Proactive Maintenance Research Articles

Experimental vibration-based output-only damage localization of mechanical systems

Vibration-based Structural Health Monitoring (SHM) is becoming increasingly widespread in a variety of engineering sectors as its variability enables diverse applications. It enables full-time assessment of the status of a structure, allowing for early maintenance and repair to prevent further damage or even failure. The presented study is part of the SPP100+ program, which has the objective of extending the usability of complex structures. This study presents an experimental investigation of the damage localization of mechanical structures while considering varying environmental and operational conditions (EOC). For this, a testing field was designed and applied, enabling one to study the effects of the varying conditions in a comparable way. The research is based on the approach to modify the state projection estimation error (SP2E) method to account for the influence of EOC. The proposed method utilizes output-only system identification and state space parameterization to estimate the current structural state. Stochastic subspace identification (SSI) and H_∞ -estimation are employed to identify the state space model during a learning phase. Subsequently, the SP2E method is applied during the monitoring phase to localize structural alterations. Moreover, an algorithm is introduced to estimate the weighting factors of different environmental and operational conditions. To verify the efficacy of the proposed method, an extensive experimental study is conducted. The numerical analysis successfully demonstrates the applicability of the method for localizing mass perturbation and stiffness degradation under various EOC. This work contributes to proactive maintenance and damage detection for mechanical structures under varying conditions.

Butterfly valve erosion prediction based on LSTM network

When valves and pipelines are used, erosion wear is a major concern. Erosion wear can lead to equipment downtime, material replacement, and other issues, as well as seal surface failure. The research delves into the phenomenon of erosion in butterfly valves, crucial components utilized across diverse industrial sectors. Erosion primarily affects the valve's disc and seat regions, leading to premature wear and compromised performance. To address this issue, the research employs computational fluid dynamics (CFD) and machine learning techniques, including long short-term memory (LSTM) and BP neural networks, to predict erosion rates under various conditions (different valve openings and particle diameters). Results indicate that erosion escalates with time and larger valve openings, highlighting the need for proactive maintenance strategies. The LSTM model demonstrates superior predictive capabilities compared to the BP neural network, offering valuable insights for improving butterfly valve design and operational efficiency in industrial settings. Furthermore, to seek a more efficient network configuration, the intelligent search capabilities of the particle swarm optimization (PSO) algorithm have been utilized to systematically explore the optimal network structure parameters. The prediction results highlight the advantages of LSTM in handling time series data, particularly in predicting erosion rates with complex dynamic characteristics.

Advanced Automation Detection of Cracks in Railway Tracks

Abstract: India’s transportation infrastructure relies heavily identification. The integration of IR sensors enables the early detection of cracks on the track surface, allowing for the timely identification of potential structural weaknesses, on the Indian Railways, which serves as a major means of long-distance travel for millions. Nevertheless, the discoveryof cracks on railway tracks still poses serious problems that can compromise safety if not addressed. This paper thereforedescribes a path-breaking effort towards a strong crack detection mechanism in railways. Our unique way involves integrating infrared (IR) sensors for crack detection and ultrasound sensors for object detection to entirely change track monitoring thereby enhancing railway safety and maintenance efficiency. The system presented is able to detect any possible hazards within targeted area thus leading to prevention-based action through proactive maintenance; italso reduces cases of accidents as early detections helps in minimizing their chances of occurrence by making use of real-time data from these sensors. Our method demonstratesits effectiveness through experiments, showing that it can detect both cracks and blocking objects on the train tracks, hence contributing towards better rail safety systems while offering a promising direction for pre- emptive maintenanceprograms and risk control strategies. Our study proposes an innovative remedy for the current issue of security inclusion along railway tracks. Withthis combination involving IRs as well as ultrasonic sensors, our device has fulltime surveillance applications so that they are able to intervene timely before accidents are caused by this malfunctioning parts of the locomotive engines.

Exploring technological and operational challenges in high-pressure: High-temperature drilling techniques

This comprehensive review explores the complex landscape of HPHT drilling, examining the underlying principles, innovative technologies, and operational strategies used to overcome challenges such as wellbore stability, fluid management, equipment reliability, and downhole conditions. By understanding and addressing these challenges, the oil and gas industry can advance HPHT drilling capabilities and unlock new sources of energy to meet growing global demand. High-pressure, high-temperature (HPHT) drilling techniques have become indispensable for the oil and gas industry to tap into deep hydrocarbon reservoirs located in challenging environments. Despite their significance, HPHT drilling operations pose a myriad of technological and operational challenges that demand meticulous attention to safety, efficiency, and success. This comprehensive review embarks on an exploration of the intricate landscape of HPHT drilling, delving into the fundamental principles, innovative technologies, and operational strategies pivotal in surmounting obstacles such as wellbore stability, fluid management, equipment reliability, and downhole conditions. The review begins by delineating the distinctive characteristics of HPHT environments, highlighting the formidable challenges inherent in drilling operations conducted under extreme pressures and temperatures. Subsequently, it elucidates the critical importance of maintaining wellbore stability amidst such conditions, elucidating the risks of collapse and blowouts and offering insights into mitigation strategies ranging from meticulous drilling fluid selection to advanced wellbore monitoring techniques. Fluid management emerges as a focal point, as the review navigates through the complexities of selecting, formulating, and managing drilling fluids tailored for HPHT environments. Innovative technologies such as nanotechnology applications and synthetic-based mud systems are explored for their efficacy in addressing fluid-related challenges and enhancing drilling performance. The reliability and integrity of equipment constitute another critical aspect examined in the review, with emphasis on mechanical challenges, material selection, and proactive maintenance strategies essential for safeguarding equipment performance and longevity in HPHT operations. Furthermore, the review presents case studies and best practices gleaned from successful HPHT drilling projects, offering valuable insights and lessons learned to inform future endeavors.

Balancing the maintenance strategies to making decisions using Monte Carlo method

This study aims to develop comprehensive maintenance strategies tailored to enhance the dependability, performance, and lifespan of critical assets within industrial and organizational settings. By integrating proactive, preventive, predictive, and corrective maintenance tactics, our strategy seeks to minimize downtime, reduce costs, and optimize asset performance. Drawing from extensive case studies across various industrial sectors, our research utilizes robust data analysis to inform strategy development.We employ mathematical cost models and simulations using the Monte Carlo Method in MATLAB to evaluate the performance and robustness of different maintenance strategies, including time-based and condition-based approaches. Our findings demonstrate that a holistic maintenance approach significantly improves operational efficiency and asset longevity. Specifically, our analysis reveals that integrated maintenance strategies lead to reduced downtime, lower maintenance costs, and enhanced asset reliability.Policy implications of our research suggest that organizations should adopt integrated maintenance strategies to enhance asset reliability and performance, ultimately achieving sustained operational excellence. By emphasizing the importance of proactive maintenance measures alongside traditional reactive approaches, organizations can effectively manage their critical assets, leading to improved operational outcomes and long-term success.–Integration of proactive, preventive, predictive, and corrective maintenance tactics–Evaluation of performance and robustness through mathematical cost models–Application of the Monte Carlo Method in MATLAB for comparative analysis

Remaining Useful Life Estimation (RUL) For Critical Mechanical Components In Tractor

The proposed framework utilizes an evolutionary mathematical approach to optimize data-related parameters for estimating the Remaining Useful Life (RUL) of critical mechanical components. By integrating advanced time window techniques and considering factors like downtime and failure occurrences, it offers a comprehensive solution for RUL prediction across various industrial contexts. Continuous monitoring systems play a pivotal role by detecting component degradation early, allowing for proactive maintenance and minimizing catastrophic failures. Combining Mean Time to Repair (MTTR) and Mean Time Between Failures (MTBF) offers valuable insights into system performance, enabling proactive maintenance strategies. This review explores mathematical methodologies for predicting RUL in tractors, crucial for enhancing maintenance planning and remanufacturing engineering.

Elevating Smart Manufacturing with a Unified Predictive Maintenance Platform: The Synergy between Data Warehousing, Apache Spark, and Machine Learning.

The transition to smart manufacturing introduces heightened complexity in regard to the machinery and equipment used within modern collaborative manufacturing landscapes, presenting significant risks associated with equipment failures. The core ambition of smart manufacturing is to elevate automation through the integration of state-of-the-art technologies, including artificial intelligence (AI), the Internet of Things (IoT), machine-to-machine (M2M) communication, cloud technology, and expansive big data analytics. This technological evolution underscores the necessity for advanced predictive maintenance strategies that proactively detect equipment anomalies before they escalate into costly downtime. Addressing this need, our research presents an end-to-end platform that merges the organizational capabilities of data warehousing with the computational efficiency of Apache Spark. This system adeptly manages voluminous time-series sensor data, leverages big data analytics for the seamless creation of machine learning models, and utilizes an Apache Spark-powered engine for the instantaneous processing of streaming data for fault detection. This comprehensive platform exemplifies a significant leap forward in smart manufacturing, offering a proactive maintenance model that enhances operational reliability and sustainability in the digital manufacturing era.

THE RELIABILITY EVALUATION OF THE DECK MACHINERY AND GALLEY EQUIPMENT OF A BULK CARRIER BY UTILIZING THE FAILURE RECORDS

Among various modes of transportation, maritime transportation holds critical importance since it provides substantial carrying capacity with low unit costs. To perform seamless and efficient operations in maritime transportation plays a pivotal role in achieving sustainable development goals and the International Maritime Organization (IMO) targets. The execution of uninterrupted operations can only be carried out with the existence of reliable systems. Creating reliable systems onboard is possible through the implementation of planned and proactive maintenance strategies and leveraging experiences gained from past failures. 10-year failure records of bulk carriers have been scrutinized within the scope of system reliability to determine critical equipment and units. The data has been categorized into subgroups under four fundamental headings, and subsequent reliability analyses have been conducted on each subgroup. Within the subgroups, the reliability of navigation equipment should be improved since it has the highest failure rate and its malfunction can cause very serious marine accidents. This equipment is followed by fire-fighting systems, cargo equipment, and GMDSS instruments which are essential for ship operations based on reliability results. Therefore, regular failure records, planned and proactive maintenance strategies, and also extra efforts should be performed on this equipment to ensure sustainable and seamless operations in the maritime sector.

Revolutionizing smart grid-ready management systems: A holistic framework for optimal grid reliability

Existing energy management systems are becoming increasingly insecure and inefficient due to the rapid adoption of smart grid technology. Current research indicates that effectively managing dynamic energy flows, adjusting to changing needs, and protecting against new cyber threats remain significant challenges for the smart grid system. An advanced and comprehensive plan for managing smart grids is therefore required, capable of addressing these delicate and multifaceted problems. The proposed framework addresses these challenges through unifying several key aspects, it includes an advanced data acquisition system that captures real-time data from various grid sources, enabling comprehensive energy monitoring and dynamic flow analysis. By integrating predictive algorithms, the framework provides precise energy demand forecasting, which is essential for adaptive grid management. A significant contribution is the incorporation of an AI-based module for diagnostics and prognostics, which leverages machine learning techniques to shift from reactive to proactive maintenance strategies. The optimal power flow (OPF) optimization module represents a central component of the framework. It employs advanced computational methods to ensure efficient and cost-effective power distribution, particularly in grids incorporating renewable energy sources. Additionally, the architectural framework is strengthened by a robust cybersecurity module designed to safeguard against a wide range of cyber threats, maintaining the integrity of both operational and consumer data. This paper also addresses practical implementation challenges such as compatibility with existing infrastructure, investment costs, and the need for specialized training. This solution represents a new benchmark for smart grid operations, ensuring more sustainable and efficient energy systems.

A simulation study of touch-free automatic alcohol-based handrub dispensers on hand hygiene disruption in healthcare settings

Automatic dispensers of alcohol-based handrub (ABHR) have been widely adopted in healthcare facilities to maintain hand hygiene (HH). A proper supply of energy and refill is crucial to ensure uninterrupted access to hand sanitizing and minimize workflow disruption and inefficiencies. Various energy design and refill replenishment technologies have emerged with promising potential to eliminate HH disruptions. However, there is a lack of quantitative studies assessing the design impact on hand hygiene performance in healthcare settings. In this paper, we employ data-driven discrete-event simulation (DES) to evaluate the long-term performance of various energy designs of automatic dispensers in healthcare facilities. We analyze 7 years of historical usage data from 4 US hospitals and identify the usage patterns, which serve as the input traffic for our simulation model. We then estimate the workflow disruption caused by different types of dispensers over a 6-year period, in terms of the number of missed HH opportunities, battery replacements, and duration of downtime. The simulation results suggest that the differences in performance are significant among dispenser types. In high usage, the number of missed HH opportunities caused by refill depletion ranges from 403.1 to 1232.4, and total downtime ranges from 0 to 96.3 h. Implementing proactive maintenance measures, such as service refill alerts, can greatly reduce the chances of ABHR depletion, resulting an 81.6 % decrease in HH disruptions for a single dispenser in high usage. Therefore, healthcare facilities should consider the variations in dispenser design, including the energy management system. They should also carefully study dispenser usage patterns to implement optimized policies and practices for ABHR refill maintenance to minimize overall missed HH opportunities.

Advanced Prognostic Models for Bearing Health: A Comparative Analysis of BiLSTM and ANFIS

Bearings play a critical role in the operation of rotary machines, serving as essential components. Their failure often leads to unexpected shutdowns, posing a significant risk to the entire system. To mitigate these risks, it is imperative to implement proactive maintenance measures and strategic planning to prevent system breakdowns. This article introduces a comparative analysis between two predictive modelling approaches: Bidirectional Long Short-Term Memory (Bi-LSTM) and Adaptive Neuro Fuzzy Inference System (ANFIS) networks, aiming to enhance bearing prognostics. The proposed methodology involves a two-step process. Firstly, data undergoes pre-processing through wavelet packet decomposition (WPD). Subsequently, a degradation model is employed for predicting the remaining useful life (RUL). To validate the accuracy of the proposed approach, extensive testing is conducted using a bearing's life dataset obtained from a run-to-failure experiment. The results demonstrate that the ANFIS model exhibits remarkable capabilities in learning and accurately estimating the system's RUL, achieving this with minimal computation time compared to alternative methods, thus presenting a more efficient and precise solution.

Enhancing Energy Management in New Energy Vehicles and Energy Storage Systems Through Advanced Data Analysis and Machine Learning

This paper explores the pivotal role of data analysis and machine learning in advancing energy management strategies for New Energy Vehicles (NEVs) and Energy Storage Systems (ESS). Focused on the comprehensive journey from data collection and preprocessing to the application of dynamic programming, reinforcement learning, and genetic algorithms, our study underscores the transformational impact of these technologies on optimizing energy utilization and prolonging battery life. Initial stages involve meticulous data gathering and preprocessing to ensure the quality and usability of information derived from operational parameters. Subsequently, feature selection and engineering refine this data into meaningful insights, laying the groundwork for predictive modeling. These models forecast energy demands and system behavior, facilitating proactive maintenance and system efficiency improvements. We delve into optimization strategies, highlighting dynamic programming's role in decision-making, reinforcement learning's adaptability to environmental changes, and genetic algorithms' exploration of optimal charging/discharging strategies. These methodologies collectively contribute to sustainable energy practices and resource conservation, marking significant advancements in the field. The integration of machine learning not only enhances predictive maintenance and charging protocol optimization but also addresses challenges related to data scarcity, model generalizability, and interpretability. This paper provides a comprehensive analysis of current methodologies and future prospects, advocating for a multidisciplinary approach to further enrich the research landscape in energy management for NEVs and ESS.

Enhancing Energy Management in New Energy Vehicles and Energy Storage Systems Through Advanced Data Analysis and Machine Learning

This paper explores the pivotal role of data analysis and machine learning in advancing energy management strategies for New Energy Vehicles (NEVs) and Energy Storage Systems (ESS). Focused on the comprehensive journey from data collection and preprocessing to the application of dynamic programming, reinforcement learning, and genetic algorithms, our study underscores the transformational impact of these technologies on optimizing energy utilization and prolonging battery life. Initial stages involve meticulous data gathering and preprocessing to ensure the quality and usability of information derived from operational parameters. Subsequently, feature selection and engineering refine this data into meaningful insights, laying the groundwork for predictive modeling. These models forecast energy demands and system behavior, facilitating proactive maintenance and system efficiency improvements. We delve into optimization strategies, highlighting dynamic programming's role in decision-making, reinforcement learning's adaptability to environmental changes, and genetic algorithms' exploration of optimal charging/discharging strategies. These methodologies collectively contribute to sustainable energy practices and resource conservation, marking significant advancements in the field. The integration of machine learning not only enhances predictive maintenance and charging protocol optimization but also addresses challenges related to data scarcity, model generalizability, and interpretability. This paper provides a comprehensive analysis of current methodologies and future prospects, advocating for a multidisciplinary approach to further enrich the research landscape in energy management for NEVs and ESS.

Instrument Air Compressors: The Lifeline of Oil and Gas Facility. Volume 1. A Case Study of Oml 17 – Nigeria

Instrument air compressors (IACs) play a pivotal role as the backbone of oil and gas facilities, providing essential compressed air to power critical instruments and pneumatic systems. (Thomas Paulose, 2024). Instrument air is a critical component in the oil and gas industry. It is compressed air that is used to power instruments and control systems that are used in the production, processing, and transportation of oil and gas. (EY, 2020). This paper delves into the indispensable significance of IACs in ensuring the reliable and safe operation of oil and gas facilities, elucidating their crucial role in various operational processes, including control systems, safety devices, and process instrumentation. Through a thorough examination of the importance of instrument air in the oil and gas industry, this paper emphasizes the paramount importance of maintaining optimal performance and reliability of IACs. It discusses the key challenges and considerations associated with ensuring the continuous supply of instrument air, encompassing equipment reliability, energy efficiency, and maintenance practices. Furthermore, the paper explores effective strategies for optimizing instrument air compressor performance, encompassing proactive maintenance, technological advancements, and operational enhancements. (Abhishek Kumar et al 2021). Utilizing case studies and real-world examples, the paper illustrates the profound impact of proficient instrument air compressor management on overall facility reliability, safety, and operational efficiency. In conclusion, this paper underscores the critical recognition of instrument air compressors as indispensable assets within oil and gas facilities. (EY, 2020). It emphasizes the imperative for proactive management and optimization to uphold their continued reliability and efficacy. (Paul J. Holdcroft et al, 2015) By prioritizing the maintenance and performance of IACs, oil and gas operators can elevate facility operations, mitigate risks, and uphold the industry's commitment to safety and environmental stewardship.

Corrosion fatigue analysis of NREL’s 15-MW offshore wind turbine with time-varying stress concentration factors

Over the last twenty years, significant development in wind turbine technologies has led to a dramatic increase in the scale of wind turbines with many now beginning to be installed in offshore locations. Consequently, modern multi-megawatt offshore wind turbines are exposed to increased cyclic loading in addition to an increased risk of corrosion attack. The combination of these two factors may result in wind turbine support structures becoming increasingly vulnerable to fatigue corrosion. The objective of this work is to investigate the impact of material thinning in fatigue-prone areas with respect to fatigue loading and ultimately to examine the potential repercussions on the lifespan of wind turbine support structures. To achieve this, a composite model is constructed coupling results from a multi-body structural dynamic model with time-varying Stress Concentration Factors (SCF) obtained from a finite element model (FEM) of NREL’s 15-MW monopile-based offshore wind turbine. The nonlinear aeroelastic multi-body dynamic model of the wind turbine is used to generate stress time histories for a set of environmental conditions based on the operational conditions of the wind turbine. The finite element model of the wind turbine is then used to identify fatigue-vulnerable regions in the wind turbine support structure and calculate SCFs for these specific areas. The integration of SCFs into the fatigue calculations reduced the lifespan of the turbine tower by a factor of 4, demonstrating the importance of precisely modelling such local stress concentrations for effective fatigue analysis. A novelty of this work arises in the ability of the finite element model to update the SCFs of the fatigue-prone areas over time as corrosion-induced wastage alters the substructure’s geometry, thereby inducing a global redistribution of stresses. A fatigue analysis is carried out availing of the SCFs which vary annually in addition to the stress-time histories produced by the multi-body dynamic model. The results illustrate that the phenomenon of corrosion thinning induced an 8.9% reduction in the fatigue life of the wind turbine tower, thus emphasising the significant importance of proactive maintenance strategies to mitigate the impact of corrosion.

Proactive System Maintenance: Machine Learning Models for Predicting and Preventing Potential System Failures

Abstract: In modern industrial systems, the prevention of failures and downtime is of paramount importance for ensuring efficiency and productivity. Proactive system maintenance approaches leverage machine learning (ML) models to predict potential failures before they occur, enabling pre-emptive actions to be taken [1]. In this paper, we present a comprehensive review of existing research on proactive system maintenance, focusing on the development and application of ML algorithms for fault prediction and prevention. We discuss various machine learning techniques, data sources, feature engineering methods, and evaluation metrics employed in this domain [8]. Furthermore, we propose novel algorithms and strategies for enhancing the effectiveness of proactive maintenance systems. Through experimentation and case studies, we demonstrate the feasibility and benefits of utilizing machine learning for proactive maintenance in diverse industrial settings.

Influence of IoT implementation on Resource management in construction

The desire to increase resource management efficacy in the construction sector is expanding because of measures to reduce costs, boost productivity, and minimize environmental impact. The Internet of Things (IoT) has the potential to alter resource management in the construction sector by delivering real-time data and insights that may assist decision-makers in optimizing resource allocation and usage. Incorporating Internet of Things (IoT) technology into the construction sector will be investigated in this study to discover how resource management is affected. The aim of the study is to identify the essential aspects that promote optimal IoT integration and to investigate how IoT may influence resource management. The relations between variables and their fundamental elements are investigated using structural equation modelling (SEM). In the context of building projects, the study analyses how IoT integration influences resource allocation and utilization, real-time monitoring, and proactive maintenance. The building sector in Malaysia provides concepts on IoT in resource management. Based on this research's outcomes, there is a distinct association between the utilization of IoT technology and effective resource management in the construction sector. IoT adoption is affected by a multiplicity of issues, including data analytics, data security and privacy, integration and interoperability, scalability, and flexibility. This study contributes to addressing considerable gaps in the corpus of information on IoT technology integration in the construction sector. It analyses how IoT may effect resource management, emphasizing how IoT technology may enhance the efficacy of human, mechanical, and material resources.

AI-enhanced subsea maintenance for improved safety and efficiency: Exploring strategic approaches

As the oil and gas industry increasingly explores deeper and more remote offshore sites, the maintenance of subsea infrastructure becomes paramount. The use of Artificial Intelligence (AI) in subsea maintenance offers promising solutions to enhance safety and efficiency in these challenging environments. This review explores strategic approaches to integrating AI into subsea maintenance operations. AI facilitates predictive maintenance by analyzing vast amounts of data collected from sensors and historical maintenance records. Machine learning algorithms can detect patterns and predict equipment failures before they occur, enabling proactive maintenance scheduling. This predictive capability reduces downtime and minimizes the risk of accidents by addressing potential issues before they escalate. AI-enabled autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) play a crucial role in subsea inspections and repairs. These AI-enhanced robots can navigate complex subsea environments, perform inspections, and execute maintenance tasks with greater precision and efficiency than human divers. By reducing the need for human intervention in hazardous environments, AI-driven AUVs and ROVs significantly improve safety. Furthermore, AI algorithms can optimize maintenance schedules based on factors such as equipment condition, environmental conditions, and operational requirements. By dynamically adjusting maintenance plans, operators can maximize equipment uptime while minimizing costs and risks. This proactive approach ensures that maintenance activities are conducted at the most opportune times, reducing the likelihood of unplanned downtime and improving overall efficiency. Moreover, AI facilitates condition-based maintenance strategies, where equipment health is continuously monitored in real-time. Sensors installed on subsea infrastructure collect data on factors such as temperature, pressure, and vibration, which is then analyzed by AI algorithms to assess equipment condition. By detecting early signs of degradation or malfunction, AI enables timely interventions, preventing costly breakdowns and ensuring optimal performance. In addition to predictive and condition-based maintenance, AI-driven analytics offer insights into operational performance and asset integrity. By analyzing data from various sources, including sensors, historical records, and operational logs, AI can identify trends, anomalies, and optimization opportunities. These insights enable operators to make data-driven decisions that enhance overall system reliability and efficiency. Strategic approaches to implementing AI in subsea maintenance require collaboration between technology providers, operators, and regulatory bodies. Establishing industry standards and guidelines for AI applications in subsea operations is crucial to ensure safety, reliability, and interoperability. Furthermore, investing in research and development to enhance AI algorithms and robotics technology is essential to unlock the full potential of AI in subsea maintenance. AI-enhanced subsea maintenance offers significant benefits in terms of safety and efficiency. By leveraging predictive analytics, autonomous robotics, and real-time monitoring, operators can optimize maintenance activities, reduce downtime, and minimize risks. Strategic approaches to integrating AI into subsea operations require collaboration, investment, and a commitment to advancing technology to meet the challenges of offshore environments.

Advancing maintenance, monitoring, and control techniques for the optimization of power electronic systems

This paper explores the innovative methodologies and technologies in predictive maintenance, real-time monitoring, fault detection, and advanced control strategies for power electronic devices. Initially, we delve into data acquisition and preprocessing techniques crucial for ensuring the quality and reliability of data used in predictive models. These models, leveraging machine learning and time-series analysis, predict the remaining useful life of devices, guiding proactive maintenance strategies. Furthermore, we discuss the significance of risk assessment and decision support systems in prioritizing maintenance tasks and allocating resources efficiently. The narrative then shifts to real-time monitoring and fault detection, emphasizing the role of sensor integration, data fusion, anomaly detection, and diagnostics in maintaining system integrity. Condition-based maintenance strategies, underscored by real-time data analytics, are presented as a means to optimize maintenance activities and enhance operational performance. The paper concludes with a detailed examination of advanced control strategies, including model predictive control, reinforcement learning, and distributed control and optimization techniques, highlighting their potential to improve system efficiency, reliability, and adaptability. Through comprehensive research and analysis, this work aims to provide valuable insights into the development of sophisticated maintenance and control mechanisms for power electronic systems, ultimately contributing to their longevity and operational efficacy.

Enhancing lithium-ion battery lifespan early prediction using a multi-branch vision transformer model

Accurately predicting the lifespan of lithium-ion batteries is crucial for effective battery management systems, particularly for ensuring the safe operation and proactive maintenance of electric vehicles. However, existing methods encounter challenges due to the early weak capacity aging trend in batteries. This study a novel approach using a multibranch vision transformer model to address early stage lifespan prediction issues. The proposed model leverages a multi-input data structure by considering various battery parameters during the charging and discharging phases. By employing distinct branch structures for different inputs, the model can separately extract features from individual input variables. Each vision transformer network was meticulously designed to extract high-dimensional global hidden features from the inputs and integrated to predict the battery lifespan. Comparative analyses against advanced baseline models and existing methods consistently demonstrate the superior performance and robustness of the proposed model. Compared with traditional vision transformer, the proposed model demonstrated a notable reduction in root mean square errors of 13.17 % and 6.32 % on the two public datasets, respectively, indicating the efficacy and reliability of our approach for accurately predicting the lifespan of LIBs during the early stages of capacity decline.