ОБГРУНТУВАННЯ ОПТИМАЛЬНИХ ПАРАМЕТРІВ СТРАТЕГІЇ ТЕХНІЧНОГО ОБСЛУГОВУВАННЯ, ЩО РЕГЛАМЕНТУЄТЬСЯ

Complex technical objects are understood as objects consisting of a large number of different types elements (tens, hundreds of thousands), each of which can represent a rather complex technical device. Elements can be electronic, mechanical, electromechanical, hydraulic, etc. The diversity of elements leads to the fact that for various elements fundamentally different physical processes (and, consequently, rates) of degradation are characteristic, leading to their failures. Objects can have an arbitrary reliability structure (as a rule, serial-parallel). The structural structure of such objects is usually hierarchical, that is, an object consists of subsystems, subsystems consist of units (cabinets), units - of devices (blocks), etc. A characteristic feature of complex technical objects for special purposes is the presence in their composition of a large number (tens, hundreds of thousands) of different types of component parts that have different levels of reliability, different patterns of their wear and tear processes. This feature requires a more subtle approach to the organization and planning of maintenance during their operation. The problem is that during the development of such facilities, all issues related to maintainability and maintenance should be addressed already at the early stages of facility design. If you do not provide in advance the necessary hardware and software for the built-in monitoring of the technical condition (TC) of the object, do not develop and “build” the maintenance technology into the object, then it will not be possible to realize in the future a possible gain in the reliability of the object due to maintenance. Since all these issues must be resolved at the stage of object creation (when the object does not yet exist), mathematical models of the maintenance process are needed, with the help of which it would be possible to calculate the possible gain in level of reliability facility due to maintenance, to estimate the cost costs required for this. Then, based on such calculations, make a decision on the need for maintenance for this type of objects and, if such a decision is made, develop the structure of maintenance system, choose the most acceptable maintenance strategy, and determine its optimal parameters. This paper provides a comparative study of various maintenance strategies. The paper also confirms that the optimal parameters of various maintenance strategies significantly depend on both the reliability and cost structure of object and the specified requirements for the level of reliability of object.

Complex technical objects in modern society are extremely important. Such objects belong to the class of recoverable objects of long-term multiple used. They tend to be expensive and require significant maintenance costs. To ensure the required level of reliability during their operation, maintenance is usually carried out, the essence of which is the timely preventive replacement of elements that are in a pre-failure state, which is very important for military equipment. A characteristic feature of complex technical objects for special purposes (military equipment) is the presence in their composition of a large number (tens, hundreds of thousands) of different types components that have different levels of reliability, different patterns of their wear and aging processes. This feature requires a more subtle approach to the organization and planning of maintenance during operation (military equipment). The problem is that when developing such objects of military equipment, all issues related to maintainability and maintenance should be addressed already at the early stages of designing an object. If you do not provide in advance the necessary hardware and software for the built-in monitoring of the technical condition (TC) of the object, do not develop and “embed” the maintenance technology into the object, then it will not be possible to realize in the future a possible gain in the reliability of the object due to the maintenance. Since all these issues must be resolved at the stage of creating an object (when the object does not yet exist), mathematical models of the maintenance process are needed, with the help of which it would be possible to calculate the possible gain in the level of reliability the object due to maintenance, to estimate the cost costs required for this. Then, based on such calculations, make a decision on the need for maintenance for this type of objects and, if such a decision is made, develop the structure of the maintenance system, choose the most appropriate maintenance strategy, and determine its optimal parameters. In this paper, we develop a methodology for optimizing the parameters of the strategy for regulated maintenance of military equipment. The paper also confirms the general idea that the data obtained fully confirm assumption that the “adaptive maintenance” strategy is more preferable in the case of unreliable (inaccurate) information about the reliability indicators of the object’s elements.

Complex technical objects in modern society are extremely important. Such objects belong to the class of recoverable objects of long-term multiple uses. They tend to be expensive and require significant maintenance costs. To ensure the required level of reliability during their operation, maintenance is usually carried out, the essence of which is the timely preventive replacement of elements that are in a pre-failure state. The problem is that when developing such facilities, all issues related to maintainability and maintenance should be addressed already at the early stages of designing the facility. If you do not provide in advance the necessary hardware and software for integrated monitoring of the technical condition (TC) of the object, do not develop and “embed” the maintenance technology into the object, then it will not be possible to realize in the future a possible gain in the reliability of object due to maintenance. Since all these issues must be resolved at the stage of creating an object (when the object does not yet exist), mathematical models of the maintenance process are needed, with the help of which it would be possible to calculate the possible gain in the level of reliability of object due to maintenance, to estimate the cost costs required for this. Then, based on such calculations, make a decision on the need for maintenance for this type of objects and, if such a decision is made, develop the structure of the maintenance system, choose the most appropriate maintenance strategy, and determine its optimal parameters.

The opportunity costs of increasing reliability in irrigation systems

An optimization-based maintenance scheduling framework is an essential tool to plan the necessary investment to maintain the required performance of a railway line. In the present study, a methodology is proposed to minimize the present value of the life cycle maintenance costs and maximize the life cycle quality level of the track-bed considering different levels of reliability. Probabilistic degradation models are developed for predicting the evolution of the railway track condition over time. Afterwards, a Genetic Algorithm based optimization procedure is applied for obtaining a set of optimal solutions taking into account several constrains. The proposed methodology is applied to an Italian railway track-line case study. The results show that it is possible to develop a decision support system to help railway managers to schedule railway track maintenance operations based on the optimal trade-off between maintenance costs and railway track geometry condition for different levels of reliability.

Purpose In today's competitive industries, the selection of best suitable maintenance strategy is dependent on large number of quantitative and qualitative factors, and it becomes an extensively difficult problem for maintenance engineers. Over the years, a diverse range of solution methodologies have been developed for solving this multi-criteria decision-making (MCDM) problem. In this paper, the authors have presented a comprehensive review of latest maintenance strategy paradigms and solution approaches proposed for the selection of an appropriate strategy in various industries. It would provide a systematic mapping of developments in this field and identify some research gaps to explore further studies. Design/methodology/approach A systematic state-of-the-art comprehensive literature review on maintenance strategy paradigms and selection approaches is presented in this study. In this study, 87 research articles published in peer-reviewed journals, since year 2012, are reviewed. Findings For the selection of a suitable maintenance strategy, a variety of criteria are considered to better evaluate the alternatives. In this study, contemporary strategies are discussed, and their applications in different industries are also depicted. Moreover, through the analysis of extant literature, critical criteria are selected and classified in six major categories (namely, economic, technical, safety, environmental, feasibility and social) and further sub-categorized in quantitative and qualitative classes. These clusters of criteria can be helpful as an initial set of criteria for survey and then case- or industry-specific criteria can be shortlisted for further alternative evaluation. Practical implications From the perspective of maintenance managers, maintenance management can be a very difficult task, considering the numerous factors affecting the decision-making process. In order to help in the decision-making process, this study presents the contemporary maintenance strategies in a systematic manner. In a previous study (Kothamasu et al ., 2006), these strategies were classified into repair and prevent classes only. With the developments of autonomous maintenance and design out maintenance (DOM), it was fair to include continuous improvement class. It will help managers and practitioners to identify, according to organization policy, appropriate maintenance strategy alternatives for the asset. A benchmark set of state-of-the-art maintenance strategies are laid out with their applications. The industrial case studies discussed in this study summarizes the optimal maintenance strategies for respective industries. Also, most critical criteria are identified from the existing studies for various industries that can help maintenance practitioners in acknowledging the critical factors and making appropriate decisions. Evaluation parameters for the maintenance strategy selection (MSS) generally conflict with each other, and considering the difficulty of quantifying the qualitative measures, it is a challenging task to determine the optimal trade-off. In order to overcome these challenges, popular MCDM approaches, demonstrating effective results across different industries are discussed with their limitations and applications. Decision-makers can refer this study to identify best suitable decision-making technique for the MSS problem in the industry of their choice. Maintenance managers and engineers can refer the case studies illustrated in Tables 1 and 2 to analyse the MSS techniques proposed by previous studies with industry-specific applications. Social implications This study is an attempt to provide a reference point for research scholars interested in the field of maintenance management and/or development of maintenance strategy framework. This study provides a critical state-of-the-art review of efforts made in the field of MSS. The prominent maintenance strategies being implemented in contemporary industries are discussed with respective case studies. Interested researchers and academicians can familiarize themselves with these strategies and their distinct features in this study. In order to guide future studies and provide a reference point for academicians, MSS critical criteria used in extant literature are identified and classified into a comprehensive benchmark framework. Moreover, the industrial case studies are discussed with the most critical criteria of MSS for different industries and which strategy is most suitable for the respective industries based on these criteria. Table 1 presents different MCDM techniques and their hybrid applications for solving MSS problem that can help researchers in identifying research gaps. Future research can be directed at addressing the limitation of MCDM approach employed in existing studies and comparing the differences in results obtained by the proposed approach. Different industrial case studies with considered maintenance strategy alternatives are presented in Table 2, which can help researchers in identifying the industries that have not been studied yet. Moreover, not all of the existing studies are carried out by considering all the presented benchmark strategies, which can be addressed in future studies by interested researchers. More detailed discussion on research gaps is presented in the following section. Originality/value From the analysis of the extant literature, the authors could observe that the decision-making process adopted in numerous studies was limited to the classical maintenance strategies and not inclusive of aggressive maintenance strategy alternatives. To overcome these limitations and help maintenance managers in the decision-making, this study depicts the contemporary maintenance strategies, critical evaluation criteria and MCDM frameworks (employed to solve the MSS problem with industrial case studies) in a structured manner.

Many scientific projects such as SETI@home are leveraging on enormous numbers of distributed internet-based idle desktop computers to execute their jobs in a Desktop Grid (DG) computing environment. These idle desktop computers (volunteers) are volatile, i.e., they are free to join and leave the DG systems. Besides, the volunteers might also have different resources which are heterogeneous in characteristics and capabilities. Due to the above facts, volunteers in a DG system have different levels of reliability and availability which makes scheduling of jobs more challenging. Thus, this paper presents a grouping method which assigns these heterogeneous volatile volunteers into different levels of reliability groups, according to their availability score. In order to derive the availability score model for the grouping purposes, this study uses statistical analysis of real trace data which are taken from SETI@home project. By having the proper grouping, better assignment of jobs can be achieved in a DG system. Simulation runs of the approach shows that it is feasible to volunteers are able to be grouped using the scoring model.

Automated lines with complex structures consist of stations and mechanisms with different levels of reliability. Most publications that present the mathematical models for productivity of automated lines are based on simplifications to derive the approximate equations of productivity. Simplification is based on the premise that all stations of the automated lines have one level of reliability, and balancing of technological process on stations is conducted evenly, etc. However, manufacturers need correct and clear mathematical models to enable the calculation of the productivity of the automated lines with high accuracy. This paper presents the analytical approach to the productivity rate of the automated lines with stations and mechanisms, with different failure rates, and processing times. The proposed mathematical models allow for the output of automated lines to be modelled with results that are close to actual productivity.

An efficient combination of multi-objective evolutionary optimization and reliability analysis for reliability-based design optimization of truss structures

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

ABSTRACTFatigue characteristics of multiwalled carbon nanotubes (MWCNTs) toughened glass fiber reinforced plastic (GFRP) composites made of woven angle ply (45°/−45°) fabric is presented. Fatigue experiments were conducted under tension–tension fatigue loading for stress ratios 0.1 and 0.3, and at three different stress intensities. The dispersion of 0.5 wt.% of MWCNTs in the modified GFRP resulted in better fatigue lifecycle compared with the GFRP modified with 1.0 wt.% of MWCNTs. In addition, MWCNTs dispersion also affected other fatigue properties, such as fatigue stiffness, cyclic creep, and dissipated energy. Analytical models were employed to demonstrate a substantial amount of reliability in accurately predicting the fatigue lifecycle of MWCNTs modified GFRP. Furthermore, a statistical methodology has been utilized to ascertain the design fatigue lifecycle at different levels of reliability.

The use of Solid State Drives (SSDs) has been increased in storage systems due to high performance and low power consumption. However, some inherent properties of SSDs result in different behavior for SSDs in comparison with Hard Disk Drives (HDDs). As an inherent property, the Bit Error Rate (BER) of SSDs increases, when the number of Program/Erase (P/E) cycles arises. This increment leads to two effects in an array of SSDs during its operation: (1) different BER on different SSDs of the system (SSD-variant BER), and (2) different BER in different time moments (time-variant BER). With respect to these two effects, the reliability evaluation of SSD-based RAIDs would be different from HDD-based RAlDs. This paper introduces a simulator called SOYA for evaluating the reliability of RAH) systems regarding different BER of SSDs. Both time-variant BER and SSD-variant BER have been considered in the evaluation process of this simulator. This simulator provides the ability to compare the reliability of SSD-based RAID systems considering arbitrary parameters in the evaluation such as disk failure rate, disk repair time, RAID-controller failure rate, RAID-controller repair time, and different levels of reliability. SOYA is the first SSD-based reliability simulator for SSD-bases RAID systems which can consider all the above-mentioned parameters together in its reliability evaluation.

Designing an optimal multi-energy system with fast charging and hydrogen refueling station under uncertainties

Range of motion and repeatability of knee kinematics for 11 clinically relevant motor tasks

This paper presents cross entropy (CE) optimization for optimal design of water distribution networks (WDN) under demand uncertainty. In design of WDNs, it is desired to achieve a minimum cost WDN that provides higher reliability in meeting the demands. To achieve these goals, an optimization model is formulated for design of WDNs with an objective of minimizing the total cost of WDN subject to meeting the nodal demands at a specified system reliability, mass conservation and other physical constraints. The uncertainty in future water demands is modeled using the theory of fuzzy random variable (FRV). The water demand at each node is assumed to be following a normal distribution with a fuzzy mean, and 10 % (or 20 %) of the fuzzy mean as its standard deviation. The water demand is represented as a triangular fuzzy number with the random demand as its kernel, and the interval of ±5 % (or ±10 %) variation of the random demand as its support for two scenarios. The fuzzy random system reliability (R) of WDNs is defined on the basis of necessity measure to assess system performance under fuzzy random demands and crisp head requirements. The latin hypercube sampling method is adopted for sampling of uncertain demands. The methodology is applied to two WDNs, and optimization models are solved through cross entropy optimization for different levels of reliability, and generated tradeoffs between the cost and R. On comparing the solutions obtained with the proposed methodology with earlier reported solutions, it is noted that the proposed method is very effective in producing robust optimal solutions. On analyzing the tradeoffs between reliability and costs, the results show that negligence of uncertainty can lead to under design of the WDNs, and the cost increases steeply at higher levels of reliability. The results of the two case studies demonstrate that the presented CE based methodology is effective for fuzzy-probabilistic design of WDNs.