Preventive Maintenance Frequency Research Articles

An assets maintenance-workforce planning problem under uncertainty: A chance constraints assisted simulation-optimization approach

Preventive and corrective maintenance-workforce planning for assets is a growing area of research, given the presence of uncertainty. We model a Simulation-Optimization framework to solve this challenging large-scale maintenance-workforce planning problem while considering its stochastic nature inside both the simulation and optimization models. A holistic simulation model is proposed through multiple modules to imitate different aspects of the maintenance process, i.e., spare facilities/storage and workforce. The workforce module represents technicians’ types, skills and levels, and employment life-cycle, including recruitment, promotion, and separation/retirement. The present uncertainty in the simulation model demands its corresponding optimization model to cope with the nature of the problem and guarantee a degree of confidence level of the optimal decisions. Therefore, a Chance Constraints Programming (CCP) method is integrated with an evolutionary optimization algorithm to limit the risk level of violating the random constraints while minimizing the total maintenance cost. Discrete Event Simulation (DES) and Self-Adaptive Differential Evolution assisted with Chance Constraints (SA-DE-CC) algorithm are coupled to solve the current problem. The optimization model is proposed to optimize the preventive maintenance frequency and workforce planning problem while implicitly considering corrective maintenance in the simulation model. The Taguchi method performs scenario and risk analysis for multiple CC probability distributions (i.e., Weibull and Normal), confidence levels, and critical parameter combinations. The model’s sensitivity analysis and managerial insights are also discussed.

Optimizing the preventive maintenance frequency with causal machine learning

Maintenance is a challenging operational problem where the goal is to plan sufficient preventive maintenance (PM) to avoid asset overhauls and failures. Existing work typically relies on strong assumptions (1) to model the asset’s overhaul and failure rate, assuming a stochastic process with known hazard rate, (2) to model the effect of PM on this hazard rate, assuming the effect is deterministic or governed by a known probability distribution, and (3) by not taking asset-specific characteristics into account, but assuming homogeneous hazard rates and PM effects. Instead of relying on these assumptions to model the problem, this work uses causal inference to learn the effect of the PM frequency on the overhaul and failure rate, conditional on the asset’s characteristics, from observational data. Based on these learned outcomes, we can optimize each asset’s PM frequency to minimize the combined cost of failures, overhauls, and preventive maintenance. We validate our approach on real-life data of more than 4000 maintenance contracts from an industrial partner. Empirical results on semi-synthetic data show that our methodology based on causal machine learning results in individualized maintenance schedules that are more accurate and cost-effective than a non-causal approach that does not deal with selection bias and a non-individualized approach that prescribes the same PM frequency to all machines.

Optimal maintenance tasks taking into account cost indicators

The reliability of technical systems plays a key role in ensuring their efficient operation over time. This article discusses the importance of reliability in maintaining preset parameters of the functioning of a system and its dependence on operating conditions. Maintenance is done to maintain the reliability of a system and control its functionality. The authors propose methods for selecting quality indicators and the optimal frequency of preventive maintenance and emphasise the importance of considering cost aspects when making decisions on maintenance. In conclusion, the article offers a brief algorithm for quality control of technical systems while placing an emphasis on system safety and economic efficiency. The study demonstrates the need to develop and apply new cost-based analysis methods to determine optimal maintenance strategies for technical systems. Overall, the article provides recommendations for improving maintenance operations and increasing the reliability of technical systems.

Transformer Population Failure Rate State Distribution, Maintenance Cost and Preventive Frequency Study Based on Markov Model

This work investigates the state distributions of failure rate, performance curve, maintenance cost and preventive frequency of the transformer population through the Markov Model (MM). The condition parameters data of the oil samples known as Oil Quality Analysis (OQA), Dissolved Gas Analysis (DGA), Furanic Compounds Analysis (FCA) and age were analyzed from 370 distribution transformers. This work utilized the computed failure rate prediction model of the transformer population based on MM using the nonlinear minimization technique. First, the transition probabilities for each state were adjusted based on pre-determined maintenance repair rates of 10%, 20%, and 30%. Next, the failure rate state distributions and performance curves at various states were analyzed. Finally, the maintenance costs and preventive maintenance frequency were estimated utilizing the proposed maintenance policy models and the failure rate state probabilities. The result reveals that the transition from state 2 to state 1 with a 30% pre-determined maintenance repair rate can provide an average reduction of failure rate up to 11%. Based on the failure rate state probability, an average increment of maintenance cost from RM 18.32 million to RM 251.87 million will be incurred over 30 years. In total, 85% of the transformer population must undergo maintenance every nine months to avoid reaching very poor conditions.

Sustainability of water facilities under community based management in Zimbabwe

Abstract Community Based Management (CBM) has been envisaged as a panacea to sustainability challenges faced in the Water Sanitation and Hygiene (WASH) sector globally. In spite of this approach having success stories, studies have shown that failures are also being recorded. This study investigated the sustainability of rural water supply facilities under the CBM approach in Chipinge District of Zimbabwe. The specific objectives were to assess the technical, financial and institutional factors influencing sustainability. A cross sectional study design was used. Data was collected using Focus Group Discussions, Key Informant Interviews, household questionnaires and an observation checklist. Quantitative data was analysed using SPSS and Microsoft Excel while qualitative data was analysed using the thematic approach. Results showed that the technical factors which are influencing sustainability are age of the water facility, frequency of carrying out preventive maintenance and existence of external support. Regression analysis showed a positive linear correlation between age and breakdown frequency (R2 = 0.46) and the odds of obtaining a breakdown decreased as the frequency of preventive maintenance increased. User communities were contributing inadequate O&M funds resulting in long downtimes. Non-functionality of WPCs negatively influenced sustainability. The study recommends training of user communities on CBM and technical skills.

An Artificial Neural Network Model for Maintenance Planning of Metro Trains

In urban transportation, trains have an increasingly important place due to the increase in the number of passengers. Meeting the number of passengers is directly related to the number of trains operated on a line. Thus, the frequency of operation of trains affects the level of wear of the equipment. This makes train maintenance more important. Equipment faults are the basis for train maintenance. However, the fault times of the equipment which are unknown causes uncertainty in the maintenance activities and plans. This uncertainty results from many factors that affect the faults of the train. If historical maintenance data, fault data, and factors affecting the faults are known, effective use of resources (time, cost and personnel, etc.) is provided and uncertainty is eliminated. In this study, firstly, maintenance data in Ankara Metro between 2017 and 2018 is examined and the factors affecting equipment faults are evaluated with expert opinion. Artificial Neural Network (ANN) model is created with the data set and this data set along with the factors affecting each the equipment fault according to the type of equipment. In the ANN model, 5 factors (Equipment Type, Preventive Maintenance Frequency, Material Quality, Life Cycle, Line Status) affecting the faults of the equipment is determined as inputs and the number of failures as outputs. The mean absolute percent error (MAPE) value is found as 11%, and the mean square error value (MSE) is 0.0028229 in the training and test stages of ANN. Then, the frequency of fault is found according to the equipment fault and a 10-week maintenance planning is applied. The results are compared with current maintenance planning. As a result of the applied maintenance planning, the average number of faults of the trains decreases by 27%, uninterrupted service rate increases by 40% and heavy maintenance errors are also prevented. Fault removal times resulted in a 10% improvement. The results showed that ANN models could be used effectively in fault prediction and maintenance planning with rail system multiple types of equipment. In the literature, there is no study that implements maintenance planning with an ANN model where all train equipments and factors affecting the failure are evaluated together. This study is the first in the field of rail systems maintenance in the literature and will be a reference for future studies.

Joint optimization for product warranty and preventive maintenance in service supply chain

Industrial competition has been extended from the traditional price and quality competition to warranty competition, from the single manufacturer competition to the whole supply chain system competition. Existing research mainly focuses on the decision-making problems of warranty period and price. However, with the increasing complexity and safety requirements of CNC machine tools and other engineering equipment, customers have put forward higher requirements for product quality and equipment reliability. In this paper, the equipment product deteriorating with aging is taken as the research object, and a two-level service supply chain profit model under Stackelberg game with strong manufacturer bundled sales of “product + warranty” is constructed. Moreover, the impact of warranty period and preventive maintenance frequency on the equilibrium price of product-service package is discussed. This paper provides a new and scientific approach for the optimization of product service strategy.

Comparative Analysis of Existing and Optimal Maintenance Policy of Water Borehole Schemes in South Eastern States of Nigeria

A study optimising maintenance cost of water borehole schemes in South Eastern states of Nigeria (Abia, Anambra, Ebonyi, Enugu and Imo States) was carried out. Data was collected from 260 boreholes spread across all local government areas in the states. Optimisation results showed that for boreholes (submersible pumps) pumping once per day, the optimal preventive maintenance frequency and resulting savings in cost are 2 and ₦521,076 for Abia; 2 and ₦783,963 for Anambra; 2 and ₦458,242 for Ebonyi; 2 and ₦740,964 for Enugu; 2 and ₦605,187 Imo. For boreholes pumping twice per day, the optimal preventive maintenance frequency and resulting savings in cost are 5 and ₦1,896,301 for Abia; 4 and ₦3,692,655 for Anambra; 5 and ₦786,913 for Ebonyi; 4 and ₦4,187,161 for Enugu; 4 and ₦2,477,609 for Imo; and for boreholes pumping thrice per day; 8 and ₦2,798,330 for Abia; 7 and ₦8,372,862 for Anambra; 7 and ₦6,485,293 for Ebonyi; 10 and ₦4,014,240 for Enugu; 10 and ₦6,021,503 for Imo; with no downtime as opposed to the wasteful current practice of no preventive maintenance with downtime of up to 12 months or more. As a recommendation for a borehole scheme, there should be a check on the type of submersible pump and generator capacity as the choice made directly affects the total operational cost.

Life cycle assessment of greenhouse gas emissions from asphalt pavement maintenance: A case study in China

Abstract Timely maintenance is crucial to ensure the long-term performance of asphalt pavements. Due to the large amount of greenhouse gases (GHG) emitted during construction, it is important to evaluate the environmental impacts of various maintenance technologies, so that the whole life impacts of asphalt pavements can be understood and reduced. This paper estimated the GHG emissions of 16 maintenance technologies in the four categories of surface seal or coating system, thin overlay system, hot recycling system and cold recycling system by using life cycle assessment (LCA), and identified the technologies and the combination of them that have the least GHG emissions. In order to compare the maintenance technologies on a level-playing basis, this study designed maintenance scenarios for the entire life cycle of the pavement, and established a system boundary where all maintenance scenarios are compared transparently for a defined functional unit. Results show that, among the preventive maintenance technologies, micro-surface and fog seal have the lowest GHG emissions. In order to meet the same performance requirements of highway pavement, cold recycling with extra overlay will generate more than twice GHG emissions that of hot recycling. Increasing the frequency of preventive maintenance, and reducing the occurrence of corrective maintenance can reduce GHGs emissions by 30%–45% in pavement life. Reasonably designed maintenance plan for the entire life cycle can significantly reduce the GHG emissions of asphalt pavements.

Stationary Characteristics of an Unreliable Single-Server Queueing System with Losses and Preventive Maintenance

This paper considers an unreliable restorable single-server queueing system with losses in which server failures may occur during operation. The random variables describing the system have general distributions. For increasing the efficiency of this system, it is proposed to carry out preventive maintenance of the server as soon as the accumulated operating time exceeds an upper permissible threshold. A semi-Markov model of the system’s evolution over time is constructed. The explicit-form expressions for the final probabilities and mean sojourn times of the system in different physical states are derived using the stationary distribution of the embedded Markov chain. The frequency of preventive maintenance of the server is optimized via maximizing the average specific profit and minimizing the average specific costs of the system.

Integrated maintenance/spare parts management for manufacturing system according to variable production rate impacting the system degradation

This study develops an analytical model in order to determine an optimal integrated maintenance plan and spare parts management. We consider a manufacturing system, producing only one type of product, over a finite planning horizon H equal to the sum of all production periods and the production quantity of each period is known. This system is subject to a continuously increasing degradation rate. That is why a preventive maintenance strategy is adopted in order to face the increasing failure rate. We noted that contrarily to the majority of studies in literature, we take into account the impact of the production rate variation on the manufacturing system degradation and consequently on the adopted optimal maintenance strategy. In addition, the real need of spare parts relative to the scheduled maintenance actions is taken into account. In fact, the purpose of our study consists at determining the optimal preventive maintenance frequency and the optimal quantity of spare parts to order by minimizing a total cost, including maintenance and spare parts management. Numerical examples are presented along with a sensitivity study in order to prove the use of the developed model for deriving the optimal integrated strategy for any instance of the problem.

Performance evaluation of the remanufacturing system prone to random failure and repair

ABSTRACTImplementation of new environmental legislation and public awareness has increased the responsibility of manufacturers. Remanufacturing has been applied in many industries and sectors since its introduction. However, only 10% to 20% of the returned products pass through the remanufacturing process, and the remaining products are disposed in the landfills. Uncertainties like high failure rates of the servers, buffer capacities, and inappropriate preventive maintenance policies would be responsible for most of the delays in remanufacturing operations. In this paper, a simulation-based experimental methodology is used to determine the optimal preventive maintenance frequency and buffer allocation in a remanufacturing line. Moreover, an estimated relationship between preventive maintenance frequency and Mean Time Between Failure (MTBF), is presented to determine the best preventive maintenance frequency. The solution approach is applied to computer remanufacturing industry. Analysis of variance (ANOVA), and regression analysis are performed to denote the most influential factors to remanufacturing cycle time (performance measures). A case study is used to show the applicability of the modelling approach in assessing and improving the cycle time, and the profit of a remanufacturing line . Managerial insights are highlighted to support managers and decision-makers in their quest for more efficient and smooth operation of the remanufacturing system.

Optimal delivery due date for a supplier with an unreliable machine under outsourced maintenance

In this paper, we analyse the due-date-to-promise problem for a supplier who supplies parts to his customer. The parts are produced on an unreliable machine with constant production rate. The machine is leased from a contractor who is also entrusted with its maintenance. The supplier's decision is the optimal due date to promise to his customer, taking into account the holding and tardiness costs on the customer side, as well as the transfer payment to the contractor for the lease and maintenance of the equipment. The contractor in turn has to decide on the frequency of pre-ventive maintenance. Both maximize their own profits. For this finite horizon optimization problem, we employ a Non-Renewal-Theory based approach from the literature to derive the performance characteristics in the transient regime - first for a general setting involving general distributions and then, for a special case. We then use Game Theory to analyse the underlying two-member game for both the non-cooperative and cooperative cases. A numerical example drawn from the literature is used to illustrate the special case. We show that for the special case, the game has a unique Nash equilibrium. Another significant result is that the Nash solution dominates the Stackelberg solution when the supplier is the leader. The supplier is worse off when the contractor is the leader. The example further shows that the supplier and the contractor stand to gain under cooperation which is well documented in the literature. We point to relevant literature for strategies to enforce this cooperation.

Preventive maintenance optimization using a Hybrid Multi-Objective Evolutionary Algorithm

This paper is focused on the problem of preventive maintenance optimization in a manufacturing environment, to determine the optimal preventive maintenance frequencies for equipment under cost and profit criteria, considering production, quality and maintenance aspects. The paper is based on a previously developed maintenance model, to execute a benefit and cost optimization process using a Hybrid Multi-Objective Evolutionary Algorithm (Hybrid MOEA) that combines a global search method with a local one. The hybrid algorithm combines the capabilities of both worlds, using a global search technique to effectively explore wide parameter spaces, deal properly with function non-linearities and avoid falling into local optimal solutions, and combining it with the capacities of local search methods to efficiently converge into local optimal solutions. The hybridization is done according to two different schemes. Firstly, ‘a posteriori’ scheme has been implemented, where the MOEA runs for a number of generations obtaining an approximation of the Pareto front to apply then a local search from each non-dominated solution of the front. Secondly, an ‘on-line’ scheme has been developed, where in each generation (or after a reduced number of generations) of the evolutionary algorithm a local search is applied on each non-dominated solution to return then the improved solutions to the MOEA as the current population. Both hybrid schemes have been applied to an industrial manufacturing case where the benefit of implementing the hybrid optimization approach is shown, by comparing the hybrid schemes with the MOEA.

A non-linear fuzzy degradation model for estimating reliability of a polymeric coating

Oftentimes, material components follow non-linear degradation because the material loss trough time is non-linear; the components present different levels of resistance during a degradation process. Constructing predictions of the component life span and performing reliability analyses regarding degradation is possible by non-linear regression. Generally, this method ignores the prediction error in the extrapolated time and it does not account measurement error in the observed sample paths. In this paper, it is proposed new means for predicting time to failure of the components, using a calibration regression method for measuring the error prediction in the extrapolation process. In order to consider the uncertainty, we proposed to perform the estimated times by a trapezoidal fuzzy numbers, providing a more reliable prediction without loss of information. Since the times to failure are shaped by fuzzy numbers, it was adapted the fuzzy probability theory to the classical reliability analysis for estimating the fuzzy reliability of a component. An accelerated test of a polymeric coating process was performed and also an application for a preventive maintenance frequency was proposed.

State of the art in simulation-based optimisation for maintenance systems

Recently, more attention has been directed towards improving and optimising maintenance in manufacturing systems using simulation. This paper aims to report the state of the art in simulation-based optimisation of maintenance by systematically classifying the published literature and outlining main trends in modelling and optimising maintenance systems. The authors investigate application areas and published real case studies as well as researched maintenance strategies and policies. Much of the research in this area is focusing on preventive maintenance and optimising preventive maintenance frequency that will lead to the minimum cost. Discrete event simulation was the most reported technique to model maintenance systems whereas modern optimisation methods such as Genetic Algorithms was the most reported optimisation method in the literature. On this basis, the paper identifies the current gaps and discusses future prospects. Further research can be done to develop a framework that guides the experimenting process with different maintenance strategies and policies. More real case studies can be conducted on multi-objective optimisation and condition based maintenance especially in a production context.

Combined production-maintenance decisions in situations with process deterioration

This article addresses an imperfect production system with process deterioration and how best to maintain the system. In the system there is a constant percentage to produce defective products in the ‘in-control’ state and a higher percentage to produce defective products in the ‘out-of-control’ state. The production process may shift randomly from an in-control state to an out-of-control state during a production run; i.e. system process deterioration. This article considers both preventive maintenance and corrective maintenance. The objective is to determine the optimal production run time and preventive maintenance frequency while still minimising the total annual cost. We provide two efficient algorithms for solving the problems when considering the preventive maintenance frequency as discrete and continuous, respectively. Finally, we apply a numerical analysis to study the effects of changing parameter values on the optimal solutions and to point out some management implications.

Optimal spares and preventive maintenance frequencies for constrained industrial systems

The research investigates the influence of an effective maintenance system on the efficient performance of any industrial system. The core concept of the research explains that the simultaneous focus on the spares inventory subsystem as well as on the preventive maintenance subsystem must be considered when developing a quality maintenance programme. Considering and developing such aspects separately will lead to suboptimal performance since there exists a trade-off between overstocking and undersupplying spares for preventive maintenance activities. Details on the technique chosen are discussed, namely simulation modelling as well as recent developments such as agent based modelling. Advantages of this technique are the flexibility in representing complex relationships within a system without knowing the exact form. The optimisation heuristic, a genetic algorithm, which was used to solve the research problem is explained. Finally a case study is used to demonstrate the aptness and success of the research approach, namely an annual 44% maintenance cost saving and 3% increase in production output.

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Research of preventive maintenance effect on reliability of fault-tolerant uninterruptible power supply is important in determination of the requirements for maintenance. In this paper the objects under consideration are two variants of the fault-tolerant system for uninterruptible power supply with (N+M) and 2×(N+M) redundant configurations, which are provided by preventive maintenance. There are given reliability models for both configurations of faulttolerant system for uninterruptible power supplies, which allows investigating the dependence of their reliability on the frequency of preventive maintenance, in this paper. Reliability models for fault-tolerant systems are formed as graphs of states and transitions. Determination of the reliability parameters is associated with the solution of system of Kolmogorov Chapman differential equations, formed according to the developed graphs. These models are useful both at the design stage (to determine the structure of the fault-tolerant system) and during operation (to determine the requirements for maintenance) of uninterruptible power supply, designed as a fault-tolerant system.

Centers for Medicare & Medicaid Services Maintenance Requirements Self-evaluation

On December 2, 2011, the Centers for Medicare & Medicaid Services released its Clarification of Hospital Equipment Maintenance Requirements. This documents sets out Centers for Medicare & Medicaid Services requirements in the much discussed arenas of frequency of preventive maintenance and the associated maintenance procedures when preventive maintenance does occur. These requirements are more stringent than those that many have argued for, and they may reflect a shortfall in current practices for some hospitals. Given the now detailed statement of requirements, it is now prudent to undertake a self-assessment of compliance with these requirements. This article offers a simple assessment tool for this purpose.