Maintenance Cost Model Research Articles

Reliability-Based Preventive Maintenance Strategy for Subsea Control System

The subsea control system, a pivotal element of the subsea production system, plays an essential role in collecting production data and real-time operational monitoring, crucial for the consistent and stable output of offshore oil and gas fields. The increasing demand for secure offshore oil and gas extraction underscores the necessity for advanced reliability modeling and effective maintenance strategies for subsea control systems. Given the enhanced reliability of subsea equipment due to technological advancements, resulting in scarce failure data, traditional reliability modeling methods reliant on historical failure data are becoming inadequate. This paper proposes an innovative reliability modeling technique for subsea control systems that integrates a Wiener degradation model affected by random shocks and utilizes the Copula function to compute the joint reliability of components and their backups. This approach considers the unique challenges of the subsea environment and the complex interplay between components under variable loads, improving model accuracy. This study also examines the effects of imperfect maintenance on degradation paths and introduces a holistic lifecycle cost model for preventive maintenance (PM), optimized against reliability and economic considerations. Numerical simulations on a Subsea Control Module demonstrate the effectiveness of the developed models.

Prediction of the maintenance cost of general aviation aircraft based on engineering method

Purpose General aviation aircraft has a wide range of applications, and effective cost management is one of the hot spots in the research of general aviation manufacturers. The purpose of this paper is to build a flexible engineering method to predict maintenance cost of general aviation aircraft. Design/methodology/approach To establish a reasonable general aviation aircraft maintenance cost prediction model, it is necessary to analyze the influencing factors and extract the main components of maintenance cost. The maintenance cost is divided by engineering method, and the estimation model of each component cost is established. Then, the general aviation aircraft maintenance cost model is obtained. The results show that the relative error of this method is between 13% and 20%, which has a good estimation accuracy and can be effectively used to estimate the maintenance cost of general aviation aircraft. Findings The maintenance cost plays an important role in the life cycle cost of general aviation aircraft. Accurate cost prediction method is of great significance to the optimal design of general aviation aircraft. However, there are few prediction models suitable for maintenance cost, the proposed approach is meaningful and quite desirable. Originality/value To some extent, this method overcomes the shortage of the work on maintenance cost prediction for general aviation aircraft. The model established in this paper has certain generality, which can provide some reference for general aviation aircraft design and operation enterprises.

점검-정비 모형에서 열화부품의 발견확률을 고려한 시스템의 최적 점검주기

Purpose: The determination of the optimal inspection interval of a system is a critical factor that can influence its maintenance. Frequent inspections yield shorter inspection intervals and higher inspection costs compared with infrequent inspections, which lead to an increase in the total maintenance cost due to the frequent occurrence of failures. The objective of this study is to determine the optimal inspection interval, which minimizes the total expected cost until the system is overhauled.BRMethods: We developed an inspection - maintenance cost model by considering the unit inspection cost, the cost for preventive maintenance, and the cost for corrective maintenance as well as the cost and quantity of a component, the distribution of the lifetime of components, and the probability of finding fault or failure during inspections.BRResults: We propose a novel method for determining the optimal inspection interval, which minimizes the total expected cost. The proposed method can be applied to systems consisting of one or more types of components. The data, which is derived from the case study that is documented in the previous research [13], are utilized to illustrate that the proposed method is effective for determining the optimal inspection interval and for conducting sensitivity analysis. Finally, we apply the proposed method to determine the optimal inspection interval pertaining to a component of auxiliary power that is built into railway trains. Consequently, we observed that a relationship exists between the reduction in the cost of operating the system and the current inspection interval.BRConclusions: The proposed method can be effective for other repairable systems, which undergo inspection, preventive maintenance, and corrective maintenance.

Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems

Currently, in manufacturing, massive useful data about health condition and maintenance is often available thanks to Industry 4.0 technologies. However, how to take advantage of historical data to optimize maintenance policies for multi-component systems has still been a challenging problem. This is especially true when maintenance cost models at component level are not available and/or maintenance actions are imperfect. In order to cope with this issue, we propose in this paper an artificial-intelligence-based maintenance approach which first constructs a predictor based on artificial neural network (ANN) for estimating maintenance cost at system level and then employs a customized multi-agent deep reinforcement learning algorithm to optimize maintenance decisions that can be applied for large-scale systems. To evaluate the performance and scalability of the proposed maintenance approach, numerical studies are conducted on a small 4-component system with different configurations and a large system composed of 15 components considering both deterministic and random maintenance quality. The simulation results show that ANN-based predictor is efficient for maintenance cost forecasting and multi-agent deep reinforcement learning is a promising solution for maintenance decision-making and optimization.

Dynamic Scheduling of Intelligent Group Maintenance Planning under Usage Availability Constraint

Maintenance, particularly preventive maintenance, is a crucial measure to ensure the operational reliability, availability, and profitability of complex industrial systems such as nuclear asset, wind turbines, railway trains, etc. Powered by the continuous advancement of sensor technology, condition-based group maintenance has become available to enhance the execution efficiency and accuracy of maintenance plans. The majority of existing group maintenance plans are static, which require the prescheduling of maintenance sequences within fixed windows and, thus, cannot fully utilize real-time health information to ensure decision-making responsiveness. To address this problem, this paper proposes an intelligent group maintenance framework that is capable of dynamically and iteratively updating all component health information. A two-stage analytical maintenance model was formulated to capture the comprehensive impact of scheduled maintenance and opportunistic maintenance through failure analyses of both degradation and lifetime components. The penalty functions for advancing or postponing maintenance were calculated based on the real-time state and age information of each component in arbitrary groups, and the subsequent grouping of the time and sequence of components to be repaired were iteratively updated. A lifetime maintenance cost model was formulated and optimized under a usage availability constraint through the sequential dynamic programming of group sequences. Numerical experiments demonstrated the superior performance of the proposed approach in cost control and availability insurance compared with conventional static and periodic maintenance approaches.

Optimization of preventive maintenance schedule of subway train components based on a game model from the perspective of failure risk

With the sustainable development of urban transportation systems, the safety and economy of subway trains have become important criteria. However, in the process of formulating the operation and maintenance strategy of subway trains, there is always a conflict of interest between the operator, who aims to improve the operation reliability, and the maintainer, who aims to reduce the maintenance cost. To resolve this conflict, this paper proposes a preventive maintenance decision-making method for subway train components based on a game model. A failure-rate model based on the memory factor was developed and combined with the dynamic attenuation law of a subway train. The failure risk of subway train components was quantitatively evaluated, and a risk penalty cost model was established. Next, a maintenance cost model based on the maintenance reliability and economy was developed. Then, a non-cooperative dynamic game model with operators and maintainers as players was developed, and the balance was solved using the particle swarm optimization algorithm. Finally, the preventive maintenance of the bogie axle box of a subway train was examined for validation. The validation results indicated that the proposed method can balance the interests of the subway train operation and maintenance departments and improve the operation and maintenance decision-making in the field of sustainable cities. • Multiple failure consequences are considered in preventive maintenance cost model. • The influence of multi-parameter combination on maintenance strategy is applied. • Conflicts of interest among decision-making participants are resolved. • The policy recommendations are provided at both the operational and strategic levels.

A hybrid maintenance policy with fixed periodic structure and opportunistic replacement

We model a maintenance policy with fixed periodic structure that is a hybrid of periodic inspection and opportunistic replacement. The policy is applicable to geographically remote systems such as offshore wind farms. The policy has three phases. Initially, there is an inspection phase to identify early defects. This is followed by a wear out phase during which corrective replacements are performed. Preventive replacement occurs at the end of this phase. The novelty of the model is an opportunistic phase, which overlaps with the latter part of the corrective phase, when preventive replacement is executed early if an opportunity arises. In this way, we model the reality in which remote systems with high logistics costs and restricted access may benefit from opportunistic visits for maintenance. Using a numerical example, we analyse the behaviour of the decision variables for a range of values of the parameters common to such systems. These parameters relate to: component heterogeneity; restricted access; default (failure to execute a planned action); arrival of opportunities and other standard parameters in a maintenance cost model. Specifically, our results indicate when opportunities can have a significant impact on the cost-rate of the optimum policy, but that leveraging opportunities cannot achieve a very high availability. Generally, we demonstrate that maintenance planning should be flexible when factors beyond the control of the maintainer impact maintenance effectiveness.

Robust Bi-Level Planning Method for Multi-Source Systems Integrated With Offshore Wind Farms Considering Prediction Errors

Considering the economy, reliability, and output characteristics of multiple power sources (MPS) and energy storage (ES) comprehensively, a multi-source system integrated with offshore wind farms (OWFs) and its construction cost, and operating and maintenance cost model are established. The system is mainly composed of OWFs, thermal power plants, gas turbine power plants, and pumped hydro storage plants. Given the economy of the power system and offshore wind power accommodation, a bi-level optimal capacity configuration and operation scheduling method is proposed for the multi-source system integrated with OWF clusters with the objective function of optimal total cost. Then, a robust bi-level planning method for the multi-source system integrated with OWFs considering the dual uncertainty of load and offshore wind power prediction is proposed, in which the upper and lower models are solved by an improved particle swarm optimization (PSO) algorithm and CPLEX solver, respectively. Based on the method, the cost-optimal capacity configuration and operation scheduling scheme of an MPS and ES can be obtained. Finally, an OWF group in Shandong Province is taken as an example to check the validity and feasibility of the proposed method.

Estimation of Annual Routine Maintenance Cost for Highway Tunnels

In highway management, the prediction of the routine maintenance cost of tunnels is an important issue in saving tunnel maintenance costs due to its uncertainty, and the influencing factors should be carefully selected because too many variables could not be involved in the model. The complicated relationship between variables may lead to the inconsistency of model coefficients with the actual situation even though the goodness of fit of the model constructed with more variables is higher. This paper presents an approach in which quantitative analysis is combined with qualitative analysis to quickly select the independent variables of the tunnel routine maintenance cost (TMC) model. Based on the routine maintenance data collection of nine highway tunnels in Shaanxi province from 2007 to 2016, the independent variables of the models are determined with one‐way ANOVA, Pearson correlation, partial correlation, and hierarchical regression. Afterwards, a fixed‐effect regression model which can reflect the overall regional features is developed. Results show that tunnel age (Age) and tunnel length proportion (PET) have less effect on TMC among the main influencing factors such as district, Age, annual average daily traffic volume (AADT), truck traffic volume proportion (PTT), PET, and number of ventilation facilities (NVF), while the NVF makes a positive contribution to the TMC. Compared with grouped regression models, the fixed‐effect regression model has higher fitting accuracy and a better regression coefficient significance. The quick independent variable selection method can shorten the time of establishing the model and determine the influencing factors of the research object effectively. The established model is suitable for forecasting the TMC and budget arrangement. In addition, the elastic analysis results of regression coefficients are helpful to the decision of maintenance strategy and the allocation of maintenance funds.

Risk assessment of FPSO topside based on generalized Stochastic Petri Net

This paper presents a Generalized Stochastic Petri Net (GSPN) approach to assess risk of FPSO topsides, considering the weather window and imperfect maintenance effect. The FPSO topside is considered as a multi-component system consisting of several assemblies. A GSPN model is developed according to system identification and the correlation between different components. The reliability, availability, and maintenance information of the system are derived using the model, based on failures collected from existing literature and database. An operation and maintenance cost model is established, and an optimized maintenance strategy is finally determined based on the GSPN outcomes. The results indicate that the proposed approach is an effective tool to evaluate the system risk and save the maintenance expenditure of FPSO topsides.

An Imperfect Debugging Software Reliability Growth Model: Optimal Release Problems through Warranty Period based on Software Maintenance Cost Model

Software come to be an important element in recent times, from small residence hold gadgets to large machinery wishes fine software. software development is a technical oriented system where range of quantitative and qualitative duties have been completed parallel a good way to meets the needs of the consumer. Many people play a vital role within the improvement of software program product, consequently there is chance of committing errors by way of these humans and these errors becomes faults in later stages. Computing software program cost for the duration of software development can facilitate us predicting the time of release of the software. In this paper we have investigated release time of software program by way of considering the imperfect debugging software program reliability growth model and cost model.

Optimizing Sensor Deployment and Maintenance Costs for Large-Scale Environmental Monitoring

Recent advances in low-power long-range communication schemes such as LoRa have opened up new potentials in large-scale Internet-of-Things (IoT) applications, especially environmental monitoring. However, the versatile environment and the long traveling distance have imposed significant challenges to maintenance. Previous research has shown that higher temperature exponentially accelerates electronics failure rates. The maintenance cost can take as much as 80% of the total deployment expenses if not managed carefully. In this article, we formulate a sensor deployment problem to preventively minimize maintenance costs while ensuring tolerable sensing quality and complete connectivity. We are the first to derive a maintenance cost model for IoT networks considering thermal degradation and battery depletion. To assess the spatial phenomena of interest, we adopt the sensing quality metric based on mutual information. While the proposed problem is nonconvex, we bring up a relaxed form and solve it with a sparse nonlinear optimizer. We further apply two population-based metaheuristics, i.e., particle swarm optimization (PSO) and artificial bee colony (ABC) algorithm, to approximate the optimal solution. Extensive simulations are performed on two real-world datasets of the Southern California region in the U.S. Our metaheuristics save up to 40% of maintenance cost compared with the existing greedy heuristics under the same acceptable sensing quality.

Time-varying reliability modelling and quasi-static accuracy optimization of precision CNC machine tools

Aiming at the influence of time-varying characteristics of geometric error on precision CNC machine tool quasi accuracy retention, a time-varying reliability accuracy maintenance method based on multi-objective reliability and maintenance cost tradeoff is proposed. Firstly, on the basis of geometric error transfer model and Achard adhesive wear theory, a quasi-static accuracy reliability limit state function to meet time-varying characteristics is established and the time-varying accuracy reliability is solved based on the First order second moment method. Secondly, according to the time-varying characteristics of the quasi-static accuracy of machine tools, the maintenance cost model and reliability multi-objective optimization model are established, and the trade-off between cost and reliability function is realized. Finally, the optimization results of three-axis machining center show that the method can realize the optimization of geometric accuracy and determine the quasi-static error compensation value which meets the requirements of reliability accuracy.

Equipment Maintenance Management in Manufacturing Companies: An Application for Total Maintenance Costs Model

Üretim işletmelerinde, operasyonların kesintisiz olarak sürdürülebilmesi ekipmanların çalışmalarını sorunsuz bir şekilde yerine getirebilmesine bağlıdır. Bu nedenle yapılan bakım faaliyetleri, üretim işletmelerinin hedefleri içerisinde yönetilmesi gereken bir konudur. Ekipman bakımının iyi yönetilmesi, işletme performansını doğrudan etkilemektedir. Diğer taraftan bakım işlemleri, maliyet gerektiren faaliyetleri içermektedir ve işletmeler tarafından fayda maliyet analizi doğru yapılmadığı sürece maliyet boyutu dikkate alınmaktadır. Ancak yetersiz bakımın neden olduğu problemler ortaya çıkarıldığında, daha büyük maliyetler olduğu görülmektedir. Bu çalışmada ekipman bakımının yönetilmesi için toplam bakım maliyetleri üzerine çalışılmaktadır. Bir firmanın bakım faaliyetleri analiz edilerek, yetersiz bakımın neden olduğu durumlar ile toplam bakım maliyetleri modeli uygulanmaktadır.

Towards A Framework for Development of Operational and Maintenance Cost Model of Highway Project in Malaysia

A major role of cost model is to ensure that the resource being handled is maintainable, functional and secure. The last decade has shown that attention to the idea of cost model added value to accomplish this management. In the context of cost model research and practice, the purpose of this paper is to identify, compare and describe the different cost models and investigate their value parameters. A systematic literature review of various publications has been followed in order to categorize the different theoretical models and to analyze the key quality parameters in the cost model. The established eight theoretical models were described in the sense of cost and the variant research projects and structures have taken into account very diverse parameters. In cost models, this analysis organizes 11-various quality parameters into four relevant headings: individuals, system and material, economy and social. Among all value parameters, cost reduction and customer satisfaction, followed by efficiency, are the most prioritized value parameters. This paper results provide a sound basis and realistic interpretation of future research to harmonize the definition of cost model added value as it is based solely on literature review.

A predictive maintenance cost model for CNC SMEs in the era of industry 4.0

Within the subject area of maintenance and maintenance management, authors identified a deficiency in studies focussing on the expected value from adopting predictive maintenance (PdM) techniques for machine tools (MTs). Authors identified no studies focussing on presenting a PdM value analysis or cost model specifically for small-medium enterprises (SMEs) operating computer numerically controlled (CNC) MTs. This paper’s novelty is addressing SMEs’ minimal representation in literature by explanatorily collecting data from SMEs within the focal area via surveys, modelling and analysing datasets, then proposes a cost-effective PdM system architecture for SME CNC machine shops that predicts cost savings ranging from £22,804 to £48,585 over a range of 1–50 CNC MTs maintained on a distributed numerically controlled (DNC) network. It affirms PdM’s tangible value creation for SME CNC machine shops with predicted positive impacts on their MT cost and performance drivers. These exploratory research findings corroborate SMEs pooling together to optimise their CNC MT maintenance cost through the recommended system architecture. Finally, it introduces opportunities for further PdM research taking into account SMEs’ perspective. The paper’s industrial application is confirmed from the surveyed SMEs that demonstrated their current utility of PdM; then anonymous positive feedback on the online dashboard, shared with participant companies, confirmed the research results supported SMEs in considering exploring the path to adapting PdM. It is anticipated that beneficiaries of this research will be maintenance managers, business executives and researchers who seek to understand the expected financial and performance impact of adopting PdM for a MT’s overall life cycle costs.

Optimal Maintenance Design-Oriented Nonprobabilistic Reliability Methodology for Existing Structures Under Static and Dynamic Mixed Uncertainties

The performances of engineering structures are degrading in service and maintenance interventions can enhance the structural reliability and make sure that the structures are effectively functional. Engineering structures often exhibit obvious time cumulative effects due to multisource time-varying uncertainties including property degradation of materials, changeable environment conditions, and dynamic loading processes, which makes the reliability assessment and maintenance design much harder. In this paper, a new time-varying nonprobabilistic reliability-oriented optimal maintenance design method with the consideration of static and dynamic mixed uncertainties is proposed. The interval model is utilized to quantify the uncertain and time-varying characteristics and the structural reliability can be assessed combining the interval model and the first-passage theory. We try to establish a more general maintenance cost model enlightened by Frangopol's work under the nonprobabilistic system and the maintenance strategy including uncertainty assessment, reliability evaluation, and cost optimization is constructed. Three numerical examples are presented to demonstrate the effectiveness of the developed methodology.

Determining the repair and maintenance cost of wood chippers

Chipping weighs heavily on the total delivered cost of wood fuel, which calls for accurate chipping cost estimates. Chipper repair and maintenance cost is perhaps the most obscure among the figures required for a reliable estimate of chipping cost. To clarify this issue, the authors examined the long-term repair and maintenance records for 51 wood chippers operated by 48 chipping service contractors. Repair and maintenance represented between 1.5% and 29% of total chipping cost, inclusive of fuel and labour (mean = 14%). The ratio between total accumulated repair (TAR) cost and machine price averaged 32% and varied with total use. The relationship between total use and TAR to price ratio was explored through regression analysis, which produced a very strong model (R2 = 0.8). This model predicts a TAR to price ratio of 0.64 at 10000 h, with no significant differences between tractor-driven and independent-engine machines. While the coefficients are different, the structure of this model offers a good match with the repair and maintenance cost models developed for farm tractors and other agricultural machinery. A second model was developed for estimating repair and maintenance cost as a function of fuel consumption. The study also provides reference figures for the contribution of labour cost to total maintenance cost. Farm tractors incur more repairs than preventive maintenance, contrary to the other dedicated components of the chipping operation, where preventive maintenance represents most of maintenance cost. Forestry users should make allowance for the lower structural strength of tractors and select large models.

Cost evaluation challenges for Internet of Things (IoT) based Product/Service-Systems (PSS)

Increasingly cost competitive Internet of Things embedded solutions support the transition of traditional business models to Product-Service Systems. One of the most challenging but rewarding PSS types is maintenance. This work aims to discuss the differences between a traditional time/use-based maintenance model in opposition to an IoT-enabled maintenance cost model, by conducting a case study in a machinery industry company in partnership with an IoT provider. Findings show that economy of scale is observed with a decrease in the need for repair visits. The ubiquitous nature of IoT allows for continuous customer support, reinforcing better equipment practices and increasing performance.

A multi-event combination maintenance model based on event correlation.

Due to the complexity of large production systems, maintenance events are diverse, simultaneous and dynamic. Appropriate maintenance management of complex large production systems can guarantee high availability and save maintenance costs. However, current maintenance decision-making methods mainly focus on the maintenance events of single-components and series connection multi-components; little research pays attention to the combination maintenance of different maintenance events. Therefore, this paper proposes a multi-event combination maintenance model based on event correlation. First, the maintenance downtime and cost of three types of maintenance events under different maintenance beginning times and degrees are analysed. Then, shared maintenance downtime and cost models are established by maintenance event correlations. In addition, a multi-event combination maintenance model is constructed to achieve the goal of the highest availability and the lowest cost rate in both the decision-making cycle and the remaining life. Moreover, a particle swarm optimization algorithm based on interval segmentation for model solving is designed. Finally, a numerical example is presented to illustrate the model.