Preventive Maintenance Cost Research Articles

Biological concrete, a viable alternative in modern civil construction: Review

Concrete and/or mortar are the most commonly used construction materials and have a high tendency to form cracks. These cracks lead to a significant reduction in the useful life of the concrete structure and high preventive maintenance costs. Although it is not possible to prevent cracks from forming, there are several types of techniques to heal cracks. It has been shown that some of the current concrete treatment methods, such as the application of chemicals and polymers (epoxy), are a source of health and environmental risks and, more importantly, are only effective in the short term. Therefore, alternative, environmentally friendly and long-lasting methods are being developed with high potential for use and at lower prices than conventional concrete. A microbial self-healing approach is distinguished by its potential for lasting, rapid and active crack repair, while also being capable of maintaining the quality of the environment. Furthermore, the self-healing approach mainly by anaerobic and aerobic bacteria prevails over other treatment techniques due to the efficient binding capacity and compatibility with concrete compositions in the formation of media capable of protecting the concrete structure by forming Calcium as a product of special metabolism. This study provides an overview of microbial approaches to produce calcium carbonate (CaCO3) and application of bioconcretes in civil construction. They discuss everything from conventional concrete, concrete pathologies to forms of remediation in the development of high quality and resistant bioconcrete based on a systematic review. Finally, this review focuses on the use of bioconcrete as a viable and sustainable alternative for modern civil construction that combines the use of alternative materials for self-healing and environmental maintenance.

Optimal preventive maintenance: Balancing reliability and costs in the electricity market

This study addresses the challenge of achieving optimal preventive maintenance within power systems, aiming to balance reliability and costs effectively. The primary focus is on understanding the relationship between preventive maintenance expenditures and the failure rate of essential transmission components, particularly transformers in substations. We contribute to the existing literature by providing two key insights. Firstly, we establish a theoretical framework for determining the optimal level of preventive maintenance, which can be extended across various electricity facilities. By analyzing the relationship between preventive maintenance costs and failure rates, our goal is to identify the investment level that ensures a reliable power system while minimizing financial burdens. Secondly, we explore the functional form that accurately characterizes the link between preventive maintenance costs and failure rates. The diminishing marginal rate of failure, dependent on different functional forms, highlights the variability of optimal preventive maintenance expenses. Utilizing Lasso regression, we identify the functional form that optimally characterizes the relationship between outage occurrences and maintenance costs. These insights extend well beyond the confines of the Korean power industry, offering a sustainable approach to managing transmission facilities and enhancing the overall stability and efficiency of global electricity networks.

A rolling horizon for rolling stock maintenance scheduling problem with cyclical activities

The rolling stock maintenance scheduling problem addressed here involves scheduling a set of cyclical preventive maintenance activities while considering resource capacity and geographically dispersed depots. The objective is to minimize both corrective and preventive maintenance costs using reliability features. We propose to use rolling horizon to decompose the problem into several successive subproblems such that each one tackles the scheduling of only the next cycle of activities instead of all cycles. The scheduled dates to be fixed should be as close as possible to their respective deadlines. Each train can have three status (in service, in maintenance, out of service). The activities deadlines are determined depending on component failure rate which has to be respected otherwise the train becomes out of service. An integer linear programming (ILP) model is proposed to solve the subproblem using a time oriented modeling to get an accurate maintenance cost (preventive and corrective) and respect a minimum of available trains. The rolling horizon allows to build a feasible schedule within a predefined planning horizon for moderate size problems with an interesting trade-off between computation time and solution quality.

Maintenance cost optimisation in critical single-component systems: A technician’s training approach based on a joint learning–forgetting and fuzzy maintenance quality model

Effective maintenance in critical systems is crucial to ensure their operability and safety. Maintenance technicians play an important role in achieving the safety and reliability objectives of critical system components. However, the role of the technical team in maintenance quality is often overlooked. Our study addresses this knowledge gap by developing a methodology to optimise the training scheduling of maintenance technicians in single-component systems, focusing on a learn-and-forget approach. Our main objective is to improve maintenance efficiency by mitigating the negative impact of technician expertise level. To this end, we implemented a simulation-based optimisation, modelling the evolution of technician experience and its influence on maintenance costs. As a case study, we used an urban aerial passenger ropeway system, demonstrating that proper training of technical staff can significantly reduce both corrective and preventive maintenance costs. Our methodology identified three strategic areas for training technical staff throughout the system lifecycle. The results highlight the importance of investing in the training scheduling of technical equipment in critical systems and suggest that an appropriate training strategy can improve operational efficiency and reduce maintenance costs. In conclusion, our research underlines the need to actively consider the human factor in the maintenance of critical systems to ensure their optimal performance and long-term safety.

A Condition-based Maintenance Policy in Chance Space

A condition-based maintenance policy is considered for a deteriorating system including both of preventive and corrective maintenance actions. The gamma process is used to model stochastic degradation in the probability space. Although, the cost of preventive maintenance is considered as an uncertain variable due to incomplete information, and its distribution is estimated based on the opinions of some experts using the Delphi method. The optimal policy is determined by minimizing the expected cost rate function. Since in this function, there are both random variables discussing in a probability space, and an uncertain variable, which is considered in an uncertain space, we have to study the optimal policy in a chance space which is a combination of probability and uncertain spaces. The proposed methodology is explained in an illustrative example. Finally, the results are applied to a real data set.

Is it wise for manufacturers to implement preventive maintenance in the sharing business model?

In the sharing business model, manufacturers typically increase customer usage through product design but contend with escalating failure costs stemming from heightened customer usage and moral hazard concerns. The preventive maintenance (PM) typically serves as a means for manufacturers to tackle the growing failure cost problem. However, manufacturers face the challenge of how to make PM strategies in intricate operational environments. We formulate a theoretical model to investigate when a manufacturer should implement PM, taking into account customer usage and product design. Our results suggest that when the fixed cost of PM is low and the customer moral hazard is not sufficiently high, or when both the fixed cost of PM and the customer moral hazard are at moderate levels, it is wise for the manufacturer to implement PM. We also find that the PM can lead to higher consumer surplus by increasing customer utility, achieving a win-win situation. In addition, our findings reveal a positive correlation between the PM effort and product quality, with higher PM effort leading to higher product quality and thus increasing customer usage. In extension models, we further explore the issues of maximising social welfare, non-zero maintenance time and endogenous maintenance frequency

Optimal Preventive Maintenance Scheduling of Multi State System. A Comparative Study of Different Meta-heuristic Algorithms

This work presents a comparative study of different meta-heuristic algorithms which are commonly used in the literature, such as Genetic Algorithms, Particle Swarm Optimization, Artificial Bee Colony and Grey Wolf Optimizer. These algorithms are applied to solve a multi-state system maintenance optimization problem considering time and system availability constraints. The objective is to find the optimal inspection and maintenance intervals for each component of the system in order to minimize the preventive maintenance cost of overall the system. The performances of the algorithms are compared using different metrics, regarding the quality and stability of the results and the speed of convergence, in order to determine the most efficient algorithms.

Precision Sensitivity Optimization for Parallel Robotic System Based on Multi-Source Preventive Maintenance

This paper presents a multi-source preventive maintenance (MPM) based precision sensitivity optimization method for parallel robotic systems. Taking a parallel mechanism as an example, the input error can be divided into finite mutually independent uncertain error sources, and the probabilistic error model is established by the Monte Carlo stochastic method. Considering that several times of maintenance can lead to an increase of the accuracy degradation factor, a generalized hierarchy maintenance yield model is established, where the cost of preventive maintenance and failure replacement maintenance are distinguished between the independent error source and the overall error source. The nonlinear relationship between the maintenance yield per unit cost and influencing factors, including the error threshold and the maintenance times is obtained, which allows to develop an optimal maintenance strategy. Preventive maintenance is introduced to avoid unintended failure of the mechanism while extending the lifetime. The high-precision parallel mechanism has demonstrated promising applications in medical, industrial and other fields, and its economic benefits can be effectively improved by incorporating the MPM method. The experiment of manufacturing human lung models using a parallel 3D printing device demonstrates that the MPM method can improve the long-term precision and reliability of the parallel device, and the optimized human lung contour tracking precision can be improved by up to 55.56%.

A chance-constrained net revenue model for online dynamic predictive maintenance decision-making

Most models for predictive maintenance (PdM) decision-making focus on the expected value of a system performance metric (e.g., the expected cost rate). However, focusing solely on such expected values may overlook the significant risks of resulting PdM decisions. Indeed, it would be practically valuable to make the optimal PdM decision by considering the least probability of achieving a target system performance. Furthermore, emphasizing only maintenance costs without accounting for operational revenues can render the maintenance strategies unappealing, particularly when the revenues across system lifecycles are significant. This study introduces a method that employs a stochastic net revenue model and chance-constrained programming to address these issues in PdM decision-making, wherein the preventive maintenance cost is proportional to the system's current degradation state. By exemplifying system degradation with the Gamma process, we derive the probability density function of stochastic net revenue and present a Bayesian method to simultaneously update the drift and diffusion parameters of the Gamma process model. The effectiveness and practical applicability of our proposed chance-constrained net revenue model and parameter updating method are demonstrated through a numerical example and a case study.

A dynamic condition-based maintenance policy for heterogeneous-wearing tools with considering product quality deterioration

The wear of a cutting tool can lead to tool failure and product quality deterioration, and thus timely maintenance of tools is crucial. Meanwhile, the wear of tools from a same population usually exhibits heterogeneous patterns. Therefore, this paper proposes a dynamic condition-based maintenance (CBM) policy for heterogeneous-wearing tools with considering the product quality deterioration caused by tool wear. The tool wear is modelled by an Inverse Gaussian (IG) process, and the wear rate is assumed to be a random variable to characterise the heterogeneity among tool wear processes. The posterior distribution of reciprocal of tool wear rate is dynamically estimated using the online wear data based on a Bayesian approach. Moreover, the impact of tool wear on product quality deterioration is modelled. The IG process is discretized into a discrete time Markov chain (DTMC). Under the frame of the DTMC, a cost function, containing product quality loss, preventive maintenance (PM) cost and corrective maintenance cost, is developed to determine the optimal PM threshold. The cost function updates dynamically with the dynamic estimation of tool wear rate and thus enables the optimal PM threshold to be dynamically revised. The effectiveness of the proposed CBM policy is demonstrated through a case study.

Optimization of preventive maintenance in production lines: an approach based on reliability statistics

The maintenance strategy must be taken from the results required by the organization, that is, aiming to meet the budgeted costs, the defined quality and the production program. Therefore, it is up to this strategy to seek to maximize effectiveness in the relationship: use of resources, costs and useful life. This article aims to define a preventive maintenance strategy for a production line, using RCM concepts, aiming to determine an optimal periodicity for the execution of this type of maintenance activity and consequently the reduction of business costs. Experimental data were obtained from the previously referenced article, linking them to the concepts of Reliability-Centered Maintenance and the Maximum Likelihood Estimator. With this, it was possible to obtain the optimal stop times for preventive maintenance of the studied machines, assuming a ratio of costs of preventive maintenance and corrective maintenance. By following the reliability techniques covered throughout the article, a company's production line can ensure that its assets are running smoothly and that any potential issues are addressed efficiently.

Mathematical model for the calculation of life cycle cost of the Kamaz truck

Currently, research on life cycle and product life cycle management is of interest in many countries, especially for products of high complexity and long service life. One of the issues of product life cycle management is the calculation and determination of product life cycle costs. Calculating the product life cycle costs will allow managers and users to know the cost that needs to be spent on acquiring the product (purchase cost), as well as corrective maintenance cost, preventive maintenance cost, operation cost, disposal cost, and also indicates the economic efficiency of purchasing, operating and using this product.This paper presents the mathematical model allowing a truck’s life cycle cost calculation and performs calculations for a specific Kamaz 43119/43118 truck. The calculation results allow evaluation of the contribution level of the cost components in the product life cycle, thus allowing managers to build financial estimates for the life cycle stages and compare the calculated product life cycle costs with the types of products in suitable circulation.

Optimal external opportunistic maintenance for wind turbines considering wind speed

ABSTRACT A novel optimal opportunistic maintenance strategy for wind turbines considering wind speed as a stochastic process is proposed in this paper. Under this strategy, periodic preventive maintenance is conducted to reduce the probability of downtime due to failure, whereas external opportunities are leveraged to reduce the cost of preventive maintenance. This combined maintenance approach can also reduce costs associated with the wind turbine lifecycle. The formulated expected costs per unit time considered two scenarios, i.e. regular operation and energy generation interruption, to obtain the optimal solution via a genetic algorithm. Furthermore, we illustrated the economic advantages of the proposed strategy by comparing various maintenance strategies, as well as a sensitivity analysis to determine the effects of the different parameters in the proposed model. Experimental results demonstrate that the proposed opportunistic maintenance strategy is more cost-effective for wind turbines than comparable methods, with an optimal solution that varies according to variations in wind speed.

Multi-phase preventive maintenance strategy for leased equipment considering usage rate variation

Lessors are responsible for equipment maintenance during the lease period. The usage rate of these equipment affects their failures, reliability and decision-making on maintenance strategy. For some leased equipment, such as those producing seasonal products, the usage rate varies. However, conventional approaches usually set usage rate as a constant. Different from the existing literature, this work considers the variation of usage rate, and proposes a multi-phase preventive maintenance (PM) decision model to determine the PM times and degrees at different phases. The goal is to minimize lessors’ lease service cost by balancing corrective maintenance (CM) cost, PM cost and penalty cost during the lease period. In this model, an age correspondence framework is applied to characterize the variation of usage rate at different phases. An accelerated failure time model and an age reduction model are adopted to capture the effect of variable usage rate and imperfect PM on the equipment’s reliability. To verify the effectiveness of the proposed model, it is contrasted with a PM decision model with average usage rate. The impact of unit PM cost, CM cost, and penalty cost on maintenance strategy is discussed by numerical examples. Managerial insights for maintenance strategy are provided for lessors. The results indicate that the proposed approach considering varied usage rate can change the optimal PM times and degrees, and consequently lead to a lower lease service cost than conventional approaches using average usage rate; lessors need to enhance PM capability and reduce PM cost.

Comprehensive Analysis of IoT with Artificial Intelligence to Predictive Maintenance Optimization for Indian Shipbuilding

The extensive review of the literature evaluation on predictive maintenance (PdM) in this work focuses on system designs, goals, and methodologies. In the business world, any equipment or system failures or unscheduled downtime would negatively affect or stop an organization's key operations, possibly incurring heavy fines and irreparable reputational damage. Traditional maintenance methods now in use are plagued by a variety of limitations and preconceptions, including expensive preventive maintenance costs, insufficient or incorrect mathematical deterioration procedures, and manual feature extraction. The PdM maintenance framework is suggested as a new method of maintenance framework to prevent any damage only after the analytical analysis shows specific malfunctions or breakdowns, which is in line with the growth of digital building and the advancement of the Internet of Things (IoT), and Artificial Intelligence (AI), and so on. We also present an overview of the three main types of fault diagnosis and prognosis methods used in PdM mechanisms: scientific, conventional Machine Learning (ML), and deep learning (DL). While offering a thorough assessment of DL-dependent techniques, we make a quick overview of the knowledge-based and conventional ML-dependent strategies used in various components or systems. Eventually, significant possibilities for further study are discussed.

Preventive maintenance optimization for key components of subway train bogie with consideration of failure risk

With the acceleration of urbanization, the number of urban rail transit trains has increased dramatically, and the economy and safety of subway trains have become important standards. As an essential subsystem of the subway train, the bogie has a long maintenance process and serious failure consequences. So, it is essential to formulate a reasonable maintenance strategy to ensure its operation. To solve the above problems, a preventive maintenance (PM) strategy for key components of train bogie is established with consideration of failure risk in this paper. Firstly, the failure risk factors of the bogie components are scored and weighted, and the failure risk cost model and PM cost model are established. Next, a Weibull distribution parameter estimation method for bogie components based on the improved beluga whale optimization algorithm (IBWO) is proposed, providing a theoretical basis for PM decision-making optimization. Then, the Lévy flight is introduced into particle swarm optimization to enhance the optimization performance of the model and obtain the optimal solution. Finally, the bogie key components of Nanning Metro line 1 are selected as a case study. The results show that the IBWO algorithm has strong applicability and feasibility, and can accurately calculate the Weibull parameter values of the bogie key components. Compared with the PM plan without considering the failure risk, the proposed method is more economical and safer, which can provide necessary theoretical support for the maintenance decision optimization of urban rail transit train components.

Feature Extraction Based on Self-Supervised Learning for Remaining Useful Life Prediction

AbstractThe prediction of the remaining useful life (RUL) is of great significance to ensure the safe operation of industrial equipment and to reduce the cost of regular preventive maintenance. However, the complex operating conditions and various fault modes make it difficult to extract features containing more degradation information with existing prediction methods. We propose a self-supervised learning method based on variational automatic encoder (VAE) to extract features of data’s operating conditions and fault modes. Then the clustering algorithm is applied to the extracted features to divide data from different failure modes into different categories and reduce the impact of complex working conditions and fault modes on the estimation accuracy. In order to verify the effectiveness of the proposed method, we conduct experiments with different network structures on the C-MAPSS dataset, and the results verified that our method can effectively improve the feature extraction capability of the model. In addition, the experimental results further demonstrate the superiority and necessity of using hidden features for clustering rather than raw data.

Analisis Perawatan Komponen Chamber Box Pada Mesin Laminating Departemen Printing Produksi Kemasan Plastik (Studi Kasus:Pt.Daesang Ingredients Indonesia)

Miwon Indonesia is one of the companies that started as a company in 1973, the company finally changed the company's name with PT. Daesang Ingredients Indonesia The research method used is Preventive Maintenance, in this study researchers analyzed the time between laminating machine damage to the Chamber Box Heater component, the data taken was the Laminating machine down time data in July – December 2022 The results of analysis and data processing showed that the level of component reliability, namely for the chamber box heater component of 0.5892 occurred on day 5. And while the press roll rewinder component obtained a reliability level of 0.5925 on day 7. And the results of the calculation of costs before preventive maintenance are obtained at a cost of Rp. 47,953,572 per 6 months and costs after preventive maintenance are obtained at Rp. 29,677,876 per 6 months. With a difference of Rp.18,285,696. From the results of preventive maintenance costs, it can reduce maintenance costs at PT. Daesang Ingredients Indonesia than before preventive maintenance calculations are carried out.

A New Approach on Preventive Maintenance in Industry

Abstract The role of maintenance in industry is important to ensure optimal performance, reliability and longevity of equipment, machinery and infrastructure. This maintenance ensure the continuity of the production with minimal losses. The present paper present the types of maintenance and based on this it focused on developing a generalized software which can be applied mainly in mechanical production industry. The main positive aspects of the software are: it represent an open source through the option of adding equipment, it can deliver sheets with costs of preventive maintenance and lists of personnel. Maintenance activities play a significant role in supporting the production process, minimizing downtime, reducing costs and increasing overall operational efficiency.

Pré-requis à la mise en place de l’emballage Ultra® en remplacement des conteneurs au sein de trois blocs opératoires : analyse d’impact budgétaire

Budgetary impact analysis of the replacement of three surgical departments containers with a new perforation-resistant packaging: Ultra® pouches and reels. Comparison of projections of containers costs of use to those of Ultra® packaging over six years. For containers, the costs include washing, packaging, curative maintenance (annual costs), and preventive maintenance (every five years). For Ultra® packaging, the costs include the first year, the purchase of a suitable storage arsenal and a pulse welder, and the transformation of the transport system. Annual costs for Ultra® include packaging and welders maintenance and qualification. In the first year, the costs with Ultra® packaging are higher than the cost related to the container model because the initial investment for the installation is not completely offset by the cost of preventive maintenance of the containers. However, from the second year of use of the Ultra®, an annual saving of €19,356 is expected and up to €49,849 in the sixth year (new preventive maintenance of containers needed). In six years, a saving of €116,186 is expected, which represents 40.4% savings compared to the container model. The budget impact analysis is in favor of the implementation of Ultra® packaging. The expenses related to the purchase of the arsenal, a pulse welder and the adaptation of the transport system should be amortized from the second year. Significant savings are even expected.