Equipment Breakdown Research Articles

Interdisciplinary Research for Predictive Maintenance of MRI Machines Using Machine Learning

Predictive maintenance is crucial for ensuring the reliability and availability of medical equipment, particularly MRI machines in healthcare facilities. This study presents a comprehensive approach to predictive maintenance of MRI machines using machine learning techniques. The objective of this research is to develop and evaluate predictive models capable of identifying patterns and indicators of impending equipment failures, thereby improving the operational efficiency and reliability of MRI machines. We utilized a dataset comprising historical maintenance records, sensor readings, and environmental conditions collected from three 1.5 T Siemens MRI machines at MGM Hospital, Warangal, Telangana, India. The dataset, initially consisting of 96 records and expanded to 1000 through computer-generated data, encompasses various operational aspects, including temperature, humidity, vibration, power consumption, and coolant flow rate. This study investigated the efficacy of multiple machine learning algorithms for predicting equipment failures, including Random Forest, Gradient Boosting, Long Short-Term Memory (LSTM) networks, and Support Vector Machines (SVM). Model performance was evaluated using standard metrics such as F1-score, accuracy, recall, and precision. Results indicate that LSTM networks achieved the highest accuracy at 89%, while SVM displayed the lowest at 82%. These findings validate the potential of machine learning in anticipating equipment breakdowns and enabling proactive maintenance strategies for MRI machines. The outcomes of this research have significant implications for enhancing the reliability and operational efficiency of medical imaging equipment in healthcare settings.

Experiences of women living with cervical cancer in Ghana: challenges and coping strategies

BackgroundCervical cancer is a leading cause of morbidity and mortality among women globally. The condition is both preventable and treatable yet remains a leading cause of cancer death in Ghana. This study aims to explore the unique experiences of women living with cervical cancer with a focus on the challenges and coping strategies.MethodsThe study employed a qualitative approach with an exploratory, descriptive design. This study was conducted among women with cervical cancer aged 18 years and above who have been diagnosed with the disease for at least 3 months. A total of 16 participants were purposively sampled based on the eligibility criteria and individually interviewed using a semi-structured guide. The six-step technique for qualitative analysis by Braun and Clarke guided data analysis.ResultsMost participants resorted to self-medication, over-the-counter drugs and herbal preparations as a first line of defense against the disease. The presence of cervical cancer affected participants’ physical and mental well-being. Other challenges included financial burden and frequent equipment breakdowns which affected patients’ treatment. Participants adopted different coping strategies such as taking blood tonic, increased rest and sleep, spiritual prayers and recreational activities. Family members, friends, the church and health workers provided support in the area of finance, advice, meal preparation and house chores, prayers and counseling to participants.ConclusionsCervical cancer affects the quality of life of many women and their significant others. The condition puts a lot of financial burden on its victims and there is the need for a system to reduce the burden on patients. It is recommended that the treatment of cervical cancer should be covered by the National Health Insurance Scheme to ease the financial burden on patients. There is a need for expansion of access to cervical cancer treatment across the country to reduce patients’ burden and relieve the pressure on the few pieces of equipment at the current treatment centres.

HIGH-TEMPERATURE EPOXY-BASED COMPOSITES

Oil-well equipment for oil production operates under complex and changing circumstances. Well pressure and temperature can reach 100 MPa and 200 °С. Chemicals are pumped into oil-wells to enhance oil yield. However, this leads to an accelerated corrosion and contributes to the equipment breakdown. Adverse effects on the equipment can be decreased through use of protecting coatings with multi-function properties.Epoxy coatings are popular. However, they are limited by operating temperature of 130 °C. If the temperature is exceeded, epoxy coatings lose their protective properties. However, researches claim that the heat resistance can be modified through epoxy resin fillers. This allows to create new composites with advanced properties. To create composites that can function under an elevated temperatures we need to research the components selection further.The article describes how the composite components influence its heat resistance. The research is based on the Wick's method. The composites matrix is ED-16, ED-20 and ED-22 epoxy-diane resin. The hardeners are PO-300, HT-450 and PEPA. The fillers are carbon nanotubes, carbon fiber, carbonaceous filament and graphene. If we add fillers to the composite based on ED-22 and HT-450 hardener, the heat resistance doubles compared to the unfilled cured resin. It is hypothesised that the epoxy free groups or hardener's free aminogen groups effect the heat resistance, because these groups can react with the fillers and result its spacious structure. This effects the composites properties.The findings serve as the basis for developing composite coatings for oil-well equipment to protect it against harsh environment.

Experimental Outdoor Vehicle Acoustic Testing Based on ISO-362 Pass-by-Noise and Tyre Noise Contribution for Electric Vehicles

This paper focuses on the novel and unique training provision of acoustics relevant for noise, vibration, and harshness (NVH), focused on the ISO-362 standard highlighting important design aspects for electric vehicles. A case study of the practical implementation of off-site vehicle testing supporting an acoustics module is described, detailing a time-constrained test for automotive pass-by-noise and tyre-radiated noise with speed. Industrial test standards are discussed, with education as a primary motivation. The connections between low-cost, accessible equipment and future electric vehicle acoustics are made. The paper contains a full equipment breakdown to demonstrate the ability to link digital data transfer, analogue-to-digital communication, telemetry, and acquisition skills. The benchmark results of novel pass-by-noise and tyre testing are framed around discussion points for assessments. Inexpensive Arduino Uno boards provide data acquisition with class 1 sound pressure meters, XBee radios provide telemetry to a vehicle, and a vehicle datalogger provides GPS position with CANBUS data. Data acquisition is triggered through the implementation of light gate sensors on the test track, with the whole test lasting 90 minutes.

MAINTENANCE PRIORITIZATION FOR AN INDUCTION FURNACE WITH ANALYTICAL HIERARCHY PROCESS IN PT. LINGGA SAKTI INDONESIA

Abstract: The effective operation of induction furnaces in steel casting is crucial for maximizing production efficiency, yet equipment breakdowns remain a significant concern for manufacturers like PT. Lingga Sakti Indonesia. Despite existing maintenance strategies, there is a need for a more systematic approach to enhance Overall Equipment Effectiveness (OEE). This study addresses the knowledge gap in maintenance system optimization by employing the Analytical Hierarchy Process (AHP) to prioritize predictive maintenance tasks. The aim of this research is to assess the current OEE, calculated at 73.26%, and propose an updated maintenance framework based on the AHP findings, which involved input from four respondents across six criteria and nine alternatives. The results indicate that electrical monitoring emerged as the highest-ranked maintenance task, suggesting its implementation as a critical strategy for improving equipment reliability. The novelty of this approach lies in integrating AHP for maintenance decision-making, offering a quantifiable method for prioritizing tasks based on expert criteria. The implications of this study are significant, as the recommended maintenance system could enhance OEE and operational efficiency, ultimately leading to reduced downtime and improved productivity. Future research should focus on the practical implementation of these proposals and their impact on long-term maintenance effectiveness within the steel casting sector.

Features of the use of big data in the financial sector

The purpose of the article is to theoretically comprehend the essence of big data, identify the advantages and risks of its use by financial organisations. The article presents the results of systematisation of knowledge about the nature and features of the big data. It has been revealed that the technology makes it possible to do better analytical research, create models for forecasting economic trends and market changes, study market dynamics, analyse medical data to improve diagnosis and choice of treatment methods, predict failure or breakdown of equipment in production by assessing data from sensors, develop social and economic programmes at the state level, identify fraud and corruption in the financial sector, etc. The relevance of the rapid development of the big data technology and the expediency of its use in this sphere is substantiated. As a result of the analysis of scientific literature, the author’s definition of the technology in the financial sector is presented. Its novelty lies in considering the features and advantages of applying the big data by financial organisations. The study of the modern practice of their usage in these institutions has revealed the main strengths and disadvantages of the technology in question.

Features of display in account of compensation of losses from written-off stocks during the period of martial state

The paper is devoted to the issues of reflecting in the accounts compensation for losses from written-off stocks during the period of martial law. The relevance of this direction of research is due to the fact that, unfortunately, at enterprises in places where assets are stored, processes of theft, improper storage and deterioration of stocks sometimes occur, as a result of which business entities suffer losses. Therefore, the use of compensation mechanisms by the guilty parties (employees of the enterprise, the aggressor country) and their reflection in accounting lead to a decrease in costs during the write-off of stocks from the enterprise's balance sheet. Every enterprise in Ukraine, during its activity, necessarily records stocks that can be used in production, trade, administration or in other areas of work. Special attention is paid to the places of their storage during the receipt of these assets. We believe that the data of warehouse accounting of stocks should coincide with the data of accounting. But, unfortunately, there are situations in which, after an inventory or military aggression, it is necessary to write off undetected (stolen), damaged or destroyed stocks. In today's conditions, Ukrainian enterprises suffer significant losses as a result of martial law. The use of unmanned aerial vehicles and missiles by Russia, the occupation of a large part of Ukrainian territories, the location of enterprises in war zones or close to them leads to the loss of enterprise assets. We have researched that these losses can occur as a result of direct ammunition hits, fires, and breakdown of equipment used to preserve stocks. Unfortunately, the loss and deterioration of stocks leads to the mismatch of their recognition as assets and the subsequent receipt of economic benefits.

Multi-agent deep reinforcement learning for dynamic reconfigurable shop scheduling considering batch processing and worker cooperation

Reconfigurable manufacturing system is considered as a promising next-generation manufacturing paradigm. However, limited equipment and complex product processes add additional coupled scheduling problems, including resource allocation, batch processing and worker cooperation. Meanwhile, dynamic events bring uncertainty. Traditional scheduling methods are difficult to obtain good solutions quickly. To this end, this paper proposes a multi-agent deep reinforcement learning (DRL) based method for dynamic reconfigurable shop scheduling problem considering batch processing and worker cooperation to minimize the total tardiness cost. Specifically, a dual-agent DRL-based scheduling framework is first designed. Then, a multi-agent DRL-based training algorithm is developed, where two high-quality end-to-end action spaces are designed using rule adjustment, and an estimated tardiness cost driven reward function is proposed for order-level scheduling problem. Moreover, a multi-resource allocation heuristics is designed for the reasonable assignment of equipment and workers, and a batch processing rule is designed to determine the action of manufacturing cell based on workshop state. Finally, a strategy is proposed for handling new order arrivals, equipment breakdown and job reworks. Experimental results on 140 instances show that the proposed method is superior to scheduling rules, genetic programming, and two popular DRL-based methods, and can effectively deal with various disturbance events. Furthermore, a real-world assembly and debugging workshop case is studied to show that the proposed method is applicable to solve the complex reconfigurable shop scheduling problems.

Assessment of Overall Treatment Time in Curative Radiotherapy

In radiotherapy, spread is a radiobiological parameter that corresponds to the total duration of treatment expressed in days. The objective of the study is to evaluate adherence to the theoretical spread among patients receiving curative irradiation.This was a prospective study conducted within the radiotherapy department at the National Oncology Institute of Rabat, including 285 patients receiving curative irradiation, regardless of tumor localization, from December 1, 2021, to February 28, 2022. Reasons for missed sessions were collected during end-of-treatment consultations.The median age of our patients was 47 years (range: 36-78 years), with 62% being female and 38% male. Among them, 47.3% were employed. Transportation methods varied; public transportation accounted for 59.2%, taxis for 9.1%, and only 8.7% used personal transportation. Travel time from home to the oncology institute was less than 20 minutes for 12.6% of patients, between 20 and 60 minutes for 75.7%, and over 60 minutes for 11.7%. The most common tumor sites were the breast in 32.2% of cases and the prostate in 23.5% of patients. The theoretical spread was adhered to in 29.8% of cases. In 56.4% of cases, the extension was between 1 and 5 days, and in 13.3% of cases, it was between 6 and 12 days.Causes of spread extension were diverse, with equipment breakdowns and maintenance accounting for 52.1% of cases, intercurrent illness for 10%, holidays for 16%, and treatment refusal for 0.6%. Evaluation of spread in radiotherapy is crucial as it represents a significant factor in local recurrence and indirectly affects distant progression, especially with prolonged treatment durations.

Production risk management of prawn crackers using fuzzy FMEA and fuzzy AHP methods

Prawn crackers hold a significant position within Indonesia's agro-industrial product landscape. The industrial process involved in their production is fraught with potential risks that could result in substantial losses for companies. PT. XYZ, a prominent prawn cracker producer in Indonesia, grapples with various risks, including non-compliant raw material supplies, machinery and equipment breakdowns, product defects, and process failures. Hence, there is an imperative need for comprehensive risk management involving further analysis, identification of potential failure modes, and formulation of mitigation strategies to curb losses. This research aims to ascertain the priority of potential failure modes and their corresponding mitigation strategies in the prawn cracker production process at PT. XYZ. The study employs risk analysis through the Fuzzy Failure Mode and Effect Analysis (Fuzzy-FMEA) method and determines mitigation strategies using the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) method. The findings reveal 11 identified failure modes in PT. XYZ's prawn cracker production process, with machinery and equipment damage emerging as the top-priority failure mode, giving a Fuzzy Risk Priority Number (FRPN) value of 7.620. The recommended mitigation strategy involves intensifying inspections of production machinery and equipment, both before and after use by workers, with a weight of 0.368.

A data quality management framework for equipment failure risk estimation: Application to the oil and gas industry

In engineering asset management, accurate failure risk estimation is essential for averting equipment breakdowns and optimizing risk-based maintenance strategies. Data quality and model fitting are two critical sources of prediction uncertainty in risk estimation. While much attention has been devoted to model fitting for risk estimation, the critical role of data quality has often been overshadowed. To bridge this research gap, this paper presents a novel data quality management framework tailored for industrial equipment failure risk estimation. The framework covers the steps from data to model. It consists of the following main phases: data development, data quality assessment, data quality requirement decision-making, data quality improvement, and risk estimation model development. The framework provides detailed guidelines that can facilitate data practitioners to build individualized data quality requirement decision-making models for failure risk estimation of their equipment. The decision-making model can measure the adequacy of existing data to build a risk estimation model that meets the specified requirements and further determines the best risk estimation model given the available data. A case study using actual data collected during oil well drilling operations from multiple oil fields demonstrates the practicality and effectiveness of the framework. In this case study, four risk estimation models are compared, including two baseline models (mean time to failure and median time to failure) and two machine-learning models (quantile regression and hidden Markov model). In addition, a decision tree-based decision model is developed to determine whether the data quality meets the requirements and the best risk estimation model in case the data quality meets the requirements.

Application of the FMECA method on the electro-hydraulic system for drilling machine type NKH45

This article focuses on the implementation of a preventive maintenance strategy, responding to a major challenge: the search for optimization in minimizing downtime of the radial drilling machine (NKH45). Crucially positioned at the heart of the mechanical manufacturing process, this machine holds significant influence on overall production performance and availability. In consideration of the strategic importance of this equipment within the framework of the mechanical manufacturing activity, an advanced methodology, in particular FMECA (Failure Mode Effects and Criticality Analysis), is implemented. FMECA represents one of the most recommended approaches in this context, based on reliability, allowing the meticulous identification of the main causes of functional failures of equipment. FMECA is a risk prevention method. It is used to improve the reliability of a product, a process or even a means of production. It makes it possible to assess the criticality of potential failures in a system. According to the recommendations of the maintenance department of the company specializing in the construction of agricultural equipment, this study will focus on a subsystem of the electro-hydraulic part of the NKH45 radial drilling machine, we use the FMEA Analysis method (Failure Mode Effects and Criticality Analysis). Thanks to this approach, it is possible to identify the main reasons for equipment breakdowns that affect production and to classify these breakdowns in order to develop an optimal maintenance strategy aimed at: reducing the number of breakdowns, preventing breakdowns, improving the preventive maintenance, reduce downtime, improve corrective maintenance, therefore the ultimate objective of this approach is to design an optimal maintenance action plan in order to significantly minimize downtime, which slows down production flow.

308 Awardee Talk: Prevention and mitigation of pathogens in feed and feed manufacturing systems

Abstract Prior to 2013, pathogen transmission through feed was focused primarily on bacteria and biosecurity was used to prevent adulterant entry into the ingredients and the feed mills. Following the porcine epidemic diarrhea virus (PEDV) outbreak in 2013, research focused on pathogen mitigation and control quickly became a subject of interest. Since PEDV, multiple endemic viruses and foreign animal diseases have been found to be transmissible in feed or feed ingredients. As African swine fever virus (ASFV) spread closer to the United States and endemic pathogens are a continued pressure on production systems, it is imperative that prevention measures are included in the feed supply chain and options for mitigation are fully elucidated for times of need. Previous research has demonstrated that preventative measures such as boot baths, lines of separation, showers, and changing clothes are effective measures when appropriately maintained. However, these protocols can seem costly and burdensome to employees, especially when the risk of disease transmission appears low. Employee training is difficult as disease transmission is difficult to visualize and exhibit the importance of each measure. Glo Germ, a florescent substance, is a cost-effective training tool which can demonstrate the efficacy of prevention protocols and can identify areas where biosecurity measures have not been properly utilized. Preventative measures are the first line of defense for feed manufacturing systems, but understanding and using mitigation techniques in the event of contamination can limit pathogen spread and protect the rest of the feed supply chain. Pelleting, as a method of thermal inactivation process, has recently been demonstrated to lessen the quantity of detectable viral RNA and decrease the risk of infection. The pelleting process, however, is a point-in-time mitigant and does not decrease the risk of product re-contamination as feed continues down the supply chain. The use of chemical mitigants, like formaldehyde, medium-chain fatty acids, and organic acids, can decrease the presence of bacterial and viral pathogens with longer-lasting mitigation properties. These mitigants can either be used in the feed itself, or as an addition to flushing batches which help disperse the chemical mitigants throughout the feed manufacturing equipment, thus reducing the risk of product re-contamination. In the event more drastic measures must be taken to eliminate pathogen presence from feed mills, gaseous fumigation and the use of portable heaters show promise as methods to decontaminate feed manufacturing equipment without the use of water or complete equipment breakdown. Overall, effective training and implementation of preventative measures are needed to provide pathogen-free feed to production systems, but continued research in mitigation and mill decontamination will further protect the feed supply chain in the event of pathogen contamination.

The real cost

Nicky Varney emphasises the real costs of equipment breakdown.

Factor analysis method for delay dynamics in Nigerian Tin Can Island Port for sustainable development

The effect of efficient and effective port system on the economy viability of any nation cannot be over emphasis. This study investigated factors that could be termed as delay causative factors in Nigerian ports industry using TIN CAN Island port complex as a case study. A well-structured questionnaire was developed for this research and 72 respondents, who are port users (custom officers, employees, and customers), were asked to rank the variables perceived to be causing delay at the port according to their respective perception of the effect of each factor on delay factors at the port. The validity of the research instrument was done using Cronbach Alpha. There are several factors that were thought to be working together in reducing efficiency in Nigerian ports and these factors were analyzed using factor analysis method. It was discovered from the analysis that Lack of Government Authority, Ullage Constraint, Inadequate Berthing, Logistics Delay, Financial Constraint and Equipment Breakdown are major factors that cause delay at Tin Can Island Port Lagos. These highlighted factors should be paid special attention in order to reduce delay in Tin Can Island port which will in turns lead to more revenue for the country.

Predictive health monitoring: Leveraging artificial intelligence for early detection of infectious diseases in nursing home residents through discontinuous vital signs analysis

This research addresses the problem of detecting acute respiratory, urinary tract, and other infectious diseases in elderly nursing home residents using machine learning algorithms. The study analyzes data extracted from multiple vital signs and other contextual information for diagnostic purposes. The daily data collection process encounters sampling constraints due to weekends, holidays, shift changes, staff turnover, and equipment breakdowns, resulting in numerous nulls, repeated readings, outliers, and meaningless values. The short time series generated also pose a challenge to analysis, preventing the extraction of seasonal information or consistent trends. Blind data collection results in most of the data coming from periods when residents are healthy, resulting in excessively imbalanced data. This study proposes a data cleaning process and then builds a mechanism that reproduces the basal activity of the residents to improve the classification of the disease. The results show that the proposed basal module-assisted machine learning techniques allow anticipating diagnostics 2, 3 or 4 days before doctors decide to start treatment with antibiotics, achieving a performance measured by the area-under-the-curve metric of 0.857. The contributions of this work are: (1) a new data cleaning process; (2) the analysis of contextual information to improve data quality; (3) the generation of a baseline measure for relative comparison; and (4) the use of either binary (disease/no disease) or multiclass classification, differentiating among types of infections and showing the advantages of multiclass versus binary classification. From a medical point of view, the anticipated detection of infectious diseases in institutionalized individuals is brand new.

A vibration analysis of the permanent magnet synchronous motor under the effect of proportional derivative control

In industrial environments, excessive vibration can pose a safety risk since it can weaken the structural integrity of PMSMs and adjacent equipment. Vibration levels can be understood and controlled to help assure safe operation and avoid mishaps or equipment breakdowns. Friction and mechanical resistance are examples of vibration-induced losses that can lower PMSMs’ overall efficiency. Through the optimization of motor design and operation, engineers may minimize vibration and increase energy efficiency while lowering operating costs. Since an electric motor’s tendency to overheat after a brief period of use is linked to oscillating vibration, this study employs a proportional derivative control (PD control) to demonstrate the potential effectiveness of a permanent magnet synchronous motor (PMSM). The external force is a component of the non-linear dynamical system. The equation of the (PMSM) system is explained with two-degree-of-freedom (2dof) differential coupled equations, consisting of the major body (motor current) and cutting drive system. The present study uses the approximate method of multiple scales perturbation technique (MSPT) to get an approximate solution, showing the response equation before using (PD) and to achieve the highest effect on the control system so as to ensure the highest efficiency at the lowest possible cost. Different numerical tests have been conducted (NPDCVF, NVC, PPF, PD, delay velocity) and showed that (PD) has the best effect and is able to control the vibrations with more accuracy than others. Then, the vibration value of the system has been studied numerically before and after applying the control method of (MSPT). By combining the analysis of the resonance situation via both the phase plane techniques and the frequency response equation ω1≅ω2,ω≅ω2, within the utilization of Runge–Kutta of the fourth-order, the stability of the numerical solution has been investigated. Then, by employing the MATLAB tool, the impact of various parameters on model performance has been examined numerically. Lastly, a comparison is made with earlier research and other techniques used in prior work using other types of control mechanisms for other systems.

Insights from the Field: A Phenomenological Study of Radiography Students' Computed Tomography Clinical Placement Experiences

Introduction: While there are studies that explore the overall experiences of radiography students during clinical placements, there is a lack of research specifically focusing on the experiences of students in the context of computed tomography (CT). Therefore, this study aimed to understand the experiences of radiography students during their clinical placements in CT imaging at a tertiary institution in Harare, Zimbabwe.
 Methods: A qualitative descriptive phenomenological study design employing one-on-one interviews with 15 undergraduate students was conducted. The interview data was managed in Nvivo 12, and the analysis was based on Giorgi’s structured way of analyzing phenomenological data.
 Results: The findings show that student's experience during CT clinical placements was diverse and distinct compared to their other placements. Four themes emerged from the interview data that summarised their experiences i. Nervousness ii. Limited CT clinical exposure iii. Theory to Practice Conversion, and iv. Interpersonal skills. Initially, they felt nervous, but later they found the placements to be interesting and challenging. Some students were able to apply their theoretical knowledge to practical situations. Students also had an opportunity to improve their interpersonal skills. However, students also faced challenges such as equipment breakdowns and a shortage of consumables. These affected the student's ability to acquire the necessary clinical skills within the given timeframe.
 Conclusion: Given the limitations in accessing CT practical experience, innovative strategies such as software simulation-based training, particularly virtual reality CT simulation, are suggested to enhance students' preparedness.

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.

Condition Assessment of Electrical Power Devices using Infrared Thermography: A Review

Departures from the stable operation of electrical equipment usually appear as changes in parameters such as vibrations, pressure, temperature, power, and flow. Excessive changes in these parameters will indicate significant damage, leading to total or partial breakdown of equipment. This paper reviews the use of infrared thermography (IRT) for the operating condition temperature monitoring of electrical equipment. Infrared thermography is a contactless method that is based on thermal radiation principles and used for determining the temperature distribution on the surface of objects by measuring the intensity of radiation emitted by the object. The basic principles underlying IRT are presented in this review, showing its pros and cons, the various IRT methods, processes involved in IRT image acquisition, intelligent techniques for IR image analysis, and factors that affect the accuracy of results, including technical, environmental, and procedural factors. Also presented is an overview of common applications of IRT condition monitoring to solar energy, medicine, civil infrastructure, and aerospace equipment. Common faults in electrical equipment, including those in transformers, circuit breakers, feeder pillars and arrestors, are discussed with illustrations from recent research studies. These studies have demonstrated the effectiveness of IRT in the predictive maintenance of electrical installations. Hence, the replication of the techniques reviewed in this work is recommended so as to reduce current costs incurred in maintaining and replacing damaged equipment, and the associated time losses.