Proactive Maintenance Strategy Research Articles

Estimation of the Remaining Useful Life of Axially Loaded Members Through Nested Least-Square Support Vector Regression

ABSTRACT Prognosis and Health Management (PHM) is crucial for ensuring the reliable operation of structural components, such as bracing members. Despite this, limited study exists for the RUL estimation of civil engineering components. This paper presents a nested framework utilizing the Least Square Support Vector Regression (LS-SVR) for accurately predicting the Remaining Useful Life (RUL) of axially loaded members. The framework addresses the challenges of sparse data and multidimensional damage-sensitive features (DSFs) by employing a two-stage process. In the first stage, DSFs are forecasted using one-dimensional mapping, providing essential inputs for the second stage, where axial stiffness degradation is predicted using LS-SVR. The proposed framework is validated through an experimental study on axially loaded members subjected to low-cycle fatigue. To enhance model performance, simulated data generated from an OpenSees model is combined with the experimental data. The nested LS-SVR framework demonstrates superior performance compared to traditional methods, achieving high accuracy in RUL estimation as evidenced by the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. The framework’s ability to forecast axial stiffness degradation, a critical indicator of structural health, makes it a valuable tool for real-time assessment of structural component RUL. By integrating this framework with real-time monitoring systems, proactive maintenance strategies can be implemented, leading to safer and more efficient structural health management.

Methods and equipment for analysis and diagnosis of marine engines during navigation

ABSTRACT Expert diagnostic systems are required for the development of intelligent marine engines and other ship systems. Measurement and analysis of marine engine parameters are fundamental in ensuring the efficient operation of these key propulsion systems. This paper introduces a novel measurement device and method capable of real-time monitoring of cylinder pressure and emissions in marine engines. The experimental procedure involved measuring critical engine parameters and emissions, such as NOx, CO2, and particulate matter, under various operating conditions. The device provided accurate real-time data acquisition, enabling detailed analysis of combustion processes and emission profiles under various operating conditions. Furthermore, a numerical model was developed, like digital twins, which utilized the measured data to simulate engine performance, predict potential failures, and optimize operational parameters to enhance efficiency and reduce environmental impact. The two most important parameters: the indicated cylinder pressure and NOx emissions were simulated and validated. Measured NOx was measured 889 ppm at 90% and simulated results was 985 ppm, which is 10% difference. Specific fuel consumption has less than 1% difference between measured and simulated data. Novelty of this work is the methodology used in the testing and calibration processes. Current actual parameters of engine operation are monitored, based on which a comparative analysis is made in real time. Previous diagnostic control systems did not provide diagnostics, comparative analysis and optimization in real time. The integration with digital twin allowed potential wear and tear predictions, fuel consumption optimization, and emission reduction strategies, showcasing the potential of this technology to improve marine engine diagnostics and maintenance. The device and its virtual counterpart worked well together to provide a complete understanding of the engine’s response. This facilitates proactive maintenance strategies and contributes to eco-efficiency in the maritime sector.

Machine Vision AI in Railroad Safety: Advanced Inspection Techniques

This article explores the transformative impact of machine vision AI in railroad safety, focusing on advanced inspection techniques. It examines the integration of technologies such as line scan cameras, LiDAR, and IoT sensors in enabling comprehensive 360-degree inspections of railway infrastructure and rolling stock. The article discusses key innovations in continuous monitoring, data processing, and AI-driven analysis, highlighting their potential to significantly enhance defect detection accuracy, reduce maintenance costs, and prevent accidents. Real-world applications in structural integrity assessment and overhead line inspection are presented, along with an analysis of current challenges and future developments in the field. The article underscores the paradigm shift from reactive to proactive maintenance strategies, promising a safer and more efficient future for rail transport.

On the Construction of Energy Efficiency-based Degradation Indicator for Photovoltaic Solar Inverters

Photovoltaic systems are essential in the renewable energy sector, addressing global energy needs. PV inverters, which convert DC from solar panels to AC for grid use, are the most failure-prone components in these systems. This study aims to develop a degradation indicator based on energy efficiency for PV inverters to enhance their reliability and lifetime management. Through analysis of data from 35 PV plants in central Europe, involving eight inverter brands and fourteen models, this research identifies trends in inverter efficiency degradation. Despite literature suggesting minimal efficiency impact over time, our findings demonstrate measurable efficiency degradation, providing a new key performance indicator for proactive maintenance and replacement strategies.

Optimization of Maintenance Strategies in an Oil and Gas Production Facility to Minimize Maintenance Cost

Abstract: In this study, maintenance strategies in an oil and gas production facility were optimized to minimize maintenance costs. The centrifugal pump system in Port Harcourt Refinery was selected for this study because it significantly impacts on the productivity of the refining plant. The pump system’s failure mode effects and criticality analysis (FMECA) were examined, and an optimized maintenance task was developed for the system. The exponential reliability method was employed to analyse the pump’s failure data to determine the pump systems' failure rate and reliability while reliability centred maintenance (RCM) and linear programming (LP) model were employed to optimize the pump’s maintenance strategies and the pump’s maintenance labour force respectively. Broad-based results of the optimized maintenance strategies consisted of on-condition-directed (CD) maintenance, preventive maintenance (PreM), and Proactive maintenance (ProM) strategies to be carried out monthly. The optimized maintenance labour force result revealed that approximately three engineers and two technicians should be employed for the pump’s maintenance task with a minimum of N2, 585, 700 as cost of salaries for the labour force monthly as against the current maintenance labour force requiring fourteen engineers and thirteen technicians monthly. The results showed that the labour cost decreased from N180,000,000 per year to N31,028,400 per year (approximately 82.76% reduction) using the proposed optimized maintenance task compared to the current maintenance plan. Conclusion and recommendation were made that the maintenance optimization technique presented in this work should be adopted by the oil and gas processing and production companies in order to minimize maintenance cost.

Online and Offline Fault Detection and Diagnostics in a Nuclear Power Plant Condenser

Nuclear power plants (NPPs) face significant financial pressures due to operational and maintenance costs. This research investigates Fault Detection and Diagnostics (FDD) techniques to optimize maintenance schedules and reduce expenses. The NPP condenser plays a critical role in converting turbine exhaust steam back into water for reuse. Condenser tube fouling, a prevalent fault mode, impedes heat transfer efficiency and can lead to decreased plant efficiency and safety risks. This study proposes an FDD framework that leverages raw signal analyses from temperature and pressure monitoring to detect and diagnose condenser tube fouling in both online and offline settings. The online approach facilitates close-to-real-time predictions, enabling proactive maintenance strategies. Additionally, the framework explores incorporating a condenser’s maintenance history for enhanced diagnostics. We employ a dataset obtained from a simulated nuclear power plant condenser using the Asherah Nuclear Power Plant Simulator (ANS). ANS replicates the operational dynamics of a pressurized water reactor (PWR) type NPP. The proposed methodology utilizes an encoder-decoder (E-D) structured 1DCNN model to analyze the raw signals. The research demonstrates consistent and accurate fault detection and diagnostics for condenser tube fouling in both online and offline scenarios. A high potential for generalization to unseen conditions was observed. However, online detection using small data windows necessitates caution due to potential false alarms around the transition points. Our findings pave the way for further exploration of robust diagnostics by accommodating a wider spectrum of fouling rates within degradation classes using ANS. This combined online and offline FDD approach offers a promising solution for promoting operational safety, efficiency, and cost-effectiveness in NPP condensers.

Overload Alarm Prediction in Power Distribution Transformers

The growing demand for electricity puts more strain on the grid, requiring automated and proactive strategies such as overload prediction to improve grid maintenance. However, the intermittent nature of power distribution loads makes the prediction more challenging. This paper proposes a novel framework for overload alarm prediction in distribution transformers, aimed at enhancing the reliability and efficiency of grid operations. Leveraging real-world smart meter data and machine learning techniques, the proposed system develops a classification model to predict overloads for distribution transformers. Due to resource constraints, a new strategy is adopted to assess the significance of alarms based on expert observations. Subsequently, a new approach is developed to imitate the experts, leading to an automated decision-making process using random forest. Ultimately, the transfer learning strategy is utilized to predict overload alarms for distribution transformers facing data scarcity in real-world applications. The proposed system demonstrates high accuracy of overload alarm predictions, paving the way for developing more proactive grid maintenance strategies.

Evaluasi Metode Health Index Untuk Menentukan Prioritas Pemeliharaan Gardu Distribusi

Electricity is an essential energy source that plays a crucial role in human life. A key component of power distribution systems is the distribution substation. As distribution substations are in operation over time, their components experience aging, which can result in operational failures. This situation emphasizes the necessity for regular evaluations and diagnostics of distribution substations. Such assessments are crucial for identifying when equipment requires repair or replacement, ensuring that the system operates at maximum efficiency. By implementing a proactive maintenance strategy, utilities can mitigate risks, enhance reliability, and extend the lifespan of their infrastructure. The scoring matrix method is a technique for evaluating the condition of distribution substations. It combines results from field and laboratory tests to produce values that accurately reflect the substation's condition. The research focused on four distribution substations managed by PT PLN, classifying them into high, medium, and low categories based on their health index scores. The results show that, among the four distribution substations evaluated, substations A and C received health indices exceeding 80%, classifying them in the high category. It is therefore recommended that these substations continue operating normally while adhering to their scheduled periodic maintenance. In contrast, substation B received a health index between 50% and 80%, placing it in the medium category. While it can remain operational, it requires closer monitoring and maintenance at specific intervals to ensure reliability and prevent potential issues.

Mechanics-informed autoencoder enables automated detection and localization of unforeseen structural damage

Structural health monitoring ensures the safety and longevity of structures like buildings and bridges. As the volume and scale of structures and the impact of their failure continue to grow, there is a dire need for SHM techniques that are scalable, inexpensive, can operate passively without human intervention, and are customized for each mechanical structure without the need for complex baseline models. We present a novel “deploy-and-forget” approach for automated detection and localization of damage in structures. It is a synergistic integration of entirely passive measurements from inexpensive sensors, data compression, and a mechanics-informed autoencoder. Once deployed, the model continuously learns and adapts a bespoke baseline model for each structure, learning from its undamaged state’s response characteristics. After learning from just 3 hours of data, it can autonomously detect and localize different types of unforeseen damage. Results from numerical simulations and experiments indicate that incorporating the mechanical characteristics into the autoencoder allows for up to a 35% improvement in the detection and localization of minor damage over a standard autoencoder. Our approach holds significant promise for reducing human intervention and inspection costs while enabling proactive and preventive maintenance strategies. This will extend the lifespan, reliability, and sustainability of civil infrastructures.

Optimized Manufacturing and Construction Site Machines Maintenance Prediction using Machine Learning and Crayfish Algorithm

Efficient maintenance prediction is critical for ensuring the operational continuity and longevity of industrial machinery. This paper presents a comparative analysis of diverse machine-learning algorithms for the task of machine maintenance prediction. Through rigorous experimentation and evaluation, we assess the performance of algorithms including AdaBoost, Random Forest, Gradient Boosting, and Support Vector Machines (SVM). Additionally, to enhance predictive accuracy, we integrate an optimizer algorithm, Cuckoo Search,into our framework. This optimization technique fine-tunes algorithm parameters, further improving accuracy. Our findings offer valuable insights into optimizing machine maintenance prediction, empowering industries with proactive maintenance strategies to mitigate downtime and enhance productivity.

Digital transformation technologies for conveyor belts predictive maintenance: a review

The availability of condition-monitoring data has increased due to internet of things (IoT) technologies, providing information on various parameters like vibration, temperature, current, and voltage. Cloud computing and big data facilitate the prevention of failures and estimation of remaining useful life through advanced mathematical models and artificial intelligence (AI) techniques. These enable prompt and suitable maintenance actions. This article conducts a systematic review of digital transformation technologies, including cloud computing, edge computing, AI, machine learning (ML), and TinyML, in predictive maintenance for conveyor belt systems. This article reviews how these digital transformation technologies improve predictive maintenance strategies for conveyor belts. The systematic review summarizes the results and challenges of various methodologies used in conveyor belt systems and suggests areas for further research. This paper aimed to serve as a useful resource for researchers, practitioners, and industry professionals seeking insights into current predictive maintenance technologies for conveyor belt systems. The takeaways of the review are expected to ignite discussion on efficient and proactive maintenance strategies and promote the development of innovative solutions for ensuring the reliability and longevity of conveyor belt systems in the digital era.

Automatic bridge inspection database construction through hybrid information extraction and large language models

Regular bridge inspections generate extensive reports that, while critical for maintenance, often remain underutilized due to their unstructured format. Traditional information extraction methods depend on intricate labeling systems that commonly require time-consuming and labor-intensive labeling. This paper presents a novel bridge inspection database construction method leveraging LLM-assisted information extraction. First, we introduce the pseudo-labelling method using a closed-source LLM to generate high-quality data. Then we propose the hybrid extraction pipeline to extract relevant information segments and process them by a generation-based IE model, fine-tuned on pseudo-labeled data. Finally, the extracted data is used to construct the bridge inspection database. The proposed method, validated with real-world data, not only demonstrates higher extraction precision than the closed-source LLM used for pseudo-labeling but also outperforms traditional methods in both data preparation time and extraction accuracy. This approach provides a scalable solution for more proactive and data-driven bridge maintenance strategies.

Optimising LPG Bottling Plant With DES Using Flexsim Simulation Tool

Purpose: This study explores the application of Discrete Event Simulation (DES) using FlexSim software to enhance the operational efficiency of a Liquefied Petroleum Gas (LPG) bottling plant. The primary goal is to ensure that the LPG plant can safely and efficiently meet escalating market demands, thereby prioritising all stakeholders' safety. Design/Methodology/Approach: The research design focused on empirical research and experimental simulation modelling. The study began with collecting and analysing one month of LPG plant data, laying the foundation for developing a simulation model. Verification and validation processes ensured the model's accuracy, enabling the investigation of various operational scenarios. The key performance indicators like First Time to Failure (FTTF), Time Between Failures (TBF), and Time to Repair (TTR) were analysed. The availability rates were 77% from actual data and 76% from simulations, showing that the model is suitable for real-world use. Findings: This study's findings underscore the potential impact of proactive maintenance strategies and operational enhancements as practical and applicable approaches to optimising performance. The analysis also revealed significant improvement opportunities through what-if scenarios: increasing MTBF by 100%, reducing MTTR by 50%, and raising conveyor speed by 15%. Research Limitation: The study's dependence on a systematic literature review could restrict its ability to capture the industry's real-time dynamics. Practical Implication: Implementing proactive maintenance strategies and operational enhancements as practical approaches can reduce downtime and costs and promote productivity and safety. Social Implication: Optimising plant operations will help maintain supply chain stability with the growing demand for LPG. Originality/Value: This work contributes valuable insights and recommendations, establishing a foundation for informed decision-making in the LPG bottling sector.

Advanced Sensor Technologies in CAVs for Traditional and Smart Road Condition Monitoring: A Review

This paper explores new sensor technologies and their integration within Connected Autonomous Vehicles (CAVs) for real-time road condition monitoring. Sensors like accelerometers, gyroscopes, LiDAR, cameras, and radar that have been made available on CAVs are able to detect anomalies on roads, including potholes, surface cracks, or roughness. This paper also describes advanced data processing techniques of data detected with sensors, including machine learning algorithms, sensor fusion, and edge computing, which enhance accuracy and reliability in road condition assessment. Together, these technologies support instant road safety and long-term maintenance cost reduction with proactive maintenance strategies. Finally, this article provides a comprehensive review of the state-of-the-art future directions of condition monitoring systems for traditional and smart roads.

Dynamic Load Balancing and Service Prioritization Algorithm for Cloud Computing Environments

Bridge infrastructure plays a critical role in ensuring the safe and efficient transportation of goods and people. This research paper investigates the strength characteristics of bridges with a focus on understanding the factors influencing their structural integrity and resilience. The study employs a comprehensive methodology that includes field inspections, laboratory testing, and advanced computational modeling techniques to assess the performance of various bridge types under different loading conditions. Findings reveal the complex interplay between design parameters, material properties, and environmental factors in determining bridge strength and durability. Moreover, the research highlights the importance of proactive maintenance strategies and innovative engineering solutions to enhance the resilience of bridge infrastructure against aging, deterioration, and extreme events. The insights gained from this study have significant implications for bridge design, maintenance practices, and public safety, ultimately contributing to the sustainability and reliability of transportation networks.

Analysis, Assessment, and Mitigation of Stress Corrosion Cracking in Austenitic Stainless Steels in the Oil and Gas Sector: A Review

This comprehensive review examines the phenomena of stress corrosion cracking (SCC) and chloride-induced stress corrosion cracking (Cl-SCC) in materials commonly used in the oil and gas industry, with a focus on austenitic stainless steels. The study reveals that SCC initiation can occur at temperatures as low as 20 °C, while Cl-SCC propagation rates significantly increase above 60 °C, reaching up to 0.1 mm/day in environments with high chloride concentrations. Experimental methods such as Slow Strain Rate Tests (SSRTs), Small Punch Tests (SPTs), and Constant-Load Tests (CLTs) were employed to quantify the impacts of temperature, chloride concentration, and pH on SCC susceptibility. The results highlight the critical role of these factors in determining the susceptibility of materials to SCC. The review emphasizes the importance of implementing various mitigation strategies to prevent SCC, including the use of corrosion-resistant alloys, protective coatings, cathodic protection, and corrosion inhibitors. Additionally, regular monitoring using advanced sensor technologies capable of detecting early signs of SCC is crucial for preventing the onset of SCC. The study concludes with practical recommendations for enhancing infrastructure resilience through meticulous material selection, comprehensive environmental monitoring, and proactive maintenance strategies, aimed at safeguarding operational integrity and ensuring environmental compliance. The review underscores the significance of considering the interplay between mechanical stresses and corrosive environments in the selection and application of materials in the oil and gas industry. Low pH levels and high temperatures facilitate the rapid progression of SCC, with experimental results indicating that stainless steel forms passive films with more defects under these conditions, reducing corrosion resistance. This interplay highlights the need for a comprehensive understanding of the complex interactions between materials, environments, and mechanical stresses to ensure the long-term integrity of critical infrastructure.

Towards proactive corrosion management: A predictive modeling approach in pipeline industrial applications

Corrosion is the most significant factor affecting pipeline integrity management. It may lead to facility damage, production interruption, environmental pollution and even explosion. Corrosion management developed in recent years is a critical means to ensure the safety and reliability of pipeline systems. However, developing proactive corrosion management is difficult due to limited availability of data on target pipelines and corrosion factors. The motivation of this study is to develop a proactive corrosion management approach based on a predictive framework. The proposed framework integrates multiple corrosion factors, diverse working conditions, multi-phase degradation, and probabilistic analysis to offer an efficient analysis tool. A numerical analysis of corrosion prediction is conducted using the corrosion data of the training pipeline and the target pipeline, and the corrosion rate, corrosion depth, and remaining useful life (RUL) of the target pipeline are evaluated. Based on the RUL information, the maintenance time of the pipeline is further analyzed to maximize the benefits of maintenance. This study offers a comprehensive approach to pipeline corrosion prediction, facilitating proactive maintenance strategies and improving asset management in the pipeline industry.

An explainable artificial intelligence model for predictive maintenance and spare parts optimization

Maintenance strategies are vital for industrial and manufacturing systems. This study considers a proactive maintenance strategy and emphasizes using analytics and data science. We propose an Explainable Artificial Intelligence (XAI) methodology for predictive maintenance. The proposed method utilizes a machine learning project cycle and Python libraries to interpret the results using the Local Interpretable Model-agnostic Explanations (LIME) method. We also introduce an early concept of spare parts management, presenting insights from predictive maintenance outcomes and providing explanations for decision-makers to enhance their understanding of the influential factors behind predictions. This study demonstrates that utilizing machine learning models in predictive maintenance is highly beneficial; however, the binary outcomes of these models can be misunderstood by decision-makers. Detailed explanations provided to decision-makers will directly impact maintenance decisions and improve spare part management.

Causes of Safety Barrier Failures at Oil and Gas Facilities in Nigeria: A Technical Approach

This study investigates the company-controlled causes of barrier failure in Niger Delta region of Nigeria. Also, it focuses on oil and gas facilities and their operations involving hydrocarbon handling. Safety Barriers in the oil and gas industry are crucial safety systems designed to prevent hazards and mitigate the consequences of incidents. These barriers, encompassing technical, operational, and organizational elements, play a significant role in handling hazardous substances and preventing their unintended release. Despite their importance, barrier failures have been identified as major causes of process safety incidents globally, including Niger Delta region of Nigeria. A cross-sectional research design, using a questionnaire to collect primary data from 132 personnel across 12 facilities, was employed. Statistical data analyses were carried out using XLSTAT Version 2016. Findings indicate that process upsets and technical/physical failures are significant risk influencing factors, while human and operational errors, though present, are less impactful. Key technical failures include degradation of valve sealing and flange gaskets, while process upsets often result from overpressure and malfunctioning transmitters. The study highlights the need for improved risk reduction strategies and periodic training to enhance safety practices in the Niger Delta's oil and gas industry. Recommendations include increasing technical components of risk strategies and ensuring regular maintenance and compliance with safety regulations; that is, proactive maintenance strategies to mitigate the identified risks.

Enhancing Ship Maintenance Management in Maritime Engineering Education: Insights from Vocational Internships

This research investigates ship maintenance management within the context of vocational education in maritime engineering, focusing on the experiences of senior students during internships in port and shipping management industries. Utilising qualitative methods, including semi-structured interviews and focus group discussions, the study explores the practical application of theoretical knowledge in preventive, corrective, and proactive maintenance strategies. Seven participants from a shipping vocational school in Jakarta provided insights into methodologies, challenges, and educational outcomes in ship maintenance. Key findings highlight the effectiveness of preventive maintenance in enhancing operational reliability and reducing downtime, despite challenges such as resource constraints and operational disruptions. Recommendations include curriculum enhancements to integrate advanced training in predictive maintenance technologies and promote sustainable practices. The research underscores the importance of vocational education in preparing future maritime engineers for industry demands and advancing sustainable practices in maritime transportation.