Proactive Maintenance Strategy Research Articles

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 GNSS Deformation Monitoring Forecasting with a Combined VMD-CNN-LSTM Deep Learning Model

Hydraulic infrastructures are susceptible to deformation over time, necessitating reliable monitoring and prediction methods. In this study, we address this challenge by proposing a novel approach based on the combination of Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) methods for Global Navigation Satellite Systems (GNSS) deformation monitoring and prediction modeling. The VMD method is utilized to decompose the complex deformation signals into intrinsic mode functions, which are then fed into a CNN method for feature extraction. The extracted features are input into an LSTM method to capture temporal dependencies and make predictions. The experimental results demonstrate that the proposed VMD-CNN-LSTM method exhibits an improvement by about 75%. This research contributes to the advancement of deformation monitoring technologies in water conservancy engineering, offering a promising solution for proactive maintenance and risk mitigation strategies.

A REVIEW ON TOTAL PRODUCTIVE MAINTENANCE (TPM)

Total Productive Maintenance (TPM) is transforming manufacturing landscapes. This review investigates its impact, revealing how TPM fosters a culture of continuous improvement through employee involvement and proactive maintenance strategies. By analyzing 30 research papers, it unveils the potent link between TPM and increased productivity alongside reduced waste. Highlighting Overall Equipment Effectiveness (OEE) as a crucial metric, the review emphasizes the importance of 5S as a foundational step for successful TPM implementation. With global adoption surging, TPM is solidifying its position as a world-class strategy for achieving peak manufacturing performance." Keywords: Total Productive Maintenance (TPM), OEE, Improve Productivity, 5S.

Investigating the Effects of Ageing on Transmission System Dependability Through the Use of an Artificial Neural Network

As power transmission systems age, their dependability becomes increasingly critical for maintaining reliable electricity supply. However, accurately predicting the impact of ageing on system dependability remains a challenging task due to the complex interactions among various components and environmental factors. In this study, we propose the utilization of an artificial neural network (ANN) approach to investigate the effects of ageing on transmission system dependability. The ANN model is trained on historical data encompassing a wide range of parameters including equipment age, maintenance records, environmental conditions, and system performance metrics. By capturing the nonlinear relationships within the data, the ANN can effectively model the intricate dependencies between ageing and system dependability. Through comprehensive simulations and sensitivity analyses, we demonstrate the capability of the ANN to forecast the degradation of transmission system dependability over time. Moreover, the model enables the identification of key factors driving system reliability decline, thereby facilitating proactive maintenance strategies and resource allocation.

Enhancing Interpretability in Drill Bit Wear Analysis through Explainable Artificial Intelligence: A Grad-CAM Approach

This study introduces a novel method for analyzing vibration data related to drill bit failure. Our approach combines explainable artificial intelligence (XAI) with convolutional neural networks (CNNs). Conventional signal analysis methods, such as fast Fourier transform (FFT) and wavelet transform (WT), require extensive knowledge of drilling equipment specifications, which limits their adaptability to different conditions. In contrast, our method leverages XAI algorithms applied to CNNs to directly identify fault signatures from vibration signals. The signals are transformed into their frequency components and then employed as inputs to a CNN model, which is trained to detect patterns indicative of drill bit failure. XAI algorithms are then employed to generate attention maps, highlighting regions of interest in the CNN. By scrutinizing these maps, engineers can identify critical frequencies associated with drill bit failure, providing valuable insights for maintenance and optimization. This method offers a transparent and interpretable framework for analyzing vibration data, enabling informed decision-making and proactive maintenance strategies to enhance drilling efficiency and minimize downtime. The integration of XAI with CNNs facilitates a deeper understanding of the root causes of drill bit failure and improves overall drilling performance.

AI-Powered Innovations in Electrical Engineering: Enhancing Efficiency, Reliability, and Sustainability

Artificial Intelligence (AI) has emerged as a transformative force in the field of Electrical Engineering, revolutionizing traditional practices and unlocking unprecedented possibilities. This research paper investigates the integration of AI-powered innovations to enhance efficiency, reliability, and sustainability within electrical engineering systems. Through a comprehensive review of existing literature, this study delves into key applications of AI, including predictive maintenance, optimal resource allocation, and fault detection, among others. Utilizing advanced machine learning algorithms and data analytics techniques, AI facilitates real-time decision-making processes, enabling proactive maintenance strategies and optimizing system performance. Moreover, AI-driven approaches contribute to the enhancement of reliability by predicting potential failures and implementing pre-emptive measures, consequently reducing downtime, and improving operational continuity. Furthermore, the implementation of AI in electrical engineering fosters sustainability by optimizing energy consumption, mitigating environmental impacts, and facilitating the integration of renewable energy sources into power grids. By leveraging AI technologies, electrical engineering systems can adapt to dynamic operational conditions, maximize resource utilization, and minimize environmental footprints, thereby paving the way for a more efficient, reliable, and sustainable future. This paper underscores the transformative potential of AI in shaping the landscape of electrical engineering and provides insights into future research directions to harness the benefits of AI-powered innovations further.

Impacts of climate change on environmental and economic sustainability of flexible pavements across China

Pavements are susceptible to accelerated deterioration due to changing climate conditions, leading to increased maintenance and excess fuel consumption through pavement-vehicle interaction. China's diverse climates raise concerns about the environmental and economic sustainability of flexible pavements amid climate change and the effectiveness of preventative maintenance strategies. This study examines climate change's potential impacts on long-term pavement performance, greenhouse gas (GHG) emissions, and costs, employing the Mechanistic-Empirical Pavement Design Guide method. The calibrated World Bank's Highway Development and Management Model 4 assesses the impacts of surface characteristics on vehicle fuel consumption. Life cycle assessment and life cycle cost analysis quantify GHG emissions and costs. Results reveal significant pavement deterioration in Southeast and Central China. Preventative maintenance strategies reduce fuel consumption, with GHG emissions and cost savings from smoother driving conditions outweighing those from maintenance. These insights stress the importance of proactive maintenance strategies for mitigating climate-induced deterioration and enhancing sustainability.

Implementation of Health Monitoring System of Induction Machine Using Computational Intelligence

The induction machine is a critical component in various industrial applications, and its reliable operation is paramount for uninterrupted processes. To ensure the continuous performance and prevent unexpected failures, the implementation of an effective health monitoring system is imperative. In this study, we propose a novel approach to monitor the health of induction machines using computational intelligence techniques. The proposed system integrates advanced computational intelligence algorithms, including machine learning and signal processing methods, to analyze the operational data and identify potential faults or anomalies in the induction machine. Specifically, we employ techniques such as artificial neural networks, support vector machines, and genetic algorithms to process the sensor data acquired from the induction machine. The health monitoring system operates in real-time, continuously analyzing various parameters such as current, voltage, temperature, and vibration patterns to detect deviations from normal operating conditions. By establishing baseline performance profiles and employing pattern recognition algorithms, the system can identify early signs of degradation or impending failures, allowing for proactive maintenance interventions. Moreover, the proposed system offers flexibility and scalability, enabling adaptation to different types of induction machines and operating environments. It facilitates remote monitoring capabilities, enabling maintenance personnel to access real-time health status and diagnostic information from anywhere, facilitating timely decision-making and proactive maintenance strategies.

Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance

In this study, we leverage SCADA data from diverse wind turbines to predict power output, employing advanced time series methods, specifically Functional Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks. A key innovation lies in the ensemble of FNN and LSTM models, capitalizing on their collective learning. This ensemble approach outperforms individual models, ensuring stable and accurate power output predictions. Additionally, machine learning techniques are applied to detect wind turbine performance deterioration, enabling proactive maintenance strategies and health assessment. Crucially, our analysis reveals the uniqueness of each wind turbine, necessitating tailored models for optimal predictions. These insight underscores the importance of providing automatized customization for different turbines to keep human modeling effort low. Importantly, the methodologies developed in this analysis are not limited to wind turbines; they can be extended to predict and optimize performance in various machinery, highlighting the versatility and applicability of our research across diverse industrial contexts.

A Framework for Managing and Monitoring the Battery Health Parameters of Rental Electric Vehicles

The rapid adoption of electric vehicles (EVs) has paved the way for a sustainable transportation future. However, ensuring the safety and reliability of rental EVs, particularly regarding their battery health, presents unique challenges. This paper proposes a comprehensive framework for monitoring and managing rental EV batteries, encompassing real-time data acquisition, advanced analytics, and proactive maintenance strategies. By implementing such a framework, rental EV providers can optimize battery performance, extend lifespan, and guarantee the safety and convenience of their customers. The surging popularity of rental electric vehicles (EVs) poses unique challenges in ensuring their safety and reliability, particularly concerning battery health. This paper presents a comprehensive framework for proactive monitoring and management of rental EV batteries. The framework encompasses real-time data acquisition of key battery parameters, advanced analytics for predicting degradation and identifying influencing factors, and data-driven maintenance strategies like cell balancing and optimized charging. Implementing this framework empowers rental EV providers to maximize battery lifespan, enhance customer experience, and contribute to a sustainable transportation future. This leads to improving the factors and the process to monitor the battery health parameters of the rental electric vehicle. By adopting the various monitoring algorithms to measure and monitor the battery health parameters such as State of Charge, State of Health, battery voltage, balancing current, and temperature are monitored by the lifespan of the battery is extended and the safety and reliability of the battery is ensured by monitoring the temperature.

THEORETICAL FRAMEWORKS OF ECOPFM PREDICTIVE MAINTENANCE (ECOPFM) PREDICTIVE MAINTENANCE SYSTEM

The Frameworks of EcoPFM Predictive Maintenance (PM) System presents a novel approach to maintenance optimization within eco-friendly power facilities, addressing the critical need for sustainable, efficient asset management. This paper introduces an integrated framework leveraging advanced predictive analytics, machine learning algorithms, and Internet of Things (IoT) technology to enable proactive maintenance interventions based on real-time data insights. Focusing on the context of the United States it highlights the significance of implementing such a system in the realm of eco-friendly energy infrastructure. The automotive and heavy-duty truck industries in the United States grapple with the challenge of optimizing maintenance strategies to ensure vehicle reliability, safety, and environmental sustainability. Traditional maintenance approaches, primarily reactive or scheduled maintenance, fall short in addressing the complexities of modern vehicle operations. The U.S. Department of Transportation reports that heavy-duty trucks transport approximately 70% of the nation's freight by weight, underscoring the sector's critical role in the economy. However, inefficiencies in maintenance strategies contribute to significant economic and operational setbacks. According to the American Transportation Research Institute, unscheduled truck maintenance and repairs are leading operational costs for fleets, with an average expense of 16.7 cents per mile in 2020, highlighting the financial strain of current maintenance practices. In the United States, the demand for eco-friendly power solutions is rapidly increasing, driven by a growing awareness of environmental sustainability and the imperative to reduce carbon emissions. As the nation transitions towards renewable energy sources and eco-friendly power facilities, the effective management of these assets becomes paramount to ensuring reliability, performance, and longevity. The EcoPFM PM System integrates diverse data sets sourced from eco-friendly power facilities across the USA, encompassing historical operational data, sensor readings, and environmental parameters. Through predictive analytics, the system identifies patterns and trends within these data sets to forecast equipment failures and performance degradation accurately. By prioritizing maintenance tasks based on risk assessment models and condition monitoring, the system enables organizations to optimize resource allocation, minimize downtime, and extend asset lifespan. Embracing the Frameworks of EcoPFM Predictive Maintenance System holds immense promise for organizations operating eco-friendly power facilities in the United States. By harnessing data-driven insights and proactive maintenance strategies, this system offers a pathway towards enhanced operational efficiency, cost reduction, and sustainability, ultimately contributing to the advancement of eco-friendly energy infrastructure in the nation.
 Keywords: Predictive Maintenance, System, ECOPFM, Technology.

Evaluating the Impact of Transitioning Maintenance Strategy from Reactive to Proactive in Power Generation Companies: An Empirical Analysis

This empirical study rigorously investigates the impact of transitioning from a reactive maintenance strategy to a proactive approach within the context of power generation companies. The central aim is to quantify and provide a comparative analysis of the efficiency, cost implications, and overall operational impact of adopting proactive versus reactive maintenance strategies in a power plant setting. Drawing on meticulously collected data, the research considers an array of key performance indicators, including maintenance costs, equipment breakdowns, downtime duration, total power output, equipment lifespan, safety incidents, regulatory compliance violations, and investment in staff training and predictive maintenance tools. The findings of this study are both revealing and quantitatively substantial. A transition to a proactive maintenance strategy has demonstrated a reduction in maintenance costs by approximately 20%, coupled with a 35% decrease in the number of equipment breakdowns. Downtime duration was significantly reduced by 40%, enhancing operational efficiency and power output. Notably, the total power output increased by 15%, and the equipment lifespan was extended by an average of 25%. Furthermore, a marked decrease of 50% in safety incidents was observed, reflecting the profound impact of proactive strategies on enhancing safety protocols. However, these improvements are juxtaposed with an initial investment surge, where staff training costs increased by 30%, and expenditure on predictive maintenance tools rose by 25%. This research underscores the critical importance of a comprehensive and quantified understanding of maintenance strategies and their broader impacts on power plant performance. The study illustrates that while proactive maintenance demands initial investments, the long-term benefits significantly outweigh these costs, leading to enhanced operational efficiency, safety, and cost-effectiveness. The insights gleaned from this study provide invaluable guidance for power plant operators, stakeholders, and policymakers in their pursuit to optimize operations, improve safety standards, and achieve economic efficiencies, thereby advocating for a strategic shift towards more proactive maintenance approaches in power plant operations.

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.

Strategic Maintenance Boosts Global Power Plant Efficiency and Safety

This study addresses the maintenance of the Main Cooling Water Pump (MCWP) in Gas and Steam Power Plants, crucial for performance and sustainability. Existing practices often lack a comprehensive approach, integrating routine inspections with proactive maintenance strategies. We aimed to develop a maintenance framework that improves operational efficiency, safety, and environmental sustainability. Over a 12-month period, data from various facilities indicated that our holistic approach increased pump efficiency by 15%, reduced downtime by 20%, and enhanced compliance with safety and environmental standards. These findings underscore the importance of integrated maintenance practices in responding to evolving industry challenges and regulations. Highlights: Holistic maintenance approach vital for MCWP performance. Safety compliance crucial for personnel and asset protection. Maintenance enhances efficiency and sustainability efforts. Keywords: Maintenance, Main Cooling Water Pump, Energy Efficiency

Remaining Useful Life Estimation of MoSi2 Heating Element in a Pusher Kiln Process.

The critical challenge of estimating the Remaining Useful Life (RUL) of MoSi2 heating elements utilized in pusher kiln processes is to enhance operational efficiency and minimize downtime in industrial applications. MoSi2 heating elements are integral components in high-temperature environments, playing a pivotal role in achieving optimal thermal performance. However, prolonged exposure to extreme conditions leads to degradation, necessitating precise RUL predictions for proactive maintenance strategies. Since insufficient failure experience deals with Predictive Maintenance (PdM) in real-life scenarios, a Generative Adversarial Network (GAN) generates specific training data as failure experiences. The Remaining Useful Life (RUL) is the duration of the equipment's operation before repair or replacement, often measured in days, miles, or cycles. Machine learning models are trained using historical data encompassing various operational scenarios and degradation patterns. The RUL prediction model is determined through training, hyperparameter tuning, and comparisons based on the machine-learning model, such as Long Short-Term Memory (LSTM) or Support Vector Regression (SVR). As a result, SVR reflects the actual resistance variation, achieving the R-Square (R2) of 0.634, better than LSTM. From a safety perspective, SVR offers high prediction accuracy and sufficient time to schedule maintenance plans.

A novel exponential model for tool remaining useful life prediction

Implementing proactive maintenance strategies based on condition prediction for cutting tools can reduce expensive, unscheduled maintenance events. This work proposes an novel exponential model to predict the Remaining Useful Life (RUL) of cutting tools. Firstly, a new monitoring indicator named second-order derivative of health index (SDHI) is constructed, and on this basis, a 3σ interval-based first predicting time (FPT) adaptive selection method is proposed to correlate the observable SDHI with the unobservable tool wear rate, automatically determines the abnormal tool wear state without human intervention. Secondly, the integration of the Bayesian inference mechanism with the expectation maximization (EM) algorithm enables the achieving of real-time iterative updates for model parameters. Thirdly, To reduce stochastic errors while predicting the RUL, particle filtering and probability density function (PDF) are applied to handle prediction uncertainty. The experimental findings obtained from the milling experiments demonstrate that the proposed model exhibits robust adaptability to various cutting conditions, thereby leading to enhanced RUL prediction performance.

Assessment of physical condition and status of maintenance of public primary schools infrastructure in Port Harcourt metropolis

This study presents a comprehensive assessment of the physical condition and maintenance status of public primary school infrastructure in Port Harcourt Metropolis. The investigation delves into various aspects, including the establishment and age of schools, building components, furniture conditions, security infrastructure, basic amenities, and environmental elements. The majority of schools were established after 1960, with a notable concentration of building construction between 2010-2020, indicating recent infrastructural development. While essential components such as classrooms, administrative offices, auditoriums, libraries, and ICT facilities are universally present, variations in the conditions of roofs, ceilings, and electricity supply from the national grid emphasize the need for targeted maintenance efforts. The study identifies a commendable commitment to transparency and accountability, as evidenced by the reporting of infrastructure conditions to higher authorities. However, a significant percentage reporting no major maintenance in the past 5 years raises concerns about the longevity of infrastructure. Recommendations include prioritizing maintenance on critical components, strategic investments in security infrastructure, and fostering sustainability through ongoing maintenance practices. The findings underscore the importance of a multifaceted approach to infrastructure management, aiming for a conducive and sustainable learning environment in public primary schools. The commitment to transparency, coupled with proactive maintenance strategies, is essential for fostering an environment conducive to effective teaching and learning, ultimately contributing to the overall well-being of the educational system in Port Harcourt Metropolis.

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.

Residual LSTM-based short duration forecasting of polarization current for effective assessment of transformers insulation

The empirical application of polarization and depolarization current (PDC) measurement of transformers facilitates the extraction of critical insulation-sensitive parameters. This technique, rooted in time-domain dielectric response analysis, forms the bedrock for parameterization and insulation modeling. However, the inherently time-consuming nature of polarization current measurements renders them susceptible to data corruption. This article explores deep-learning-based short-duration techniques for forecasting polarization current to address this limitation. By incorporating spatial shortcuts, the residual long short-term memory (LSTM) network facilitates the seamless propagation of spatial and temporal gradients. Furthermore, the relative forecasting assessment of the proposed residual LSTM model’s performance is made against traditional LSTM, attention LSTM, gated recurrent units (GRU), and convolutional neural network (CNN) models. Thus, optimal model selection strategies are evaluated based on their capability to capture extended dependencies and short-term information present in the data. In addition, the Monte Carlo dropout prediction is employed to estimate uncertainty in polarization current forecasts. The findings demonstrate that the proposed residual LSTM network model for polarization current forecasting yields the lowest error metrics and maintains prediction consistency over the testing duration. Thus, the proposed approach significantly reduces PDC measurement time, providing an effective means to develop proactive maintenance strategies for evaluating the insulation condition of transformers.

ИСПОЛЬЗОВАНИЕ ШТАТНОЙ ОСНАСТКИ ТОКАРНОГО СТАНКА ДЛЯ АНАЛИЗА ВИБРАЦИОННОГО ФОНА ПРОЦЕССА РЕЗАНИЯ

Predictive maintenance is a proactive maintenance strategy that uses condition monitoring tools to detect signs of damage, abnormalities, and equipment performance. The most commonly used method for rotating machines is vibration analysis. The most commonly used method for condition analysis of rotating parts is vibration analysis. Most signal analysis tools today use the fast Fourier transform (FFT), which is a special case of the generalized discrete Fourier transform and converts a vibration signal from its time domain representation to an equivalent frequency domain representation. This article focuses on the use of discrete Fourier transform for machining signal analysis.