Preventive Maintenance Scheduling Research Articles

СОВЕРШЕНСТВОВАНИЕ СИСТЕМЫ РЕМОНТА И ТЕХНИЧЕСКОГО ОБСЛУЖИВАНИЯ ПАССАЖИРСКИХ ВАГОНОВ ДАЛЬНЕГО СООБЩЕНИЯ

The existing systems of maintenance and scheduled preventive maintenance of passenger rolling stock used for long-distance passenger traffic with speeds up to 140 km/h are considered. Promising areas for their improvement are identified. A combined repair system is proposed, which provides a uniform approach of the rolling stock to the position of a single technical audit and scheduled preventive maintenance without taking into account its mileage. The replacement of the element base, when the mileage criteria are met, is carried out on repair and equipment tracks or specially equipped positions during the technical preparation of the rolling stock at the initial point. The proposed system, in comparison with existing ones, reduces the need for depot repairs; the cost of carrying out scheduled repairs of cars, the time spent by cars in an idle fleet, and also provides opportunities for additional operation of the car at the operational stage of the life cycle. Also, the proposed system allows regulating the frequency of the element base replacement of passenger cars with a designated service life of 40 years, including them in the mandatory requirements for the necessary periodic replacement during capital repairs of the car every 5 years.

Human factors engineering simulated analysis in administrative, operational and maintenance loops of nuclear reactor control unit using artificial intelligence and machine learning techniques

The nuclear reactor control unit employs human factor engineering to ensure efficient operations and prevent any catastrophic incidents. This sector is of utmost importance for public safety. This study focuses on simulated analysis of specific areas of nuclear reactor control, specifically administration, operation, and maintenance, using artificial intelligence software. The investigation yields effective artificial intelligence algorithms that capture the essential and non-essential components of numerous parameters to be monitored in nuclear reactor control. The investigation further examines the interdependencies between various parameters and validates the statistical outputs of the model through attribution analysis. Furthermore, a Multivariant ANOVA analysis is conducted to identify the interactive plots and mean plots of crucial parameters interactions. The artificial intelligence algorithms demonstrate the correlation between the number of vacant staff jobs and both the frequency of license event reports each year and the ratio of contract employees to regular employees in the administrative domain. An AI method uncovers the relationships between the operator failing rate (OFR), operator processed errors (OEE), and operations at limited time frames (OLC). The AI algorithm reveals the interdependence between equipment in the out of service (EOS), progressive maintenance schedule (PRMR), and preventive maintenance schedules (PMRC). Effective machine learning neural network models are derived from generative adversarial network (GAN) algorithms and proposed for administrative, operational and maintenance loops of nuclear reactor control unit.

Reliability modeling and warranty optimization for products with self-healing under a dynamic shock environment

As modern products evolve in intelligence, self-healing mechanisms have been gaining prominence in the field of reliability. In actual operation, the performance of a product is inevitably influenced by external environments and user interactions. However, existing warranty studies often overlook these factors, leading to oversimplifications in product reliability modeling. This paper offers a comprehensive and realistic view of the product failure process by integrating the dynamic shock environment, inherent self-healing property, and post-sale consumer utilization. The constructed reliability evaluation model accounts for fatal and non-fatal shocks with a dynamic time-varying failure threshold. We introduce the concept of self-healing trigger/delay time, considering a generalized self-healing process. The approximate product reliability is derived, and its accuracy is validated using Monte Carlo simulations. Then, derived from real-world practices, we propose a novel two-stage warranty policy which combines lemon law principles with preventive maintenance. By minimizing the expected cost rate throughout the warranty service period, a preventive maintenance schedule optimization model is developed from the manufacturer's perspective. Finally, a numerical case is presented to reveal the influences of different factors on product reliability and demonstrate the existence of the optimal solution.

Optimizing maintenance logistics on offshore platforms with AI: Current strategies and future innovations

Offshore platforms are vital assets for the oil and gas industry, serving as the primary facilities for exploration, extraction, and processing. Maintenance logistics plays a crucial role in ensuring these platforms operate efficiently and safely. However, the remote and harsh environments of offshore platforms present significant challenges for maintenance activities. Traditional maintenance strategies often struggle to meet the demands of these environments, leading to inefficiencies, increased costs, and potential safety risks. This review discusses the application of Artificial Intelligence (AI) in optimizing maintenance logistics on offshore platforms. Current strategies involve a combination of preventive, predictive, and corrective maintenance approaches. Preventive maintenance schedules regular inspections and replacements based on predetermined intervals, while predictive maintenance utilizes data analytics to predict equipment failures and plan maintenance activities accordingly. Corrective maintenance addresses issues as they arise, often in response to unexpected failures. AI offers opportunities to enhance these strategies by leveraging advanced data analytics, machine learning, and optimization algorithms. AI-enabled predictive maintenance can analyze vast amounts of data from sensors, historical maintenance records, and environmental factors to forecast equipment failures with greater accuracy. This allows for proactive maintenance planning, minimizing downtime and reducing maintenance costs. Furthermore, AI can optimize maintenance logistics by improving resource allocation and scheduling. Through real-time monitoring and analysis, AI systems can prioritize maintenance tasks based on urgency, equipment criticality, and resource availability. This ensures that maintenance crews are deployed efficiently, reducing idle time and improving overall productivity. Future innovations in AI for maintenance logistics on offshore platforms include the integration of Internet of Things (IoT) devices and autonomous systems. IoT sensors can provide real-time data on equipment condition and environmental factors, enabling more precise predictive maintenance models. Autonomous maintenance robots equipped with AI algorithms can perform routine inspections and minor repairs, reducing the need for human intervention in hazardous environments. However, implementing AI in offshore maintenance logistics also poses challenges, including data quality, cybersecurity, and workforce readiness. Ensuring data accuracy and reliability is crucial for effective AI models, requiring robust data collection and management processes. Cybersecurity measures must be strengthened to protect AI systems from malicious attacks that could disrupt operations or compromise safety. Additionally, workforce training and education are essential to prepare personnel for working alongside AI systems and interpreting AI-generated insights. Optimizing maintenance logistics on offshore platforms with AI offers significant benefits in terms of efficiency, cost savings, and safety. By leveraging AI technologies, current maintenance strategies can be enhanced, and future innovations can revolutionize offshore maintenance practices, making operations more sustainable and resilient in the face of evolving challenges.

An integrated framework of preventive maintenance and task scheduling for repairable multi-unit systems

Preventive maintenance and task scheduling are both crucial activities in industrial practice. Although they are interdependent and mutually influential, most existing studies typically analyze them separately. In this paper, an integrated framework of preventive maintenance planning and task scheduling is explored. On one hand, while most scholars focus on the system as a whole in scheduling studies, we further take into account the degradation and maintenance of multiple units during the task scheduling process. On the other hand, instead of only using "direct replacement" as the main maintenance method, we further consider the repairability of the unit by incorporating imperfect maintenance effects into the maintenance modeling. Additionally, the lifecycle partitioning method is proposed to analyze the maintenance groups for multiple units at the completion of scheduling tasks and their corresponding probabilities. Based on this foundation, an integrated decision model is established with the preventive maintenance threshold and scheduling task sequence as decision variables to minimize the total weighted expected completion time. Finally, a novel genetic algorithm based on hybrid encoding is proposed, and the effectiveness of integrated decision-making was validated using a practical example.

Effect of Load on Sequential Imperfect Preventive Maintenance and Replacement Schedules of Mechanically Repairable Machines

This paper examines the impact of load on the operational time and maintenance cost of mechanically repairable machines. Three different levels of load with multiplicative impact on the hazard rate of the failure distribution were applied to the working of a cassava grinding machine using a two-parameter Weibull distribution with respective hazard and cumulative hazard functions. Their effect on the preventive maintenance (PM) and replacement schedules revealed that at above maximum load level, the length of the machine’s operational time decreased drastically compared to the decrease at maximum load level and relative decrease at the below maximum load level when compared to the machine’s operational time at the minimum load level. The application of load also results in frequent preventive maintenance actions and an increase in machine downtime for a given cost ratio. This implies that the influence of load on the PM and replacement maintenance schedule of mechanically repairable machines is essential to the design and operation of such machines. The results also provide maintenance engineers with an operational guide for PM and replacement maintenance actions in order to prevent failure maintenance and increase the machine’s availability for enhanced productivity.

Development of optimal operation algorithm for rotation mechanism of refuelling machine television rod

The paper examines the issues of optimizing the operation of the rotation mechanism of the television rod of the refuelling machine during the refueling campaign during the scheduled preventive maintenance at the NPP. The task under consideration is to minimize the time spent on turning the television rod during a reloading campaign in order to save time and money on technological operations, and thereby reduce the downtime of the nuclear power plant unit. A description of the design and purpose of the work area, the refuelling machine and other objects involved in technological operations is provided. In all parts of the working area there is a certain number of «problem» cells in which interference and/or blocking of rotation of the television rod may occur. The exact location of all «problem» cells and the permissible angles of movement and observation for them are revealed. The list of operations is compiled as a table containing: operation numbers, markings of the corresponding cluster, as well as the initial and target positions in coordinates. This table comes complete with a cartogram with coordinates and cell labels. It is calculated how long it takes to turn from one permissible viewing angle to another. An example of time calculation for an operation with a reloading machine is given.

Совершенствование технологии эксплуатации инженерных систем жилых зданий на основе использования организационно-технологического моделирования

The author has proposed a model for the technical operation of residential buildings, which considers two main functions for maintaining the housing stock: scheduled preventive inspection and repair of technical facilities, as well as the elimination of sudden malfunctions of technical equipment, in particular emergency ones. It is believed that the work team can begin scheduled preventive maintenance and inspection only when all requests for sudden malfunctions have been satisfied.
 To conduct the study, the theory of queuing is used - one of the probabilistic methods.
 The main parameters of the model are: the average time between the occurrence of equipment malfunctions, the average time for eliminating such malfunctions, the average time for preventive inspection and repair of one technical object. Based on the methods of queuing theory, the characteristics of the system are determined that determine the quality of its work, as well as the boundaries of changes in parameters at which the system copes with the work from the standpoint of one or another criterion.

Метод оценки факторов, влияющих на вибрацию судового дизеля, вызванную работой цилиндропоршневой группы

The efficiency of the use of the marine fleet is associated with a reduction in the cost of transportation, the need for which is steadily increasing. A significant share of the total transportation costs is occupied by the costs associated with the operation of ships, therefore, reducing these costs is a reserve for reducing the cost of maritime transportation. The problem of increasing the efficiency of the operation of marine vessels is closely related not only to reducing the cost of it, but also to reducing the time for maintenance and repair of ships. The existing system of scheduled preventive maintenance and repair provides for the maintenance and restoration of the normal technical condition of the vessel and its elements with a pre-planned frequency. The transfer of ships to work with extended repair periods is possible only when solving a whole range of engineering and technical measures and scientific research aimed at improving the quality of repairs, the level of technical operation, improving logistics, etc. An essential role in this is played by the use of modern methods and means to determine the technical condition of shipboard devices and mechanisms, primarily engines of shipboard power plants (SEUs), without disassembling them. High vibration loads of SEU parts lead to fatigue stresses, accelerate wear of the contacting surfaces, reducing the reliability and service life of marine diesel engines, therefore monitoring of their vibration activity is important, especially during operation. The safe operation of ships of the transport fleet largely depends on the reliability of marine diesel engines. Technical means of monitoring their operability in the process of work are currently insufficiently effective and need to be improved. It is relevant not only to develop new, more advanced and mobile hardware and software, but also to search for new scientific and methodological approaches to using the information contained in the spectrum of vibration parameters. Marine diesel internal combustion engines are complex systems that involve many variable parameters. The study of such engines requires the use of methods that can be used to systematically investigate the influence of various factors on their operation, in particular, random experimental schemes are used, since they provide systematic control over variable parameters and maximize the accuracy of estimates. The results of the application of the method of ranking the factors influencing the vibration of a marine diesel engine are presented in order to establish the most significant of them. A mathematical model is presented that allows calculating current values and predicting changes in the most significant indicators, among which the most important is the size of the gap between the piston and the cylinder sleeve.

Application of Participatory Concept in Minimizing Food Packaging Waste (FPW) in Manufacturing Firms

All the industrialized countries lately focus on the need for the best solutions for the reduction of food waste at the various stages of the supply chain by conducting the participatory concept. It is a solution and stakeholder-oriented concept providing information on how to analyze the status quo identification of organization-specific problems and with the involvement of relevant stakeholders, develop solutions in the processes along the food value chain. The objective of this study is to present a participatory (five-phase) concept to diminish FPW in manufacturing firms. This concept is adapted to the continuous improvement (PDCA cycle) applied in Total Quality Management (TQM), which involves a participatory approach where workers and stakeholders are collaborated to detect causes for inefficiency and advance measures to counteract FPW in the Healthy Food Drink powder Manufacturing firm. The research was conducted by analyzing the data related to FPW such as Aluminum Wrappers (AlWs), Bag in Boxes (BIBs), and Plastic Bottles (PBs) using Microsoft Excel and Power BI software. Also, it paid attention to related operational and supporting processes within the organization and the behavior of the staff to get an impression of the working atmosphere and attitudes of staff. The analysis was done as a comparison with the collected FPW data for 2 months before and after developing, and implementing upgraded, targeted management practices. This analysis provided the basis for the developmental framework with the focus of increasing the effectiveness of FPW management practices by assessing the structure and selecting the best waste management methodologies for FPW. According to the analyzed data, the total wastage of AlWs, BIBs, and PBs showed 5.81%, 1.82% and 1.96% respectively. With the participatory concept, the organizational weaknesses which caused the occurrence of FPW were identified, and developmental measures were implemented to obtain significant reductions of waste of Alws, BIBs, and PBs up to 2.06%, 0% and 0.43% consequently. Counteractive measures, such as join in workforces into the developing and implementing waste reduction measures, workforce training, scheduled preventive maintenance on machines, utilization of removable ink-to-print date codes, efficient demand planning and accurate demand forecasting, enhance workforce motivation and commitment, establishing trust among all employees and proper communication flows within departments which influencing the occurrence of FPW have been proposed to avoid losses. It affirms that the participatory concept either contributes to reducing FPW or enhancing resource efficiency in the food industry, as this would enable companies to benefit economically. 
 Keywords: Food packaging waste, Participatory concept, Continuous improvement, Integration, Resource efficiency 

Minimization of Root Causes of Declining Productivity in a Manufacturing Company: A Case Study of Jocalis Company

This presents a comprehensive methodology employed to identify and address declining productivity issues within the JOCALIS Aluminium Roofing Sheet Manufacturing Company Limited, situated in Onitsha, Nigeria. The study encompasses various manufacturing divisions, with a primary focus on aluminium roofing sheet production of three lines involve in production of red, black, and milk colour coated roofing sheet. The research aims to uncover the root causes of defects in the manufacturing process and develop effective strategies for improvement. Key methods include data collection through interviews, company records, library research, and internet sources, followed by data analysis and root cause identification. From the result obtained, the average availability of critical machine like the roller machine after root cause analysis is increased by 10.62%. Also, the average MTBF (mean time between failure) of the critical machine after root cause analysis is increased by 13.66% and MTTR (mean time to repair) is decreased to 46.42% respectively. The applications and general impact of this study includes enhancing machine availability, reducing downtime, increasing efficiency, optimizing maintenance practices, implementing preventive maintenance schedules, improving equipment diagnostics, and prioritizing root cause analysis to reduce breakdowns.

Statistical learning for maintenance optimization: modeling the hazard function with variable recovery factors

Abstract: This study aims to utilize advanced statistical methods in equipment maintenance within the machining and casting processes of an industrial company, focusing on cost optimization. Employing a methodology that combines literature review, mathematical formulation, and statistical treatment of historical data from preventive and corrective maintenance, the study stands out for the use of maximum likelihood estimation to determine not only the model parameters but also the recovery factor used during preventive maintenance. It also calculates the optimal maintenance time to minimize the total cost. This work not only underscores the importance of a data-driven maintenance approach, balancing reliability and cost to achieve effective optimization but also signals the need for future studies, especially in simulations to further enhance preventive maintenance scheduling, encompassing all aspects of the production process.

Joint optimization of flexible job shop scheduling and preventive maintenance under high-frequency production switching

To secure a larger market share in the dynamically evolving personalized market demand, enterprises have to adopt a more flexible production mode. In this context, the implementation of parallel production with multiple product types necessitates frequent switching between different products, imposing elevated requirements on machine fault-free operation and stable production. There is a compelling motivation to investigate the joint optimization of the flexible job shop scheduling problem and preventive maintenance (FJSSP-PM). However, existing research has primarily focused on utilizing predetermined maintenance as inputs for optimizing production scheduling, while overlooking the uncertain maintenance requirements of machines during the production process. Therefore, this paper proposes a two-stage joint optimization model to simultaneously address three subproblems: machine assignment, operation sequencing, and maintenance arrangement. Specifically, the first stage involves constructing a mathematical model for the FJSSP to minimize penalties for tardiness and workload balancing. Subsequently, a comprehensive failure rate model is developed to determine maintenance requirements for machines with production-dependent failure behavior, particularly under high-frequency production switching scenarios. Furthermore, a feedback-updated strategy is proposed to achieve synchronous optimization of production and maintenance activities in an economically and operationally efficient manner. Given the complexity of the problem, a hybrid meta-heuristic algorithm is designed to effectively handle large-scale cases. The effectiveness of the proposed model in terms of cost-savings has been verified compared with existing relevant methods.

Technical Preparation for Scheduled Preventive Maintenance of Double-Decker Rolling Stock

The article is devoted to the study of the process of technical preparation preceding scheduled preventive maintenance of newly commissioned double-deck passenger coaches. The paper considers the main tasks and goals of technical preparation, including the step-by-step process of car repair, as well as repair planning, formation of a range of applied materials, tools, and step-by-step control of work performed during repair and maintenance. Upon identifying features and inconsistencies that arise during technical preparation for repair of double-deck coaches of a new model range, ways to solve them are proposed.The paper considers the main directions in organisation of preventive maintenance in terms of implementation of measures to integrate into the process the features associated with the specifics of the assemblies, parts and materials used in those coaches; main tasks of preparing for scheduled preventive maintenance with regard to provision of workshops, of developing forms, methods and adjusting production processes, as well as of employing small-scale mechanisation tools, and manufacturing devices to speed up the repair process. The processes of carrying out scheduled preventive maintenance are structured allowing development of a process model for repair of a double-decker coaches when they are delivered at maintenance and repair facilities.The study allows regulating and improving the efficiency of technical preparation for repair of double-deck coaches, ensuring higher quality and speed of repair work, as well as effectively assessing the real need for spare parts and materials used during the repair process, since the operation of these coaches provides for a new set of spare parts, materials and elements in comparison with single-deck analogues already manufactured.

Integrated non-cyclical preventive maintenance scheduling and production planning for multi-parallel component production systems with interdependencies-induced degradation

Integrated non-cyclical preventive maintenance scheduling and production planning for multi-parallel component production systems with interdependencies-induced degradation

Optimal Preventive Maintenance Scheduling for Wind Turbines under Condition Monitoring

Renewable energy sources, such as wind and solar, are positioned to play a pivotal role in future energy systems. In this paper, we propose a mathematical model for calculating and regularly updating the next preventive maintenance plan for a wind farm. Our optimization criterion considers various factors, including the current ages of key components, major maintenance costs, eventual energy production losses, and available data monitoring the condition of the wind turbines. Employing Cox proportional hazards analysis, we develop a comprehensive approach that accounts for the current ages of critical components, significant maintenance costs, potential energy production losses, and data collected from monitoring the condition of wind turbines. We illustrate the effectiveness of our approach through a case study based on data collected from multiple wind farms in Sweden. Our results demonstrate that preventive maintenance planning yields positive effects, particularly when the wind turbine components in question have significantly shorter lifespans than the turbine itself.

Determining Preventive Maintenance Schedule on Press Machine Components using Age Replacement Methods

To minimize severe damage and reduce maintenance costs, it is essential to monitor and control the performance decline of machines periodically. Implementing regular preventive maintenance on the machines used in production activities is a key approach to achieve this goal. By performing scheduled maintenance on a regular basis, the strength of the machines can be maintained, ensuring their stability, and facilitating smooth production processes. Notably, the press machine serves as the primary printing equipment for the company’s product manufacturing. Any breakdown in this machine can significantly disrupt production and lead to losses for the company. Frequent production interruptions due to sudden inspections and repairs are among the challenges faced by the company. To address this issue, it is recommended for the company to establish a specific preventive maintenance schedule for the press machine to ensure its stability. The Age Replacement method can be employed in creating this preventive maintenance schedule, as it determines the optimal replacement interval based on the component’s age. By utilizing the Age Replacement method, the company can generate the most effective proposed scheduling required for maintaining the press machine in optimal condition.

A memetic based algorithm for simultaneous preventive maintenance scheduling and spare-parts inventory management for manufacturing systems

To increase machine availability, it may be necessary to halt them to replace components before failures occur. Therefore, optimizing spare parts inventory management is a key factor in improving maintenance activities. Spare part costs are a significant contributor to total maintenance costs, including holding and ordering costs, and are often dependent on the maintenance schedule. Hence, it is important to optimize preventive maintenance scheduling and spare parts inventory management together. The first attempt to introduce a new Mixed-Integer Linear Programming (MILP) model that integrates both decisions was by Mjirda et al. (2016). Our paper is the first to extend their work in order to come up with an efficient aided decision making framework based on a mat-heuristic approach that delivers a good trade-off between computational time and solution quality. Indeed, this paper introduces a novel Hybrid Memetic Algorithm (HMA) that synergistically combines Memetic Algorithm (MA) with MILP to solve this NP-hard problem. The paper aims to provide a unified and efficient mat-heuristic based aided decision making framework that optimizes the sum of maintenance, operating, holding, and ordering costs by synchronizing preventive maintenance schedules and spare parts inventory for a set of independent machines. Its efficiency is illustrated through an extensive computational study. The HMA outperforms standalone MILP solvers, solving 36% of instances to optimality within a computational time of less than 141 s, and achieving a maximum gap of 5% for 46% of instances within less than 200 s. Our findings highlight the computational efficiency and solution quality offered by the HMA, thereby advancing the state-of-the-art in integrated spare parts inventory management and preventive maintenance scheduling.

APLICAÇÃO DA LOGICA FUZZY PARA PRIORIZAÇÃO DA MANUTENÇÃO DE DISJUNTORES DE SUBESTAÇÃO DE ENERCIA ELÉTRICA

In electric power supply, in order for electric power substations to continue to operate, the maintenance of the electrical equipment present is of utmost importance. Currently there are several forms of preventive maintenance scheduling, where each power supply company adopts an existing method or develops a new method. To perform this work, we used the methodology of fuzzy logic that enables the calculation of reliability among the uncertainties, in order to classify the degree of criticality in the maintenance of circuit breakers that may present technical problems, performed a preventive maintenance, comparing with the method already used by the distribution company. The results found demonstrate greater accuracy than the model currently presented by the distributor. Although the results obtained are satisfactory, its use is only indicated as a support tool for teams and specialists. Since its factors still need to be adjusted, for a better predictability rate, where some samples the maintenance date is very close to the breaker failure date.

Fuel cell preventive maintenance in an electricity market with Hydrogen storage and Scenario-Based risk management

The application of the fuel cell (FC) in improving the clean and energy-efficient cogeneration of heat and power has been broadly justified by researchers. However, the reliable operation of FC units requires periodic maintenance services. In this regard, preventive generation maintenance scheduling (GMS) notably preserves the units’ lifespan, prevents unexpected failures, and restricts imposed repair costs caused by failures. The present work models the GMS problem for FC and highlights its challenges compared to the GMS of conventional generation units. The main challenges, including simultaneous satisfaction of the power-heat dual dependency in FC, the power and heat demand balances, and maintenance considerations, including duration, continuity, and coordination of maintenance, are addressed in this research. The maintenance program is first planned for a year to optimize the maintenance timetable. Afterward, daily generation scheduling is optimized subject to the GMS program obtained for the studied year. Moreover, by using risk management, the economic risk associated with the problem (due to uncertainty in the electricity price) is handled. The obtained results for the impacts of risk management on FC’s maintenance schedule depict an 11.5% improvement in the cost of the worst-case scenarios, namely a cost reduction from M$111.4 to M$98.6.