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In this paper, we propose a statistical model to describe the behaviour of failure times associated with groups of repairable systems hierarchically represented, under a competing risks framework, considering the existence of unobserved heterogeneity that acts individually on systems of each group, as well as the possibility of imperfect repairs whose initial failure rate is in the form of the power law. In this context for the unobserved heterogeneity in the groups, we consider the multiplicative frailty. To illustrate the use of the proposed model, we consider a database with the failures of 38 agricultural machines categorized into five different groups. We understand that the tractor fleet corresponds to the farm's agricultural system, therefore the need for intervention in this system occurs with the failure of any unit, individually, in a serial structure, of competing risks.e On the other hand, the understanding of the time in which all the machinery that makes up the fleet will have required some intervention is obtained by analysing the results under a parallel structure. [ABSTRACT FROM AUTHOR]

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To quickly manufacture multi-variety and low-volume products, manufacturing factories are increasingly sharing resources on collaborative production networks. However, the reliability of communication between factories cannot be fully guaranteed using traditional centralised approaches. Emerging blockchain technology can solve this problem due to its characteristics such as decentralisation and security. In this context, an integrated optimisation problem of multi-factory production and blockchain-secured collaborative maintenance is studied in this paper. Two scenarios are introduced with respective Q learning-based solution frameworks to solve the integrated problem. In the simulation scenario, preventive maintenance (PM) with flexible time windows is integrated with multi-factory production scheduling for reducing the probability of machine failures, and an initial integrated optimisation scheme is obtained. To make it more realistic, inevitable failures are considered in the actual production scenario, and the proposed collaborative maintenance strategy is triggered. Specifically, a corrective maintenance (CM) strategy is carried out immediately on the failed machine in case of a failure, followed by the PM on machines of the same type as the failed machine in other factories and the rescheduling of unprocessed jobs. Through a series of numerical studies, the effectiveness of the proposed optimisation approach and maintenance strategy is validated, and some interesting managerial implications also rise. [ABSTRACT FROM AUTHOR]

Piping is an essential component of processing plants that transfers process fluids from one piece of equipment to another. To detect the unpredictable deterioration of piping systems, periodic or continuous pipe inspection for wall loss is conducted, which determines when preventative or corrective maintenance activities, usually replacements, should be done. Therefore, it is necessary to develop an optimal maintenance strategy to replace piping systems to reduce the overall maintenance expenditure and avert the effects of pipe failure. Most of the literature in this field concentrates on maintenance activities executed during decision-making. This paper investigates creating a system and tool to assist decision-makers in evaluating and selecting the optimal moment to replace a deteriorating pipe based on the current state of the piping system. Considering that preventative replacement of the pipeline is done during a scheduled shutdown, also referred to as a turnaround, this paper investigates making a preventative replacement ruling two turnaround cycles ahead. We account for the varying thickness at different monitoring locations of two piping systems in a refinery by applying a nonstationary gamma process with random effects. We employ the Markov decision process (MDP) and the value iteration algorithm to determine the most effective maintenance replacement policy. The outcome of our analysis is an optimal maintenance policy that minimizes the cost of maintenance. Practical Applications: This paper's practical use is to aid in maintenance planning in refineries, midstream or pipelines, chemical plants, and gas facilities by planning piping replacement in two shut-down cycles ahead of time. The pipe wall is classified into states ranging from the present thickness reading to the retirement thickness. The likelihood of transiting from one state to another is calculated based on the corrosion rate. Given that the pipe wall thickness is classified into states, the current wall thickness, transitional probabilities, and preventative and corrective maintenance costs, including lost profit opportunity, can be inputted into the model to help decision-makers decide if it is optimal to replace the piping in the subsequent two shut-down cycles. This policy will assist in reducing the risk of a pipe failure, which has financial, safety, and environmental consequences. It is also critical not to replace a still-in-good-shape pipe. This model may be customized to pipe systems in the various petrochemical sectors with known retirement thicknesses to estimate the risk of operating near the retirement thickness. [ABSTRACT FROM AUTHOR]

This research presents the design and implementation of the preventive and predictive maintenance plan developed for the seven polishing machines of the Swedish multinational SKF, of the LSB (Large Size Bearing) line. Based on Reliability Centered Maintenance (RCM), which allows identifying the most relevant equipment for the process through a criticality analysis adopting strategies in order to eliminate the occurrence of failures. The methodology implemented in this research allowed the generation of maintenance tasks that facilitated the management and visualization of the information of each asset. The company provides maintenance services for large bearings used in cement plants, foundries and industrial material transport machines, seeking to optimize production methods and the application of this theoretical and analytical procedure allowed verifying that the conditioning services offered by the company are more efficient, demonstrating with figures the retribution of the plan. [ABSTRACT FROM AUTHOR]

In this study, we introduce a novel multi-objective optimization model tailored for modern manufacturing, aiming to mitigate the cost impacts of operational disruptions through optimized corrective maintenance. Central to our approach is the strategic placement of maintenance stations and the efficient allocation of personnel, addressing a crucial gap in the integration of maintenance personnel dispatching and station selection. Our model uniquely combines the spatial distribution of machinery with the expertise of operators to achieve a harmonious balance between maintenance efficiency and cost-effectiveness. The core of our methodology is the NSGAIII+ Dispatch, an advanced adaptation of the Non-Dominated Sorting Genetic Algorithm III (NSGA-III), meticulously designed for the selection of maintenance stations and effective operator dispatching. This method integrates a comprehensive coding process, crossover operator, and mutation operator to efficiently manage multiple objectives. Rigorous empirical testing, including a detailed analysis from a Taiwanese electronic equipment manufacturer, validated the effectiveness of our approach across various scenarios of machine failure frequencies and operator configurations. The findings reveal that the proposed model significantly outperforms current practices by reducing response times by up to 23% in low-frequency and 28.23% in high-frequency machine failure scenarios, leading to notable improvements in efficiency and cost reduction. Additionally, it demonstrates significant improvements in operational efficiency, particularly in selective high-frequency failure contexts, while ensuring substantial manpower cost savings without compromising on operational effectiveness. This research significantly advances maintenance strategies in production environments, providing the manufacturing industry with practical, optimized solutions for diverse machine malfunction situations. Furthermore, the methodologies and principles developed in this study have potential applications in various other sectors, including healthcare, transportation, and energy, where maintenance efficiency and resource optimization are equally critical. [ABSTRACT FROM AUTHOR]

This article aims at the development of a generic framework for estimating trade-offs between cost and availability at the equipment level due to corrective maintenance in aircraft maintenance, repair, and overhaul (MRO). The research focuses on closed-loop learning, testing, and validating the proposed framework in a South Asian military aircraft MRO organization. The methodological development of this study is based on soft system methodology through practice-based research. A dynamic simulation has been used to represent the trade-off between availability and cost after the study of more than five hundred corrective maintenance cases for impact analysis. The developed model has been tested for the fix, Gaussian, and triangular distribution in an aircraft MRO shop to calculate the liquidity damage cost due to delivery overrun of equipment. Simulation results of multiple queues indicate that input distribution plays a significant role in the trade-off between the cost and availability of LRUs in aircraft MRO. Academically, this is the first study focussing on the impact of multiple queuing distributions on cost and equipment availability in military aircraft MRO. The proposed framework may help in more precise projection in MRO scheduling, benchmarking, and decision-making while signing the commercial contract between the original equipment manufacturer and the customer. [ABSTRACT FROM AUTHOR]

Railway incidents undermine both punctuality and capacity. As traffic volumes increase, the frequency of these incidents is also expected to increase, driven by higher asset utilization, reduced time for maintenance, and further worsened by climate change. This highlights the importance of efficient incident management and corrective maintenance. This study uses a combination of exploratory data analysis and random forest regression to investigate restoration time − a key delimiter of corrective maintenance. Using data from the Swedish railway network, the study investigates the driving factors of restoration times. The maintenance contractor and the type of incident including repair action, failure cause, and the number of concurrent incidents were found to have the highest influence on restoration times respectively. Weather parameters only show discernible influence (both direct and indirect influence) beyond marked thresholds. For the Swedish railway network, these thresholds are −20 °C and 23 °C maximum daily temperatures, 18 mm maximum daily precipitation, and 20 m/s maximum windspeed. Precipitation’s and windspeed’s direct effects become more prominent beyond 50 mm and 35 m/s respectively. An understanding of the factors influencing restoration times informs the process of designing corrective maintenance protocols. Moreover, the method used in this study can be adapted to other railway networks.

The prioritisation of public facilities' maintenance is a necessary but complex task due to the need of considering both physical and socio-economic criteria. This study addresses this problem by quantifying the improvement in the delivery of social benefits that the corrective maintenance of an urban area's public facilities could yield. Based on this, a decision framework is proposed to design and schedule corrective maintenance plans at a municipal scale. The methodology integrates multi-criteria assessment with an analytical method for evaluating the contribution of an area's public facilities to its sustainable urban development based on their type of social infrastructure and their maintenance condition. The decision framework is implemented as a software to facilitate its application to a case study, consisting in building urban regeneration strategies aligned with governmental guidelines. The results revealed that decision-making is more efficient when considering the facilities' type of social infrastructure. In addition, a cost-efficient prioritisation of corrective measures yields better results than neglecting the economy. [ABSTRACT FROM AUTHOR]

PT. XYZ is a part of the largest oil and Gas Company in Indonesia, responsible for managing and developing oil and gas production activities in the East Java. The Central Processing Area serves as the facility for processing fluids produced from the East Java fields. The processed output consists of three phases: Gas, Oil, and Water. The operation of the Gas Engine G102-B TBG620 V16K running 24 hours a day to generate electricity at the central processing area of PT. XYZ requires regular maintenance, including scheduled preventive maintenance and unplanned or corrective maintenance. In the event of troubleshooting, such as a decrease in temperature in chamber 2B or a temperature sensor reading of 280 °C in that chamber, immediate corrective maintenance is necessary. Based on observations conducted in the power plant area, two possibilities causing the temperature decrease in that chamber were identified. The first possibility was the need for spark plug replacement, and the second was a malfunctioning temperature sensor resulting in incorrect readings. Therefore, the spark plug replacement was performed first, requiring a gas engine shutdown for approximately 2 hours for repairs. After the spark plug replacement, it was discovered that there was no change in the temperature reading in chamber 2B, which remained at 280 °C. Hence, the corrective maintenance performed did not have a significant impact because the issue was caused by a faulty temperature sensor. However, it was observed that there was a 1.16% increase in the generated power compared to before the corrective maintenance was conducted. The difference in power output could be attributed to variations in the quality of gas used in the combustion process of the gas engine. [ABSTRACT FROM AUTHOR]

Over time, persistent change may have a considerable effect on the codebases of enterprise resource planning (ERP) systems. Small changes, however, are not always expressions of intentional and incremental transformation. They may result from unplanned but necessary and urgent corrections for repairing undesired functionality that entered the ERP system via previously applied change. Every such repair will affect the performance and prioritization of operational processes and, by extension, influence the business that the system intends to support. We developed a change log data view to assess business impact by classifying repairs into one of two mutually exclusive classes. Repairs are either effective, meaning that the next change delivers the fix, or defective, meaning multiple successive attempts are required to satisfactorily correct the initial erroneous change. Investigating the extent to which both repair types on proprietary codebase customizations affect competence center operational performance, our view delivered two finds. First, change size is a poor indicator for repair resolution efficacy. Second, most repairs appear to be defective suggesting poor codebase change process control. Our data originated from 25 SAP landscapes, representing an average system age of around five years and an average landscape size of 57 ERP system instances. [ABSTRACT FROM AUTHOR]

Purpose: Maintenance represents an indispensable role in the productive sector of the steel industry. The increasing use of operating with a high level of precision makes hydraulic systems one of the issues that require a high level of attention. This study aims to explore an empirical investigation for decreasing the occurrences of corrective maintenance of hydraulic systems in the context of Lean 4.0. Design/methodology/approach: The maintenance model is developed based on action-research methodology through an empirical investigation, with nine stages. This approach aims to build a scenario to analyze and interpret the occurrences, seeking to implement and evaluate the actions to be performed. The undertaken initiatives demonstrate that this approach can be applied to optimize the maintenance of an organization. Findings: The main contribution of this paper is to demonstrate that the applied method allows the overviewing results, with a qualitative approach concerning the maintenance actions and management processes to be considered, allowing a holistic understanding and contributing to the current literature. The results also indicated that Lean 4.0 has direct and mediating effects on maintenance performance. Originality/value: This research intends to propose an evaluation framework with an interdimensional linkage between action research methodology and Lean 4.0, to explore an empirical investigation and contributing to understanding the actions to reduce the occurrences of hydraulic systems corrective maintenance in a production line in the steel industry. [ABSTRACT FROM AUTHOR]

Availability is an important index for the evaluation of the performance of software aging systems. Although the corrective maintenance increases the system availability, the associated cost may be very high; therefore, the balancing of availability and cost during the corrective maintenance phase is a critical issue. This paper investigates optimal corrective maintenance policies via an availability-cost hybrid factor for software aging systems. The system is described by a group of coupled differential equations, where the multiplier effect of the repair rate on a system variable is bilinear term. Our aim is to drive an optimal repair rate that ensures a balance between the maximal system availability and the minimal repair cost. In a finite time interval [ 0 , T ] , we rigorously discuss the state space of the system and prove the existence of the optimal repair rate, and then derive the first-order necessary optimality conditions by applying a variational inequality with the adjoint variables. [ABSTRACT FROM AUTHOR]

This research presents the design and implementation of the preventive and predictive maintenance plan developed for the seven polishing machines of the Swedish multinational SKF, of the LSB (Large Size Bearing) line. Based on Reliability Centered Maintenance (RCM), which allows identifying the most relevant equipment for the process through a criticality analysis adopting strategies in order to eliminate the occurrence of failures. The methodology implemented in this research allowed the generation of maintenance tasks that facilitated the management and visualization of the information of each asset. The company provides maintenance services for large bearings used in cement plants, foundries and industrial material transport machines, seeking to optimize production methods and the application of this theoretical and analytical procedure allowed verifying that the conditioning services offered by the company are more efficient, demonstrating with figures the retribution of the plan.

The share of wind energy in the energy mix is continuously increasing. However, a very important issue associated with its generation is the high failure rate of wind turbines. This situation particularly concerns large wind turbines, which are expensive and have a lower tolerance for system damage caused by various failures and faults. Vulnerable components include sensors, electronic control units, electrical systems, hydraulic systems, generators, gearboxes, rotor blades, and so on. As a result, significant emphasis is placed on improving the reliability, availability, and productivity of wind turbines. It is extremely important to detect and identify abnormalities as early as possible and predict potential failures and damages and the remaining useful life of components. One way to ensure turbine efficiency is to plan and implement preventive repairs. This work shows a semi-Markov model of a preventive maintenance system based on Enercon E82-2 wind turbines. The system's performance quality is evaluated based on profit over time and an asymptotic availability coefficient. The developed model establishes formulas describing the efficiency functions and formulates the conditions for the existence of extremes (maxima) of these functions. Computational examples provided at the end of the paper illustrate the obtained research results. A preventive maintenance model is developed that can be applied to wind turbine hazard prevention (determining optimal times for wind turbine preventive maintenance). [ABSTRACT FROM AUTHOR]

Maintenance strategies play a crucial role in ensuring the reliability and performance of complex systems. Imperfect inspections, characterized by the probabilities of false positives and false negatives, significantly impact the effectiveness of maintenance decisions. This survey explores maintenance models under imperfect inspections, characterized by constant and non-constant probabilities of false positives and false negatives. This study investigates various maintenance approaches, such as preventive and corrective maintenance, and evaluates their performance, considering the uncertainties introduced by imperfect inspections. By analyzing the existing literature and research findings, this survey provides valuable insights into the challenges and opportunities associated with maintenance decision making in the presence of inspection imperfections. The comparison between maintenance models with constant and non-constant probabilities of false positives and false negatives sheds light on the dynamic nature of these models, enabling a deeper understanding of their real-world applicability and effectiveness. This comprehensive overview is a valuable resource for researchers, practitioners, and decision makers involved in maintenance planning and optimization in diverse industrial sectors. [ABSTRACT FROM AUTHOR]

Switches & crossings (S&Cs) are vital assets as they allow for increased railway capacity by introducing flexibility and connectivity in railway networks. At the same time, this makes them critical since they can cause costly delays and disruptions if they are not well maintained. This motivates studies to improve maintenance strategies of S&Cs, considering both the life-cycle costs (LCCs) of the assets and socio-economic transportation costs for passengers and freight customers. In this paper, the interdependence between deterioration mechanisms, maintenance activities, and expected LCC (including transportation costs) for the crossing panel -- an S&C subsystem -- is investigated using a combination of mechanical and econometric modelling. The interrelation between the degradation of contact geometry and track settlement is analysed using simulations of dynamic vehicle--turnout interaction. Long-term mechanical degradation of the crossing panel is simulated for different maintenance strategies that correspond to different timing of the associated maintenance measures (crossing repair welding and tamping). This provides the basis for analysing the interdependence between preventive and corrective activities using econometric modelling. Based on a case study of a common type of S&Cs in the Swedish infrastructure, the impact of different maintenance strategies on LCC and transportation costs is analysed. Opportunities and challenges in the development of more economically effective maintenance strategies of S&Cs are discussed. [ABSTRACT FROM AUTHOR]

Computerized maintenance management systems for power plants are not well suited for application in solar fields. They work well for tracking maintenance requirements on the hundreds or even thousands of pieces of equipment in conventional power plants, but they do not work as well tracking the maintenance requirements on hundreds of thousands of identical pieces of equipment found in a solar field. A database application, known as a field status program, is being developed for tracking the maintenance and status of collectors and components in parabolic trough solar fields as a complement to the computerized maintenance management system. This system, patterned after a similar system developed by the KJC Operating Company in the 1990s, provides a valuable tool for the O&M of parabolic trough solar fields. This paper describes the development and application of the tool and the new expanded capabilities that are being added to further enhance the O&M of solar fields.

The purpose of this paper is to report on the application of the PDCA cycle in reducing machine downtime of bottled water production in an Indonesian manufacturing company. The PDCA cycle was used as a methodology in this study to solve machine downtime problems. During brainstorming sessions with the company's representative, tools such as 5W+1H and the fishbone diagram were used. The collected data was then used to design a machine component prototype in order to reduce downtime. The main result of this study was the design of a machine component to help reduce machine downtime by adding it to the cap-sealing machine. Based on observations over three months, the assembled component reduced downtime from 31.37% to 8.63%, exceeding the company's standard of a maximum loss of 10%. Furthermore, the company will benefit from this research because the designed machine component will reduce machine downtime and successfully increase the company's KPI value. [ABSTRACT FROM AUTHOR]

Waste-to-energy (WtE) plants are complex systems that requiring different types of maintenance to be reliable and available in functionality. The inadequacies of WtE plant lifetime maintenance may increase the production costs and negatively affect the competitiveness and the availability of WtE plants. To keep the efficiency of all the plant systems high and operating as expected during their lifetime, it is important to maintain them. This study focuses on the maintenance of WtE plants by analyzing the operating procedures at a case company. In the study, a multi-criterion decision-making method (MCDM) named Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used to evaluate the weight and rank of twelve identified criteria for spare parts of the case WtE company. The empirical part of this study consists of a qualitative study, where data were collected from an open-ended questionnaire survey and case company data from existing documents. The respondents' rate from the questionnaire survey was 20%. Key findings from the study show that human, economic, equipment and tool related, management, and environmental factors have an important impact on the effectiveness of the maintenance and availability of the WtE plant. The study also shows that quality, lead time, price, and the severity of spare part failure are the key criteria to consider when selecting spare parts for the WtE plant. The study recommends several initiatives to improve the availability of WtE plant and spare parts which will help to reduce the costs of maintenance as well as mitigate the risks related to the maintenance. [ABSTRACT FROM AUTHOR]

Companies that providing motorised vehicle rental services specialising in coal mining areas have to go the extra mile in carrying out maintenance due to unexpected damage by the maintenance schedule. This research aims to discover problems related to constraints on the maintenance system carried out so that it is less efficient and effective in its implementation. Preventive maintenance with a modularity design method collects data with documentation, interviews, and observations. The analysis technique determines the company's total cost (Initial TC). It continues determining the total cost of the proposal (Proposed TC) which is processed using app. Minitab 2021. The results of this study indicate that maintenance costs using preventive maintenance with the modularity design method are lower and more efficient than maintenance costs using corrective maintenance carried out by the company. [ABSTRACT FROM AUTHOR]

This paper aims to introduce a joint optimization approach for maintenance, quality, and buffer stock policies in single machine production systems based on a P control chart. The main idea is to find the optimal values of the preventive maintenance period, the buffer stock size, the sample size, the sampling interval, and the control limits simultaneously, such that the expected total cost per time unit is minimized. In the considered system, we have a fixed rate of production and stochastic machine breakdowns which directly affect the quality of the product. Periodic preventive maintenance (PM) is performed to reduce out-of-control states. In addition, corrective maintenance is required after finding each out-of-control state. A buffer is used to reduce production disturbances caused by machine stops. To ensure that demand is met during a preventive and corrective maintenance operation. All features of three sub-optimization problems including maintenance, quality control, and buffer stock policies are formulated and the proposed integrated approach is defined and modeled mathematically. In addition, an iterative numerical optimization procedure is developed to provide the optimal values for the decision variables. The proposed procedure provides the optimal values of the preventive maintenance period, the buffer stock size, the sample size, the sampling interval, and the control chart limits simultaneously, in a way that the total cost per time unit is minimized. Moreover, some sensitivity analyses are carried out to identify the key effective parameters.

Energy efficiency is an effort made with the aim of reducing the amount of energy needed when using equipment or even an energy-related system. Energy efficiency can be achieved by adopting more efficient technologies or production processes. A good maintenance strategy will maximize production efficiency and play an important role in the sustainability of production operations. This study aims to analyze the relationship between energy efficiency and corrective maintenance carried out in the production areas of mineral water companies. The implementation of corrective maintenance is applied to the gallon/bottle washer machine, which is the main equipment in the bottled mineral water production process. Implementation of corrective maintenance is carried out using the Plan, Do, Check, Act (PDCA) cycle for continuous improvement. The results showed that energy consumption decreased after several corrective maintenance tasks were carried out on the washer machine. The energy efficiency obtained is 42.13 kWh/1000 bottles of production, or equivalent to a decrease of 7.2%. [ABSTRACT FROM AUTHOR]

Availability is an important index for the evaluation of the performance of software aging systems. Although the corrective maintenance increases the system availability, the associated cost may be very high; therefore, the balancing of availability and cost during the corrective maintenance phase is a critical issue. This paper investigates optimal corrective maintenance policies via an availability-cost hybrid factor for software aging systems. The system is described by a group of coupled differential equations, where the multiplier effect of the repair rate on a system variable is bilinear term. Our aim is to drive an optimal repair rate that ensures a balance between the maximal system availability and the minimal repair cost. In a finite time interval [0,T], we rigorously discuss the state space of the system and prove the existence of the optimal repair rate, and then derive the first-order necessary optimality conditions by applying a variational inequality with the adjoint variables.

Maintenance strategies play a crucial role in ensuring the reliability and performance of complex systems. Imperfect inspections, characterized by the probabilities of false positives and false negatives, significantly impact the effectiveness of maintenance decisions. This survey explores maintenance models under imperfect inspections, characterized by constant and non-constant probabilities of false positives and false negatives. This study investigates various maintenance approaches, such as preventive and corrective maintenance, and evaluates their performance, considering the uncertainties introduced by imperfect inspections. By analyzing the existing literature and research findings, this survey provides valuable insights into the challenges and opportunities associated with maintenance decision making in the presence of inspection imperfections. The comparison between maintenance models with constant and non-constant probabilities of false positives and false negatives sheds light on the dynamic nature of these models, enabling a deeper understanding of their real-world applicability and effectiveness. This comprehensive overview is a valuable resource for researchers, practitioners, and decision makers involved in maintenance planning and optimization in diverse industrial sectors.

In reliability engineering literature, a large number of research papers on optimal preventive maintenance (PM) of technical systems (networks) have appeared based on preliminary many different approaches. According to the existing literature on PM strategies, the authors have considered two scenarios for the component failures of the system. The first scenario assumes that the components of the system fail due to aging, while the second scenario assumes the system fails according to the fatal shocks arriving at the system from external or internal sources. This article reviews different approaches on the optimal strategies proposed in the literature on the optimal maintenance of multi‐component coherent systems. The emphasis of the article is on PM models given in the literature whose optimization criteria (cost function and stationary availability) are developed by using the signature‐based (survival signature‐based) reliability of the system lifetime. The notions of signature and survival signature, defined for systems consisting of one type or multiple types of components, respectively, are powerful tools assessing the reliability and stochastic properties of coherent systems. After giving an overview of the research works on age‐based PM models of one‐unit systems and k‐out‐of‐n systems, we provide a more detailed review of recent results on the signature‐based and survival signature‐based PM models of complex systems. In order to illustrate the theoretical results on different proposed PM models, we examine two real examples of coherent systems both numerically and graphically. [ABSTRACT FROM AUTHOR]

The purpose of this article is to choose a maintenance procedure for the critical equipment of a forging production line with five machines. The research method is quantitative modelling and simulation. The main research technique includes retrieving time between failure and time to repair data and find the most likely distribution that has produced the data. The most likely failure rate function helps to define the maintenance strategy. The study includes two kinds of maintenance policies, reactive and anticipatory. Reactive policies include emergency and corrective procedures. Anticipatory policies include predictive and preventive ones combined with a total productive maintenance management approach. The most suitable combination for the first three machines is emergency and corrective choice. For the other machines, a combination of total productive maintenance and a predictive approach is optimal. The study encompasses the case of a serial production manufacturing line and maximum likelihood estimation. The failure rate function defines a combination of strategies for each machine. In addition, the study calculates the individual and systemic mean time to failure, mean time to repair, availability, and the most likely number of failures per production order, which follows a Poisson process. The main contribution of the article is a structured method to help define maintenance choices for critical equipment based on empirical data. [ABSTRACT FROM AUTHOR]

Maintenance is the protection of machines and equipment to maximize their availability. This area has become so prominent that today it occupies an important place in the organizational structure and is even one of the important areas for maintaining and increasing productivity. As the maintenance industry has improved, so has the staff that performs it, from versatile technicians to specialists who fully understand their field of work (Instituto Tecnológico Superior de Teziutlán, 2012). It has become one of the basic needs of a company or organization since technological advances have become a necessity for its growth and development. Machines and equipment in good condition are critical for the optimal operation of the process, which has led companies to consider implementing a maintenance program that helps not only to carry out repairs and avoid breakdowns, but also to identify possible causes and breakdowns. of the machines. in downtime or production Find defects before unplanned downtime, reducing corrective maintenance costs. [ABSTRACT FROM AUTHOR]

In this paper, we study renewals in a system with two components connected in series. Both components can undergo corrective maintenance (i.e., either full replacement/perfect repair or minimal repair) when a failure occurs. When one of the units fails and is correctively maintained, the other one is either preventively replaced (if cost-feasible) or simply left working as is. When a component is minimally repaired or left working as is, its remaining lifetime is reevaluated ("memory effect") and is taken into consideration in calculating the next renewal. A "coupled" lifetime that combines the lifetimes of the two serially connected components is proposed to represent the joint lifetime of the system. We develop renewal functions based on the coupled lifetimes and show that they follow the classical or generalized renewal theory depending on whether the components work without memory or not. Approximation formulas for the new renewal functions are also obtained and validated by Monte Carlo simulations for various combinations of distributions, and a comparative cost analysis is conducted. [ABSTRACT FROM AUTHOR]

A typical manufacturing system consists of a large number of repairable components/ machines which age with time and require maintenance. This paper proposes a novel maintenance policy selection method using genetic algorithm. Where, maintenance problem is formulated for n- component repairable system to minimize the total maintenance cost. The various maintenance policies and repairable components are represented in the form of chromosomes, initially various chromosomes are randomly generated which are then assessed and selected using fitness value and then crossover and mutation function is performed to obtain a better chromosome. Several iterations are performed till the desired results is achieved. The proposed algorithm is further explained and validated through an illustrative example.

• A weighted k-out-of-n system consisting of critical components with high weight and other low-weight ones is studied. • The maintenance policy considers the number of failed critical components and the total weight of working components. • The expected cost per renewal cycle and system availability are analyzed based on survival signature. • The PM cycle and inspection period are optimized under the minimal expected cost rate or maximal availability. This paper presents a novel preventive maintenance (PM) policy for a weighted k-out-of-n: G system. The system consists of critical components with high weight and other low-weight components, while the critical ones significantly impact the system's total weight. The lifetime distribution of critical and other components follows the Weibull distribution with different shape and scale parameters. Different maintenance actions are performed on the component according to the number of failed critical components and the total weight of the working components. The expected cost per renewal cycle and system availability are analyzed based on survival signature. The optimal PM cycle and the inspection period are derived by minimizing the expected cost per renewal cycle or maximizing availability. Finally, the effectiveness of the presented strategy is illustrated by a numerical case. [ABSTRACT FROM AUTHOR]

The article is devoted to the problem of the validity of determining the wear of component parts when calculating the damage caused to a vehicle due to its deterioration. The authors address the legal line of arguments of the differentiated approach when deciding on the need to consider wear and tear. They also make suggestions on its practical reflection in forensic expert opinions. Finally, it is highlighted that when making decisions regarding wear and tear, it is essential to consider the method of compensation for damage caused and official legal information.The authors justify the necessity to use the provisions of the methodological recommendations developed in the Federal State Budgetary Institution of the Russian Federation under the Ministry of Justice of the Russian Federation in the calculations, which will allow experts to formulate categorical and not relative or multivariate conclusions.

In this article, we consider a single‐unit system that operates in a dynamic environment and is subject to shocks. Shocks only affect the system (nonlethal shock) and do not cause sudden failure, and arrive according to a counting process. Both homogeneous and nonhomogeneous Poisson processes are considered for shocks arrival modeling. In order to model the dynamic environment and consider shock effects, a multiplicative failure rate model is proposed. Both corrective maintenance and shock‐based preventive maintenance are considered, and two policies are proposed. In the first proposed policy, the system is replaced by a new one upon a failure or based on the predetermined number of shocks, whichever comes first, while the second proposed policy extends the first one by considering an imperfect preventive repair at each inspection time. The inspection times are periodic and the interinspection interval is considered as a decision variable. The proposed policies are optimized according to long‐run cost rate criteria. Numerical examples illustrate the applicability and efficiency of the proposed policies. [ABSTRACT FROM AUTHOR]

The purpose of this article is to support choices in maintenance strategy for critical equipment of a manufacturer of the chemical industry considering implications in manufacturing priorities. The research method is quantitative modeling. The object is a set of five critical machines of a company in the process industry. The study develops a framework associating three maintenance strategies, reactive, predictive, and preventive, and respective deployed policies, with the phases of the lifecycle, infant mortality, useful life, and wear-out. This study also develops a framework associating maintenance strategy with four primitive manufacturing priorities, cost, quality, flexibility, and dependability. By modeling time to failure and time to repair, the study positioned the machines in the lifecycle (two are in infant mortality, three in the wear-out phase). According to the priorities (two are expected to reduce cost, three are expected to increase the dependability of deliveries), maintenance strategic pathways were depicted for each machine. Further considerations on availability helped to understand the strategic challenges that the maintenance team must face to support the company´s manufacturing strategy. The main implication is a set of assumptions relating to maintenance choices and competitive priorities in manufacturing that should be tested in further studies. [Display omitted] • Manufacturing and maintenance strategies require integration to achieve competitive performance. • The expected utility of maintenance policies varies under different manufacturing priorities. • If the priority is cost reduction, emergency strategy provides the best expected result. • If the priority is quality improvement, predictive strategy provides the best expected result. • If the priority is dependability, preventive strategy provides the best expected result. [ABSTRACT FROM AUTHOR]