Preventive Maintenance Interval Research Articles

A study on two‐dimensional warranty with a preventive maintenance policy under burn‐in or run‐in

AbstractProducts are often offered with warranties, which have become a common marketing strategy intended to indicate that product quality is guaranteed by the firm. Warranties can attract consumer purchases, but they also incur warranty costs, especially when firms falsely claim that their products are high quality; therefore, the determination of an appropriate warranty policy is of particular importance. This study considers a two‐dimensional warranty for complex repairable products, such as a vehicle, a lathe, or industrial machinery, with consideration of (1) burn‐in (run‐in), which is a supervised accelerating testing process performed before product launch to prevent defective products from being sold on the market, and (2) periodic preventive maintenance, which is performed during the usage period to prevent the occurrence of unexpected failures under normal usage conditions. The optimal burn‐in (run‐in) time and preventive maintenance intervals are determined with the aim of minimizing warranty costs for firms. The results of the study show that given a higher repair cost and a greater infant mortality failure rate, more frequent preventive maintenance is preferable, and the repair cost has the largest effect on the total warranty cost for the firm.

Application of Reliability Centred Maintenance in Improving Aircraft Availability with Preventive Maintenance Intervention

Aircraft are intricate flying machines equipped with advanced technologies and state-of-the-art information and communication systems. Maintaining these aircraft to ensure maximum availability and minimal downtime is a critical and challenging task. Therefore, aircraft maintenance, repair, and servicing are of utmost importance. This research work aims to develop a methodology for reliability centred maintenance (RCM) to enhance aircraft availability. An RCM approach with the Multi attribute decision making (MADM) technique for assigning criticality of systems was implemented to improve aircraft availability. Failure mode and effects analysis (FMECA) was utilized for the failure investigation in the aircraft systems. However, instead of the Risk priority number (RPN) due to its inherent flaws, the Analytical hierarchy process (AHP) was utilized to evaluate the criticality of systems. The remedial measures to minimize the failure frequency and downtime were proposed. The improvement in availability, post implementation of proposed preventive maintenance was validated with positive results. An improvement of approximately 6.62% in availability and a reduction of about 15.71% in the total downtime was observed on the implementation of proposed preventive measures. The availability analysis was carried out for aircraft systems; however, the model could also be used for other repairable systems following a similar maintenance philosophy.

Analisis interval waktu perawatan preventif pada Paper Machine dengan metode reliability centered maintenance (RCM)

The maintenance process and preventive measures on paper machines need to be studied further, this will affect the production process and paper machine performance. This study aims to determine the criticality of the drying machine failure and determine the preventive measures that must be taken in the maintenance of the dryer and determine the time interval for the maintenance of the dryer machine components. To overcome this problem, this research was made using the Reliability Centered Maintenance (RCM) method. RCM can be interpreted as a process used in determining what to do for the machine, wherein in the RCM methodology there is one FMEA calculation in dealing with critical components in the dryer. Based on data processing and analysis using the RCM method, the maintenance time interval results from the maintenance of the dryer component. The results found are drums/cylinders with maintenance intervals of 82.7 hours, components of all pipes with maintenance intervals of 91.1 hours, and gear components with maintenance intervals of 110.7 hours. The implications for the company's factory regarding preventive maintenance intervals will have an impact on improving the production process and performance of paper machines through early prevention of machine failures that often occur.

Finite time preventive maintenance optimization by using a Semi-Markov process with a degraded state. A case study for diesel engines in mining

During the design of maintenance plans for an asset, the frequency calculation of preventive interventions is a maintenance engineer's essential task. This document presents a tool (mathematical formula) to calculate the preventive maintenance interval. The tool considers the maintenance costs and the income generated for the company by its use. Managers can benefit from its simple application to gain efficiency. Typically, business projects are executed over a period. The tool calculates the optimal interval for any given project duration. It also allows the analysis of the size of the preventive interval when the income received changes. This change may be due to asset impairment or changes in the market price of the business product. For the development of the tool, a semi-Markovian model is designed with four states representing the operation and maintenance of the asset. The asset evolves by transiting between states and accumulating costs and income in the form of returns. From the model, a system of difference equations is developed whose variable is the expected accumulated return at any transition, and the z-transform is used to solve it. Deriving its mathematical expression, the formula for the preventive interval that maximizes the expected accumulated return is obtained. Both formulae are used in a case study. Taking advantage of the degraded state, it is shown that higher income increases the interval size while lower-income decreases it. The limits of these variations are also established. Simulation and numerical methods are used to validate analytic results.

Fracture Analysis of a Cycloidal Gearbox as a Yaw Drive on a Wind Turbine

Fast growth of renewable energies has required addressing challenges such as generating the largest energy production possible throughout the equipment's lifespan and between its preventive and corrective maintenance intervals. Actions such as preventive maintenance, and improvement of the main components, mainly when it comes to reliability and predictability are of extreme importance to reach maximum generation. Due to the importance of the yaw drives for wind turbines, this paper aims to evaluate a failure that occurred on a cycloidal gearbox used in a drive of this kind. For the evaluation of the yaw drive, all the components were analyzed to determine the incurred fracture mechanism. Such analysis was performed by mapping all components, conducting a hardness test to check the components' mechanical properties, analysis of the fractured surfaces of the cycloidal disc, and numerical simulation (linear elastic) via the Finite Element Method (FEM) to check the stress distribution on the fractured part (cycloidal disc) under load, and theoretical calculation of the cycloidal disc lifespan. In addition, the stress distribution by FEM was compared with the broken regions of the physical part. To sum up, after all the evaluations, it is possible to claim the results demonstrate there was a premature fracture of the cycloidal disc that occurred due to the phenomenon of high cycle fatigue.

A novel refined maintenance strategy for full life cycle of high‐speed automatic train protection system

AbstractAutomatic train protection (ATP) system is essential for ensuring the safe operation of high‐speed trains. However, the existing extensive and fixed maintenance mode of the ATP system results in a waste of resources. To achieve a state of operation and maintenance that ensures both protection capability and economic efficiency, a lean method in a dynamic maintenance mode for the full life cycle of the ATP system is proposed. Firstly, reliability tests are carried out based on historical failure data. The parameter values of the possible life distribution are estimated by maximum likelihood method, and the optimal life distribution of different devices is obtained through the Kolmogorov–Smirnov hypothesis test. Secondly, a dynamic failure rate function is introduced to describe the impact of maintenance on device performance. A refined maintenance model is then established within the life cycle, and the dynamically changing preventive maintenance intervals and frequencies are obtained using a genetic algorithm. Finally, to mitigate the impact of the intermittent operation of ATP system on maintenance, the multidimensional relationships among the maintenance strategy, service time and operation mileage are revealed. The effectiveness of the proposed method is verified through an example test on a type of driver machine interface device.

Group Maintenance for Numerical Control Machine Tools: A Case Study

The high-reliability requirement of numerical control (NC) machine tools emphasizes the effectiveness of preventive maintenance (PM). However, most existing PM decisions are made by treating them as single-component systems, resulting in the problems of “excessive maintenance” or “insufficient maintenance.” Moreover, real failure data has rarely been utilized for maintenance decision-making, which leads to idealized policies far from practice. This article presents a framework to derive the optimal group maintenance (GM) policy from real failure histories for an NC machine tool comprised of heterogeneous components. Criticality analysis is first conducted to identify high-risk and low-risk components. The lifetime of each high-risk component is characterized by a two-parameter Weibull model or by a Weibull competing-risk model according to whether its failures are independent of the other components. High-risk components with strictly increasing or bathtub-shaped failure rates are selected for PM decision-making. This article introduces a multilevel PM decision-making approach: 1) a repair-replacement model is developed at the component level to derive the optimal PM interval and the maximum number of PM actions; and 2) at the system level, a GM procedure is presented to obtain the optimal PM interval of the system. The failure histories of an NC machine tool with 13 components are used to illustrate the effectiveness of the proposed approach. A maintenance plan is finally made based on optimization results to guide maintenance implementation.

Decision Making Reliability Approach for Maintainability of Economizer of Coal Based Boiler

This paper investigates the maintainability of economizers of three coal-based boilers for making decision of selection of replacement or maintenance practices. A sudden failure in the economizer can result huge production loss hence, a suitable decision model is essential which provides a strong data base for making optimal decision. A practical model for making decision is developed with respect to failure/repair data of three different economizers and trend with serial correlation tests are conducted. An investigation is carried out to determine the idealized statistical probability distribution providing best fit distribution. Further their Reliability and Maintainability are calculated. The tests of trend and serial correlation show very fast increasing failure rate of economizer # 1&2 with respect to economizer # 3. The present paper highlights that the failure data of economizer # 1 and economizer # 2 follow the NHPP model with poor reliability condition which indicates that these economizers should be replaced to restore boiler system as new as. However, failure data of economizer #3 follows the Lognormal distribution with poor reliability condition which suggests to develop the Preventive Maintenance Interval (PMI) to restore the boiler system. The present model serves a strong ground to decide replacement/preventive maintenance policy for not only to economizers but also to any pressure parts of coal based boilers.

Straightening machine preventive maintenance intervention plan based on AHP: a case study in a steel company in Brazil

Equipment failure and production shortfalls can cause significant impacts on companies. Preventive maintenance can potentially increase equipment reliability and availability, helping companies achieve their corporate goals while reducing operation and maintenance costs. However, determining the optimal interval between maintenance interventions involves balancing various factors such as maintenance costs, equipment availability, risk of failures, and the need for corrective maintenance, as well as potential operational and financial impacts. Under this context, this paper aims to define the interval between preventive maintenance interventions for a key production equipment in a steel company located in Southeast region of Brazil. Upon documental analysis and interviews with experts, an AHP was applied to hierarchize the maintenance periodicity, based on the following criteria: part replacement based on service life, downtime hours for preventive maintenance, equipment reliability, and cost of materials and labour. A biweekly preventive maintenance periodicity was identified as the most adequate for the analysed device. An implementation plan was approved and preliminary results indicate no availability deterioration, while maintenance costs are below historical average.

Integrated Optimization Model for Maintenance Policies and Quality Control Parameters for Multi-Component System

The practical applications of integrated maintenance policies and quality for a multi-component system are more complicated, still rare, and incomplete to meet the requirements of Industry 4.0. Therefore, this work aims to extend the integration economic model for optimizing maintenance policies and quality control parameters by incorporating the Taguchi loss function for a multi-component system. An optimization model is developed based on preventive maintenance, corrective maintenance policies, and quality control parameters with the CUSUM (Cumulative Sum) chart, which is widely used for detecting small shifts in the process mean. The model was developed to minimize the expected total cost per unit of time and to obtain the optimal values of decision variables: the size of samples, sample frequency, decision interval, coefficient of the CUSUM chart, and preventive and corrective maintenance intervals. The solution steps were employed by selecting a case study in the Alahlia Mineral Water Company (AMWC). Then, the design of experiments based on one-factor-at-a-time was used to evaluate the effect of selected decision variables on the expected total cost. Finally, sensitivity analysis was performed on the selected decision variables to demonstrate the robustness of the developed model. A predictive maintenance plan was developed based on the optimal value of preventive maintenance interval, and the results showed that the performance of the maintenance plan realizes the full potential of the integrated model. In addition, the case study results indicate that the extended integrated model for multicomponent is the new standard for the quality production of multi-component systems in future works.

An energy-efficient collaborative strategy of maintenance planning and production scheduling for serial-parallel systems under time-of-use tariffs

The industrial sector is the largest consumer of total energy in the world, with the majority of its consumption in the form of electricity. Recently, to strengthen the capacity building of peak load regulation, many countries have implemented time-of-use (TOU) tariffs to encourage manufacturing enterprises to shift their electricity consumption from on-peak hours to mid-peak and off-peak hours. It brings the urgent requirement for energy-efficient operation and maintenance (O&M) schedules in manufacturing enterprises, among which the maintenance planning and production scheduling (MPPS) are both highly correlated to electricity consumption under this time-varying electricity charging modes. To tackle this key issue, a complex MPPS problem for serial-parallel manufacturing systems under TOU tariffs is studied in this paper. To solve the problem, an energy-efficient two-stage maintenance (ETM) strategy is developed to minimize the sum of the total electricity cost and tardiness cost. In the first stage, the preventive maintenance (PM) planning is presented to obtain the multi-attribute PM intervals for each machine, considering machine availability, maintenance cost, and the potential impact of planned PM actions on average electricity price. Based on the PM intervals, a mixed-integer programming model is proposed for a hybrid flow shop schedule with PM actions in the second stage. Finally, the results confirm the effectiveness of this ETM strategy in achieving electricity cost savings and ensuring system productivity, which can provide instructions for the operation of industrial enterprises.

Stochastic analysis for estimating track geometry degradation rates based on GPR and LiDAR data

The estimation of track degradation rate is a challenging step in scheduling preventive maintenance interventions due to its uncertain nature. To tackle this challenge, a large dataset of historic passenger track geometry data, between 2011 and 2021, integrated with Ground Penetrating Radar (GPR), and Light Detection And Ranging (LiDAR) data are investigated. Stochastic models are proposed for estimating the expected track degradation rates based on subsurface and drainage conditions of blocks and also a framework is proposed for automatically evaluating drainage conditions of track from LiDAR data. Results indicate that the probability of having high track degradation rates in highly fouled ballast condition can be 10 times more than in clean ballast condition. In addition, analyzing the effect of drainage conditions revealed that sections with poor drainage conditions are 30% more likely to have high track degradation rates than sections with moderate drainage conditions. The results of this study enhance scheduling geometry maintenance and provide reliable information for decision making on different types of maintenance such as Ballast Cleaning and ditching.

A New Integrated Risk-Assessment Model for Minimizing Human-Machine Error Consequences in a Preventive Maintenance System

In this paper, a risk-assessment model for minimizing human-machine error consequences during the implementation of preventive maintenance tasks has developed. The developed model aimed to find the optimal preventive maintenance interval (PMI) and corresponding minimum risk consequences costs per unit of time. Based on expert judgment, a human error probability (HEP) model was developed using the success likelihood index methodology (SLIM). In addition, we developed a system failure probability (SFP) model based on the failure and repair data of the system. The HEP model was integrated with an SFP model to develop a human-machine error model. Then, the risk-assessment model was developed based on the consequences of system failure and human error. The effectiveness of the developed human-machine error model was shown by applying a numerical example for a multi-component series system. The optimization toolbox from MATLAB R2022b was applied to solve the developed human-machine model. The results showed that the optimal PMI can be implemented after 24 working hours to 105 hours under the acceptable limit of risk in the system which is 100 SR/h. Finally, the developed model is an effective method that can be applied to a wide range of manufacturing systems to minimize financial risks related to system inspection and maintenance while meeting safety and availability requirements.

Optimisation of Reliability and Maintenance Plan of the High-Pressure Fuel Pump System on Marine Engine

Abstract This paper presents a method of adjusting and designing the maintenance scheme for the high-pressure fuel pumps of a slow-speed two-stroke marine engine, MAN 6S70MC-C. The maintenance database for the marine fuel system was obtained from the planned maintenance software, and covered a period of 11 years. During this period, 29 failures occurred that required corrective actions. Our methodology includes failure mode analysis, risk analysis, reliability calculation and maintenance interval adjustment. Each failure is described using a failure mode analysis, based on a combination of the mode and cause of failure. The objective of this study is to recommend a new preventive maintenance interval based on the exponential reliability results and the analysed maintenance data. The initial maintenance plan for each fuel pump was set to 8,000 running hours, whereas in the modified plan, it is recommended to set this to 4,000 hours. Our results show an increase in the system reliability from 60% to 95% when the new modified maintenance plan is applied. In addition, the results and the recommended initial maintenance schedule are validated based on three similar types of engine with the same fuel pump system. The new maintenance approach can reduce the risk of component failure, which will lead to increased reliability of the fuel pump system and the optimisation of maintenance costs.

Residual lifetime estimation for the mining truck tires

Reliability analysis has a very important role in supporting the operation of a system in harsh working conditions such as mines. Haul trucks are considering as a main asset of an open pit mine. Dump trucks are comprised of different sub-systems and among them, tires have a significant effect on the haulage economy. This study focused on the remaining life estimation of the mining truck tires considering the environmental operating conditions at an open pit iron ore mine in Iran. To achieve this, the proportional hazard model was used to estimate the conditional reliability functions and then the mean residual life of tires at the various initial survival times. The results of this study show that the ambient temperature has a significant effect on the reliability performance. By increasing the initial life of tires from 500 to 1000 h, the mean residual life will be decreased by 21% and 30% in low (below 15°C) and high (above 15°C) ambient temperature levels. These results are helpful to keep the truck tire performance at a desirable level by considering the appropriate preventive maintenance intervals.

Optimal preventive maintenance interval model with time value of money consideration

The search for a plant maintenance strategy to mitigate the impact of random factory machine failures has never been more challenging. Apart from the harsh global competition, manufacturing facilities have become more sophisticated and expensive; frequent and prolonged breakdowns may ruin a firm’s business. Consequently, many have turned to the Preventive Maintenance (PM) strategy for a solution. However, the efficacy of PM has been shown to depend on finding a maintenance time interval decision model capable of incorporating most of the relevant maintenance cost components. While past studies have considered maintenance system set-up, execution, and breakdown cost components, the literature is sparse on decision models incorporating the cost of borrowing money. Exploring the feasibility of developing such a model is the aim of this study. Accordingly, a maintenance-investment cost function was formulated in terms of PM time interval and plant reliability; a PM decision problem was then defined and solved as that of PM investment cost minimization. Using three cases from the literature, the optimal maintenance time intervals (238.11, 14695.28, and 2076.54), failure probabilities (0.71, 0.35, and 0.61), and costs for preventive maintenance (PM) (840950.87, 149352.55, and 33979.57) were found to be reasonable.

Optimal preventive maintenance interval model with time value of money consideration

Optimal preventive maintenance interval model with time value of money consideration

Dynamic Preventive Maintenance Optimization of Subway Vehicle Traction System Considering Stages

The current maintenance mode of the subway vehicle traction system does not consider the change of failure rate in the operation of components, and there is an unreasonable maintenance interval and high maintenance cost. To solve this problem, a dynamic preventive maintenance strategy for subway vehicle traction systems considering stages is proposed. Service age decreasing factor and failure rate increasing factor are introduced to build a new dynamic reliability model considering the effect of preventive maintenance on component failure rate. Non-preventive failure maintenance, preventive maintenance, preventive update maintenance and opportunistic maintenance are adopted. For the components in degradation or random failure stages, a preventive maintenance optimization model with the least maintenance cost, constrained by reliability, is established. The maintenance strategy is optimized and solved based on the enumeration method to obtain single-component irregular preventive maintenance intervals and multi-component group preventive maintenance times. The results show that the proposed maintenance models are reasonable and feasible. On the premise of ensuring the system reliability, the number of vehicle outages for maintenance and the maintenance cost are reduced. Feasible and effective maintenance models and methods are provided. They can ensure the reliability of subway vehicles and reduce maintenance costs. The effectiveness and economy of maintenance work is increased.

A predictive multi-objective condition-based maintenance (CBM) policy considering ecological factors

PurposeThe purpose of this paper is to propose an improved multi-objective optimization model for the condition-based maintenance (CBM) of single-component systems which considers periodic imperfect maintenance and ecological factors.Design/methodology/approachBased on the application of non-periodic preventive CBM, two recursion models are built for the system: hazard rate and the environmental degradation factor. This paper also established an optimal multi-objective model with a normalization process. The multiple-attribute value theory is used to obtain the optimal preventive maintenance (PM) interval. The simulation and sensitivity analyses are applied to obtain further rules.FindingsAn increase in the number of the occurrences could shorten the duration of a maintenance cycle. The maintenance techniques and maintenance efficiency could be improved by increasing system availability, reducing cost rate and improving degraded condition.Practical implicationsIn reality, a variety of environmental situations may occur subsequent to the operations of an advanced manufacturing system. This model could be applied in real cases to help the manufacturers better discover the optimal maintenance cycle with minimized cost and degraded condition of the environment, helping the corporations better fulfill their CSR as well.Originality/valuePrevious research on single-component condition-based predictive maintenance usually focused on the maintenance costs and availability of a system, while ignoring the possible pollution from system operations. This paper proposed a modified multi-objective optimization model considering environment influence which could more comprehensively analyze the factors affecting PM interval.

Optimization of Two-Dimensional Extended Warranty Scheme for Failure Dependence of a Multi-Component System with Improved PSO–BAS Algorithm

Human society is entering the era of Industry 4.0; engineering systems are becoming more complex, which increases the difficulties of maintenance support work. Failure dependence exists widely in multi-component systems. In this work, a model of two-dimensional (2D) warranty decision making was constructed by using a failure-dependence analysis for multi-component systems and by considering the extended warranty cost and the system availability. The decision was to cut the warranty cost as much as possible for manufacturers, while the constraint condition was the minimum acceptable availability for the customer. The model combined preventive maintenance as well as corrective maintenance strategies. Under the condition that the multi-component system is replaced upon the expiration of the extended warranty (EW), the optimal 2D EW duration and preventive maintenance interval could be obtained through the model. In a case analysis, the optimal EW scheme for the gearbox of an electric multiple unit (EMU) system was obtained by using a grid search algorithm, a PSO algorithm, and a PSO–BAS algorithm. Through comparison, the PSO–BAS algorithm obtained a better scheme with lower warranty costs and higher system availability. A comparative analysis and a sensitivity analysis showed that the model provides a theoretical basis for manufacturers to optimize their 2D extended gearbox warranties.