Effective Maintenance Plan Research Articles

Enhancing pavement crack segmentation via semantic diffusion synthesis model for strategic road assessment

Computer-aided deep learning has significantly advanced road crack segmentation. However, supervised models face challenges due to limited annotated images. There is also a lack of emphasis on deriving pavement condition indices from predicted masks. This article introduces a novel semantic diffusion synthesis model that creates synthetic crack images from segmentation masks. The model is optimized in terms of architectural complexity, noise schedules, and condition scaling. The optimal architecture outperforms state-of-the-art semantic synthesis models across multiple benchmark datasets, demonstrating superior image quality assessment metrics. The synthetic frames augment these datasets, resulting in segmentation models with significantly improved efficiency. This approach enhances results without extensive data collection or annotation, addressing a key challenge in engineering. Finally, a refined pavement condition index has been developed for automated end-to-end defect detection systems, promoting more effective maintenance planning.

The Influence of Seasonal Effects on Railway Vertical Track Modulus

Adequate vertical track support is essential for safe and efficient railway operations. Insufficient support leads to distorted track geometry, increased dynamic loads, component stress, poor ride quality, rolling stock damage, and derailment risks. Current inspection practices focus on assessing the condition of the track components and geometry, rather than the root causes of degradation. To improve this condition, this study presents the use of a methodology that utilizes an autonomous vertical track deflection measurement system mounted on a loaded rail car (36 tonnes/axle) to support track maintenance decisions in a heavy haul railroad located in southeast Brazil. The system continuously measured substructure stiffness along the railway line. Over one year, data were collected from over 8000 km of track. The study highlighted seasonal effects on track degradation over time, identifying areas with significant deflections and high deflection rates, which contribute to issues such as differential settlement and reduced lifespan of track components. Additionally, the study revealed seasonal effects, with deflections peaking during wet weather and decreasing during dry cycles. A method to classify weak track areas was developed, facilitating monitoring and enabling more effective maintenance planning, contributing to the reduction of overall track maintenance costs and enhancing safety and operational efficiency.

System-level Probabilistic Remaining Useful Life Prognostics and Predictive Inspection Planning for Wind Turbines

Wind energy plays a crucial role in the energy transition. However, it is often seen as an unreliable source of energy, with many production peaks and lows. Some of the drivers of uncertainty in energy production are the unexpected wind turbine (WT) failures and associated unscheduled maintenance. To support an effective health management and maintenance planning of WTs, we propose an integrated data-driven framework for Remaining Useful Life (RUL) prognostics and inspection planning of WTs. We propose a Long-short term memory (LSTM) neural network with Monte Carlo dropout to estimate the distribution of the RUL of WTs, i.e. we develop probabilistic prognostics. Different from existing studies focused on prognostics for single components, we consider the simultaneous health-monitoring of multiple components of the WTs, thus seeing the turbine as an integrated system. The obtained prognostics are further included into a stochastic planning model which determines optimal moments for inspections. For this, we pose the problem of WT inspections as a renewal reward process. We illustrate our framework for four offshore WTs which are continuously monitored by Supervisory Control and Data Acquisition (SCADA) systems. The results show that LSTMs are able to estimate well the RUL of the WTs, even in the early phase of their usage. We also show that the prognostics are informative for maintenance planning and are conducive to conservative inspections.

Utilizing Electrochemical Techniques for Assessing the Probability of Concrete Resistivity and Corrosion Potential in Reinforced Concrete Structures

Corrosion of reinforcing steel is a major durability issue for reinforced concrete structures exposed to aggressive environments. Early assessment of corrosion probability is important for effective maintenance planning. This study investigates the application of non-destructive electrochemical methods like concrete resistivity and half-cell potential measurement, along with integrated assessment and advanced techniques, for evaluating corrosion risk in reinforced concrete. Concrete specimens with and without Albizia amara resin coating were subjected to accelerated corrosion testing. Concrete resistivity and half-cell potential values showed an inverse correlation, with lower resistivity indicating higher corrosion risk. Resistivity and potential results for the coated samples exhibited intermediate values compared to the control and corroded samples, demonstrating the resin's partial passivating effects. Rebar diameter measurements before and after exposure confirmed corrosion led to reductions in diameter and cross-sectional area, which coating mitigated. Statistical analysis validated the significant impacts of original bar diameter and corrosion level on subsequent geometry changes. Mechanical testing of reinforcing steel bars found the control samples had the highest strength and ductility, while the coated bars exhibited properties closer to the controls than the corroded bars. The protective capabilities of the bio-based Albizia amara resin coating were validated through multipoint monitoring of material durability parameters. Overall, results demonstrated the effectiveness of integrated electrochemical testing with non-destructive techniques for comprehensive condition assessment and corrosion risk evaluation in reinforced concrete structures

Advancing predictive maintenance for gas turbines: An intelligent monitoring approach with ANFIS, LSTM, and reliability analysis

Gas turbine malfunctions can significantly impact production and safety. This study proposes an intelligent monitoring system for MS5002C gas turbines using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Long Short-Term Memory (LSTM) algorithms for real-time anomaly detection and predictive maintenance. Based on extensive historical data (1985–2021), the system predicts component degradation and calculates failure probabilities. This enables the development of an effective preventive maintenance plan, extending turbine life and optimizing performance.

Designing a Reliability Maintenance Plan for Refinery Plants: A Case Study of a Nigerian-Based Refinery and its Power Systems

This study developed a comprehensive and effective reliability maintenance plan for refinery plants. Using a Refinery based in Nigeria as a case study, the research identified key factors affecting the reliability of refinery operations and proposed strategic maintenance measures to enhance operational efficiency and safety. The study utilized reliability centered maintenance (RCM) and failure modes and effect analysis (FMEA). From the evaluation, breaking down the components of the power system in the plant and addressing them individually exposes more vividly the errors in that system. Each failure mode was analyzed in terms of possible consequences on operation, function, or system status. The subject of risk assessment was presented and utilized to develop an approach for implementing the RCM in refinery power systems Finally, a proposed action plan was introduced to reduce the RPN number, thereby increasing the overall reliability and efficiency of the system component. It was observed that by reducing the RPN of each component, the overall efficiency of that component increases saving the company huge maintenance cost.

An optimal selective maintenance policy for multi-state series production systems by considering environment impact

Series production systems are prevalent in the manufacturing industry. The productivity of these systems is influenced by various factors, including system conditions, on-site environment, maintenance planning, and others. Efforts to develop an effective maintenance plan that considers the on-site environment are crucial for maximizing net revenue over the system’s lifetime, which has practical significance. Against this backdrop, a selective maintenance model is constructed for a multi-state series production system over a finite time horizon. This model describes the operation and maintenance processes at any decision period and analyzes the impact of the on-site environment on system productivity. Subsequently, an optimal maintenance strategy is formulated with the goal of maximizing net revenue throughout the system’s lifetime. To efficiently solve this optimal strategy, a hybrid solution approach is developed by integrating a deterministic method with a heuristic method. Finally, a numerical example involving a real chip mounter system is presented to verify the developed maintenance strategy. This is followed by a comparative experiment to validate the performance of the hybrid solution approach. Additionally, sensitivity analysis is conducted to test the strategy’s robustness. The results from the numerical example, comparative experiment, and sensitivity analysis demonstrate that the developed strategy is effective and robust, and the hybrid approach is efficient.

Age Estimation of Transmission Line Using Statistical Health Index and Failure Probability Curve-Fitting Method

The aging process of transmission lines has a direct impact on the reliability and safety of the power grid. Therefore, an accurate age estimation method is imperative for effective maintenance planning and infrastructure investment. This paper introduces a systematic methodology for estimating the age of overhead transmission lines, utilizing the percentage statistical health index (%SHI) and the failure probability curve-fitting (FPCF) method. The %SHI, employing a scoring and weighting approach derived from test results and inspections, is used to assess the actual condition of transmission line equipment. Additionally, the FPCF approach is applied to illustrate the connection between the SHI and the likelihood of failure, facilitating the assessment of transmission line age by fitting failure probability curves to the SHI data. This age is directly associated with the probability of experiencing a failure. The evaluation was conducted on 924 towers situated along four transmission lines connecting the 115 kV substations S1–S2, S3–S4, S5–S6, and S7–S8. These transmission lines are in four regions with diverse terrain and environments such as mountains, rice fields, and more. In the SHI calculation, practical testing results and historical failure data were applied. The results clearly indicate that there were notable disparities in the age estimations for transmission lines in diverse geographical regions of Thailand when compared to their actual ages. These discrepancies can be attributed to various factors, including the local environment, such as rainfall, flooding, and salt-laden air as well as specific geographical features like mountainous and coastal terrain. To mitigate the deterioration of transmission lines in all regions, it is essential to implement a proactive maintenance strategy. This strategy should involve more frequent inspections, the use of advanced monitoring technologies, and the establishment of robust maintenance procedures, with which it would become possible to enhance the accuracy of equipment condition assessments, ultimately resulting in an overall improvement in the reliability of transmission lines.

Reliability-Centered Preventive Maintenance Optimization for a Single-Component Mechanical Equipment

Due to the high failure rates of mechanical equipment with complex structures and numerous moving parts, devising an effective preventive maintenance (PM) plan and avoiding the influence brought by failure is crucial. However, some PM efforts are disorganized, unpractical, and unscientific, leading to prolonged downtime and significant cost losses. The challenge in creating PM plans is exacerbated by the asymmetry between maintenance and failure data. Therefore, focusing on single-unit mechanical equipment, the reliability-centered maintenance (RCM) idea is put forward to find out the key parts to implement preventive maintenance, and PM models are built to draw up a more reasonable PM plan. Such strategies aim to lower maintenance costs and enhance economic performance. Data on past maintenance and failures are analyzed to determine the life distribution and maintenance effect functions, helping to quantify the uncertainty caused by data asymmetry. Two PM optimization models considering time-varying failure rates are proposed: one focuses on minimizing costs, while the other aims to maximize availability. A PM plan example is demonstrated using a component from a tire-building machine including six parts, which proves the validity of the models. The availability results of two parts corresponding to the maintenance strategy obtained by the availability maximization model are above 0.99, and the results of total costs per unit time of the remaining four parts obtained by the cost minimization model are under 5.69.

Application of XGB-based metaheuristic techniques for prediction time-to-failure of mining machinery

Mining equipment is a critical component in the success of mining operations, and unplanned downtime can be costly. Efficient and intelligent maintenance planning is therefore essential to minimize downtime and maximize productivity. Consecutive time-to-failure (TTF) is an important indicator of machine reliability, and accurate TTF predictions enable effective preventive maintenance planning. Therefore, this study proposes hybrid models of extreme gradient boosting (XGB), optimized by particle swarm optimization (PSO) and gray wolf optimization (GWO), for the prediction of TTF in mining machinery. Additionally, validation of the hybrid model was conducted using support vector regression (SVR) method. Historical data on mining machine failures were collected, and a case study was conducted to investigate shovels in an open-pit mine. The PSO-XGB method was found to be the most accurate predictor of failure time with R2 values of 0.99, RMSE values of 50.66 and 51.77, MAE values of 4.52 and 10.81, and AARE values of 1.15 and 1.24 in the training and testing phases. This research highlights the importance of efficient and intelligent maintenance planning to minimize downtime and optimize productivity in mining operations.

Estimation of burst pressure of pipelines with interacting corrosion clusters based on machine learning models

Pipeline corrosion defects mostly appear in a colony such that they interact to reduce the failure pressure, which is not defined by features of a single corrosion defect. The huge amount of corrosion defects captured by in-line inspection tools including the variability of defect profile in pipelines and the dependence of the reliability assessment on such data pose significant research challenges in performance assurance. This highlights the need for computationally efficient modelling schemes to estimate the burst pressure of pipelines affected by both longitudinal and circumferential interacting corrosion defects. In the present paper, a novel approach is proposed for this purpose by combining supervised machine learning methods with 25 numerical models of corroded pipelines, validated with experimental results available from literature. Additionally, six improved composite defect shapes are proposed, resulting in 150 models to examine the non-linear behaviour of interacting corrosion defects by capturing the real the defect profiles captured by the In-line Inspection tools. The predicted failure pressures from the developed numerical models produced an absolute mean deviation of not exceeding 2.03% and 2.2% from the experimental burst pressure and the modified Mixed Type Interaction approach respectively, better than published results from the literature. Notably, the predicted failure pressures based on real pipeline data, infused with the generated artificial neural networks and non-linear regression models provide a total mean deviation of 3.1% and 7.3% respectively, thereby providing a path for effective maintenance planning.

DENSE SENSING ON ROAD BRIDGES NETWORK: NEW APPROACH TO DATA‐INFORMED ASSESSMENT

AbstractHazards, ageing, damaging, and traffic loads increase are some of the growing risks that bridge infrastructures in Europe face throughout their design life. Key issues of the asset management plan are the safety assessment of existing structures, the prioritization of the interventions and the optimization of the maintenance schedules. As such, to enable appropriate diagnostics and cost‐effective decisions on assessment/repairs, timely and accurate information from monitoring, inspection and testing is essential. This paper outlines the strategic role of SHM systems to identify the actual condition and future performance of the infrastructures, supporting an effective maintenance plan to meet the target reliability requirements. This contribution describes the use of dense sensing on an Italian bridges network of over 100 bridges, to account for monitoring in safety assessment, facilitating the application of proactive maintenance strategies through a deeper knowledge of the structural behaviour over time. Multiple case studies are detailed, where MEMS sensors have been installed with the purpose of monitoring the evolution of deterioration process, supporting a data‐informed structural diagnostics as well as providing evidence of the effectiveness of strengthening interventions.

Development of a non-Gaussian copula Bayesian network for safety assessment of metro tunnel maintenance

The operation and maintenance of metro systems play a crucial role in urban development, with a focus on ensuring the serviceability and safety of metro tunnels. Accurate evaluation of the condition of these tunnel states requires investigating the complex interaction of multiple factors that impact their safety state. This study developed a hybrid model that integrates pair copula constructions (PCCs) and Bayesian networks (BN) to assess the safety state of metro tunnels, considering complex dependencies among these factors. First, key performance indicators (KPIs) were selected to assess tunnel safety, based on six failure modes. Then, an improved copula-based PC algorithm was employed to learn the non-Gaussian bayesian network model, which eliminated the normality assumption in the marginal densities of the KPIs. Finally, a combination of forward reasoning and GM(1,1) was utilized to predict the safety state of the metro tunnel in time series, facilitating the formulation of effective operation and maintenance plans. Furthermore, the proposed approach was applied to a real case study of the Wuhan metro system to demonstrate its effectiveness and applicability. The results highlighted the significant influence of some KPIs, such as convergence deformation, dislocation displacement, and stripping area, on metro tunnel safety. These KPIs emerged as key factors requiring focused attention in the operation and maintenance of metro tunnels.

Implicit function‐based continuous representation for meticulous segmentation of cracks from high‐resolution images

AbstractHigh‐resolution (HR) crack images offer more detailed information for evaluating the structural condition and formulating effective maintenance or rehabilitation plans. However, the meticulous segmentation of HR crack images has been a challenge due to the limitations of mainstream deep learning algorithms that extract features in a discrete manner, as well as the constraints of computing resources. To address this issue, a novel implicit function‐integrated architecture, called the crack continuous refinement network (CCRN), was proposed for meticulous segmentation of cracks from HR images using a continuous representation manner. First, a crack continuous alignment module with a position encoding function was proposed to encode the tiny crack pixels that are easily lost in the sampling process. Then, a lightweight decoder embedded with implicit functions was customized to recover crack details from the aligned latent features and continuous position encoding information. Afterward, the gap between low‐resolution training images and HR inference results was bridged by the proposed continuous inference strategy. Finally, the robustness and practicability of the well‐trained CCRN were demonstrated by a parallel comparison and an unmanned aerial vehicle‐based field experiment.

Characterization of the state of the traffic signs focused on cyclists in Bogotá

IntroductionTraffic signs can be functional if they are visible and legible, especially when they alert about grave hazards to vulnerable road users such as cyclists. However, what is the current state of traffic signs directed at cyclists? This knowledge is essential for road infrastructure, especially in a city like Bogotá, which presents growing mobility challenges for cyclists. This paper aims to characterize the state of the signs directed at cyclists in Bogotá and evaluate the conformity level according to current guidelines. MethodsA reliable method was developed to evaluate existing signs. The method assesses the physical and functional characteristics of signs to verify if they are adequate from the human factors perspective and if they facilitate perception, are obeyed, and conform regarding dimensions, location, and function. With the method, an observational study was applied to characterize and evaluate signs in urban public spaces. ResultsEighty traffic signs were characterized in ten high-risk sectors for cyclists in Bogotá. Fifty-five percent of the signs are not in good condition, and often damaged; 18% are not coherent with the environment. Thirty-eight percent do not comply with the appropriate board size. The “Get off the bike” sign has the highest violation rate. ConclusionTraffic signs aimed at cyclists present problems focused on compatibility with guidelines and compliance by cyclists. The observation indicates no effective maintenance plans. The coherence problems indicate insufficient infrastructure design planning. The Road Signaling System requires standardization in its design, implementation, and operation to promote perception, comprehension, and compliance.

A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features

Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses, a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents. The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering. Current oil pipeline leakage signals are insufficient for feature extraction, while the training time for traditional leakage prediction models is too long. A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt (GA-LM) classification model for predicting the leakage status of oil pipelines. The signal that has been processed is transformed to the time and frequency domain, allowing full expression of the original signal. The traditional Back Propagation (BP) neural network is optimized by the Genetic Algorithm (GA) and Levenberg Marquardt (LM) algorithms. The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter. The Accuracy, Precision, Recall, and F1score of the GA-LM model is 95%, 93.5%, 96.7%, and 95.1%, respectively, which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples. The proposed GA-LM model can obviously reduce training time and improve recognition efficiency. In addition, considering that a large number of samples are required for model training, a wavelet threshold method is proposed to generate sample data with higher reliability. The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.

Behavior Analysis of Feeding Unit of a Paper Industry in Fuzzy Environment

The feeding unit is an important functional part in the paper industry that requires the development of effective maintenance plan based on its reliability analysis. This study analyzes reliability of the considered unit employing trapezoidal fuzzy numbers in fuzzy [Formula: see text]–[Formula: see text] methodology with Petri net modeling. In this analysis, the collected crisp input data of each subsystem has been fuzzified using trapezoidal fuzzy numbers. Then fuzzy values of several reliability factors of feeding unit, namely, failure rate, repair time, reliability, availability, etc. at ±15%, ±25% and ±40% spread levels have been calculated. Further, the obtained fuzzy values of reliability factors have been defuzzified to analyze system behavior of feeding unit. The analysis is useful for plant maintenance and operation management to understand the behavior of feeding unit in a more realistic manner.

Analytical Hierarchy Process (AHP) for Maintenance Management at Bali Mandara Hospital

The absence of an assessment of the level of damage and the condition of the Bali Mandara Hospital building makes the maintenance management plan contained in the umbrella contract only based on the damage that occurred in the previous year. Therefore, it is very difficult to determine which building infrastructure components require maintenance and repair first. This study examines the priority scale of infrastructure maintenance at Bali Mandara Hospital using the Analytical Hierarchy Process (AHP). In this study, researchers used a quantitative-descriptive approach. The assessment of the building condition index value is then determined by the priority scale of each component using the Analytical Hierarchy Process (AHP) method with the help of the Expert Choice Version 11 application and the SWOT Method. The results showed that the index value for the infrastructure condition of the Bali Mandara Hospital building was 89.492 with very good condition criteria and was in zone I, while the highest priority scale was faucets, namely 1,000, and the lowest priority was action on a scale of 0.050, where this priority scale can be used in planning. Standard Operating Procedure (SOP) and efficient maintenance management. So based on the results of this study, with SWOT analysis, maintenance management can develop a more effective and efficient maintenance plan and Standard Operating Procedure (SOP) by the building condition index and existing priority scale.

Decentralized and Privacy Sensitive Data De-Duplication Framework for Convenient Big Data Management in Cloud Backup Systems

The number of customers transferring information to cloud storage has grown significantly, with the rising prevalence of cloud computing. The rapidly rising data volume in the cloud, mostly on one side, is followed by a large replication of data. On the other hand, if there is a single duplicate copy of stored symmetrical information in the de-duplicate cloud backup the manipulation or lack of a single copy may cause untold failure. Thus, the deduplication of files and the auditing of credibility are extremely necessary and how they are achieved safely and effectively must be addressed in academic and commercial contexts urgently. In order to tune in this task by using application recognition, data similitude, and locality to simplify decentralized deduplication with two-tier internode and application deduction, we suggest a flexible direct decentralized symmetry deduplication architecture in a cloud scenario. It first distributes application logic to the contents of the directory through implementation-oriented steering to maintain a deployment location and also attributes the same kind of information to the cloud backup node with the storage node specificity by means of a hand printing-based network model to attain adequate global deduplication performance. We build up a new ownership mechanism during file deduplication to ensure continuity of tagging and symmetrical modeling and verify shared ownership. In addition, we plan an effective ownership policy maintenance plan. In order to introduce a probabilistic key process and reduce key storage capacity, a user-helped key is used for in-user block deduplication. Finally, the protection and efficiency audit demonstrate that the data integrity and accuracy of our system are ensured and symmetrically effective in the management of data ownership.

An Assessment of Operation and Maintenance of Trail Bridges for Sustainable Mobility in Nepal

Trial bridges have a significant impact on mobility and socioeconomic status in rural regions of Nepal. The construction of the trail bridges is the important transportation mobility of Nepal to enhance rural mobility. It is essential to establish an effective operation and maintenance (O&M) plan for the long-term sustainability of the trail bridges. Proper O&M mechanism has a significant impact on safe, secure, durable, and sustainable service in the community. In our context, it is very challenging due to a lack of proper technology, a proper management system, and a scarcity of resources. The study was focused on evaluating the implementation status of operation and maintenance of trail bridges in Mugum Karmarong Rural Municipality of Mugu district of Nepal. Quantitative and qualitative data have been collected to evaluate the operation and maintenance status of twenty bridges. The related officials, businessmen, teachers, students, local community members, user group members, health workers, and other stakeholders of Trail Bridge were considered for the survey. The design guidelines, published literature, and other related guidelines of the Government of Nepal (GoN) were analyzed for the research. The convenient survey was conducted from sampled respondents along with three focus group discussions in three trail bridge projects. Further, key informants were interviewed comprising different stakeholders involved in the project planning and implementation process from central to the local level. The quantitative and quantitative data has been adopted and analyzed using a simple statistical method has performed for quantitative data analysis, and the content analysis method is used to interpret qualitative findings. The study concluded that the overall implementation status of O&M of trail bridges in Mugum Karmarong Municipality is disappointing and needs immediate operation and maintenance. The study indicates that proper O&M of trail bridges have a significant reduction in time to reach the school and health service is directly related to increased enrollment in schools and increased health facilities in communities, respectively. The income status and livelihood of people in communities have improved due to an increase in mobility and market accessibility from the operation of trail bridges . The output of this study will be the milestone for the proper operation, and management system development for sustainable, reliable, and cost-effective infrastructure development in the country.