiency and high cost of work. The task was solved by increasing the manufacturability of agricultural aircraft during their production and operation. This made it possible to organize its serial production and operation in many farms of the country.