Proposed Maintenance Policy Research Articles

Proposed maintenance policy using reliability centered maintenance (RCM) method with FMECA analysis: A case study of automotive industry

PT XYZ is one of the companies engaged in the automotive industry, especially in the motorcycle industry that produces spare parts on motorcycles. One component made by PT XYZ is Guide Comp Level K1AA. The problem in the company is one of the machines to produce the Guide Comp Level K1AA component, which is a Press machine that has a high frequency of failure. The Press machine itself consists of electrical and mechanical systems that each system consists of sequential subsystems as many as 6 subsystems and 10 subsystems. By using risk matrix, obtained two critical subsystems namely brake and trigger. Then the analysis of failure mode, effects and criticality analysis (FMECA) was carried out with output in the form of risk priority number (RPN), obtained that the selected critical subsystem is the brake. Using the reliability centered maintenance (RCM) method, then obtained the maintenance policy for the brake critical subsystem are 2 scheduled on condition task with interval time is 2.55 months and 2 scheduled restoration task with 5,394 months. The proposed maintenance policy also decreases the total maintenance cost for the critical subsystem brake up to 25% from existing maintenance costs.

A Combined Maintenance Strategy Considering Spares, Buffer, and Quality

This paper studies an innovative maintenance model for an upstream machine in a common production system. Multiple system-wise features are integrated in the model, including maintenance, spare parts, buffer inventory, and product quality. The objective is to minimize the system expected cost rate by determining the optimal inspection interval, preventive maintenance threshold, corrective maintenance threshold, reorder level of spare parts, maximum stock level of spare parts, and maximum inventory level of the buffer. In this maintenance model, both the hazard rate and the product qualification rate can trigger maintenance activities. The well-known proportional hazard model (PHM) is adopted to describe the state of the upstream machine. The effect of machine state on product quality is explicitly characterized. Due to the complexity of the mathematical model, simulation and a genetic algorithm are employed to determine the optimal solution. A case study of bearings is presented to demonstrate the performance of the proposed maintenance policy. The results show that this policy is practical and can reduce system cost significantly.

Maintenance policy structure investigation and optimisation of a complex production system with intermediate buffers

Existing research about the maintenance optimisation of production systems with intermediate buffers largely assumed a series system structure. However, practical production systems often contain subsystems of ring structures, for example, rework and feedforward. The maintenance optimisation of these complex systems is difficult due to the complicated structure of maintenance policies and the large search space for optimisation. This paper proves the control limit property of the optimal condition-based maintenance policy. Based on the control limit property, approximate policy structures that incur a smaller policy space are proposed. Because the state space of a production system is often large, the objective function of the maintenance optimisation cannot be evaluated analytically. Consequently, a stochastic branch and bound (SB&B) algorithm embedding a sequential simulation procedure is proposed to determine a cost-efficient condition-based maintenance policy. Numerical studies show that the proposed maintenance policy structures can deliver a cost-efficient maintenance policy, and the performance of the SB&B algorithm is enhanced by the inclusion of a sequential simulation procedure.

Dynamic maintenance based on functional usage profiles

PurposeFor many decades, it has been recognized that maintenance activities should be adapted to the specific usage of a system. For that reason, many advanced policies have been developed, such as condition-based and load-based maintenance policies. However, these policies require advanced monitoring techniques and rather detailed understanding of the failure behavior, which requires the support of an OEM or expert, prohibiting application by an operator in many cases. The present work proposes a maintenance policy that relieves the high (technical) demands set by these existing policies and provides a more accurate specification of the required (dynamic) maintenance interval than traditional usage-based maintenance.Design/methodology/approachThe methodology followed starts with a review and critical assessment of existing maintenance policies, which are classified according to six different aspects. Based on the need for a technically less demanding policy that appears from this comparison, a new policy is developed. The consecutive steps required for this functional usage profiles based maintenance policy are then critically discussed: usage profile definition, monitoring, profile severity quantification and the possible extension to the fleet level. After the description of the proposed policy, it is demonstrated in three case studies on real systems.FindingsA maintenance policy based on a simple usage registration procedure appears to be feasible, which enables a significantly more efficient maintenance process than the traditional usage-based policies. This is demonstrated by the policy proposed here.Practical implicationsThe proposed maintenance policy based on functional usage profiles offers the operators of fleets of systems the opportunity to increase the efficiency and effectiveness of their maintenance process, without the need for a high investment in advanced monitoring systems and in experts interpreting the results.Originality/valueThe original contribution of this work is the explicit definition of a new maintenance policy, which combines the benefits of considering the effects of usage or environment severity with a limited investment in monitoring technology.

An integrated model for maintenance policies and production scheduling based on immune–culture algorithm

The development of integrated modelling for maintenance policies of multi-component repairable system and production scheduling is challenging for two reasons. First, capturing dependency of this multi-component repairable system is difficult because different failure types associated with different components are under competing risks and their complicated relationships may lead to overall system dependency. Second, the integrated model is difficult to optimize because it is an NP-hard problem that exact optimization methods are intractable. For coping with these two difficulties, we propose a parametric statistical model using copula function to capture the overall system dependency. Under partially perfect maintenance policy at component-level, the likelihood functions for observed failures are derived and maximum likelihood method is used to estimate unknown parameters. Then relying on this parametric statistical model, the system hazard function is derived to depict the reliability-based imperfect preventive maintenance policy at system-level. Finally, to obtain the optimal solution(s) of the integrated model, we design an adaptive immune clone selection–culture algorithm, which is inspired from immune clone selection algorithm and culture algorithm. Results of the case study validate that our proposed maintenance policies and methodology have great advantages over the component-level or system-level maintenance policy and immune clone selection algorithm.

Periodic preventive maintenance planning for systems working under a Markovian operating condition

Systems commonly work under time-varying operating condition. Operating condition can influence the failure time of systems, which should be taken into account when do preventive maintenance (PM) planning. This study considers a one-component system working under time-varying operating condition, where the evolution process of the operating condition is governed by a Markov process. We assume that the failure time distribution of systems working under different operating condition can be modeled by an accelerated failure time model. A periodic PM policy is proposed to mitigate the increasing failure risk, where both imperfect maintenance (IM) and replacement are considered. The optimal combination of PM interval and number of IM actions before each replacement are determined by minimizing the long-term average cost, which is formulated based on the semi-regenerative properties of the system state. We also consider a special case where only two operating conditions exist. Numerical studies are conducted to validate the proposed maintenance policy.

The Proposed Maintenance Task for Plastic Injection Machine Using Reliability and Risk Centered Maintenance (RRCM) method in Manufacturing Industry

The objective of this research was to determine the optimal maintenance time interval for the selected critical components and the total cost of maintenance of a plastic injection machine. In determining the critical components, a risk matrix was used, and three components were selected, namely, hydraulic hose, barrel, and motor. Using the Reliability and Risk Centered Maintenance (RRCM) method, the researchers got a proposed maintenance policy and the total maintenance cost. Based on the result, it shows that there are seven proposed maintenance tasks with three scheduled oncondition tasks and four scheduled restoration tasks with an average maintenance interval of two months. The total maintenance cost proposed is IDR91.595.318. The cost is smaller compared to the actual maintenance costs of the company.

Intelligent sensor impact on predictive maintenance program costs

In this work, we develop a simulation study based on economic optimisation to compare the economical impact of two maintenance policies, traditional failure maintenance policy with predictive maintenance policy that utilises intelligent network sensor information. The simulation study established in this work compare tow maintenance strategies: predictive maintenance and failure-based maintenance, in order to compare when it is less expensive to maintain the equipment before it breaks down using intelligent network sensors than to replace it after its breakdown, to sum up, if it is profitable to implement this new technology. Also with this proposed approach, the decision maker could be in the position to decide on a most appropriate economical framework for the optimum cost, based on the comparison between breakdown cost and the cost of sensors. The method can be used by companies to make a decision when considering implementing remote monitoring. To illustrate the use and the advantages of the proposed maintenance policy, a numerical example is investigated.

Modeling and Simulation Approaches for Reliability Analysis of Drilling Machines

Drilling, as the initial stage of exploitation in open-pit mining, has a significant effect on subsequent mining stages. Any failure in a drilling machine stops the drilling operation and, as a result, all following operational phases come to a halt. The drilling system is the most important part of the drilling operation. This study selected two new rotary-type drilling machines (A and B) in Sarcheshmeh Copper Mine in Iran as a case study to perform a reliability and availability analysis of their drilling systems. The drilling system was divided into three independent subsystems; drill string, rotary head, and mast. The reliability modeling used a reliability block diagram and Monte Carlo simulation. Reliability and availability were evaluated, and the importance of each subsystem estimated. A preventive maintenance schedule was proposed using weighed reliability allocation to achieve the reliability target. The results show the mast is the most reliable subsystem. Importance measure analysis indicates the drill string and rotary head are the most critical subsystems of machines A and B, respectively. Finally, under the proposed maintenance policy, the reliability of the drilling systems of machines A and B is improved.

Stochastic Filtering Approach for Condition-Based Maintenance Considering Sensor Degradation

This paper proposes a condition-based maintenance (CBM) policy for a deteriorating system whose state is monitored by a degraded sensor. In the literature of CBM, it is commonly assumed that inspection of system state is perfect or subject to measurement error. The health condition of the sensor, which is dedicated to inspect the system state, is completely ignored during system operation. However, due to the varying operation environment and aging effect, the sensor itself will suffer a degradation process and its performance deteriorates with time. In the presence of sensor degradation, the Kalman filter is employed in this paper to progressively estimate the system and the sensor state. Since the estimation of system state is subject to uncertainty, maintenance solely based on the estimated state will lead to a suboptimal solution. Instead, predictive reliability is used as a criterion for maintenance decision-making, which is able to incorporate the effect of estimation uncertainty. Preventive replacement is implemented when the estimated system reliability at inspection hits a specific threshold, which is obtained by minimizing the long-run maintenance cost rate. An example of wastewater treatment plant is used to illustrate the effectiveness of the proposed maintenance policy. It can be concluded through our research that: 1) disregarding the sensor degradation while it exists will significantly increase the maintenance cost and 2) the negative impact of sensor degradation can be diminished via proper inspection and filtering methods. Note to Practitioners —This paper was motivated by the observation of sensor degradation in wastewater treatment plants but the developed approach also applies to other systems such as manufacturing systems, chemical plants, and pharmaceutical factories, where sensors are dedicated to a long-time operation in a harsh environment. This paper investigates the impact of sensor degradation on CBM and suggests that the effect of sensor degradation should be carefully addressed while making maintenance decisions. Otherwise, it will lead to a suboptimal maintenance decision and increase the operating cost. An optimal maintenance decision, which contains the optimal inspection interval and reliability threshold, is achieved via minimizing the long-run cost rate. In the presence of measurement noise and intrinsic uncertainty from degradation, a stochastic filtering approach is employed to estimate the system and sensor state. Based on the estimated states and the calculated reliability, a dynamic maintenance decision is obtained at each inspection. This paper can be further extended considering non-Gaussian noise and alternative degradation processes.

A two-phase preventive maintenance policy considering imperfect repair and postponed replacement

This paper investigates a novel two-phase preventive maintenance policy for a single-component system with an objective of maximizing the revenue generated by the performance-based contracting (PBC). The system undergoes a defective state before failure, and produces signal hinting the condition. The maintenance policy consists of two phases: imperfect maintenance phase followed by postponed replacement phase. In the imperfect maintenance phase, inspection is performed to reveal the defective state, leading to a possible repair. Both the inspection and the repair are imperfect. In the postponed replacement phase, preventive replacement is performed during the upcoming scheduled maintenance window, before which no inspection or repair is executed. The expected net revenue under PBC is maximized via the joint optimization of the inspection interval, number of inspection and preventive replacement interval. We apply the model to a case from a steel converter plant, and the results show that our proposed maintenance policy outperforms some existing maintenance policies in terms of the net revenue.

Post-warranty maintenance optimization for products with deterioration depending on aging and shock

ABSTRACTDue to the non-substitutability of the product, the monopoly manufacturer designs warranty to reduce maximally warranty cost. Although the monopoly manufacturer maintains responsibly product reliability in warranty, the consumer still confronts a problem about how to sustain product reliability after the designed warranty expires. Goal of this paper is, from the consumer’s perspective, to propose a reliability maintenance policy after expiry of the monopoly manufacturer’s warranty. First, a renewable repair-limit pro-rata replacement warranty (RRLPRRW) policy is considered from viewpoint of the monopoly manufacturer and used as a quality guarantee of the product with the deterioration affected by internal aging and external shock. Second, depreciation cost is earlier considered in the process of modeling life cycle cost, which differs from the traditional works on the consumer’s post-warranty reliability maintenance problem. Third, the consumer’s discounted cost rate model considering depreciation cost is derived to achieve optimal maintenance policy. Finally, a numerical example is provided to illustrate our approach. It was found that the proposed maintenance policy is more effective and the depreciation cost considered in life cycle cost has significant effect on extension of the post-warranty period.

A Maintenance Cost Study of Transformers Based on Markov Model Utilizing Frequency of Transition Approach

In this paper, a maintenance cost study of transformers based on the Markov Model (MM) utilizing the Health Index (HI) is presented. In total, 120 distribution transformers of oil type (33/11 kV and 30 MVA) are examined. The HI is computed based on condition assessment data. Based on the HI, the transformers are arranged according to its corresponding states, and the transition probabilities are determined based on frequency of a transition approach utilizing the transformer transition states for the year 2013/2014 and 2012/2013. The future states of transformers are determined based on the MM chain algorithm. Finally, the maintenance costs are estimated based on future-state distribution probabilities according to the proposed maintenance policy model. The study shows that the deterioration states of the transformer population for the year 2015 can be predicted by MM based on the transformer transition states for the year 2013/2014 and 2012/2013. Analysis on the relationship between the predicted and actual computed numbers of transformers reveals that all transformer states are still within the 95% prediction interval. There is a 90% probability that the transformer population will reach State 1 after 76 years and 69 years based on the transformer transition states for the year 2013/2014 and 2012/2013. Based on the probability-state distributions, it is found that the total maintenance cost increases gradually from Ringgit Malaysia (RM) 5.94 million to RM 39.09 million based on transformer transition states for the year 2013/2014 and RM 37.56 million for the year 2012/2013 within the 20 years prediction interval, respectively.

Proposed Maintenance Policy and Determining Sparepart Amount Using Reliability Centered Maintenance (RCM) and Reliability Centered Spares (RCS) for Eurosicma E 75 Machine

PT Konimex produce drug and food. Every year PT Konimex sets a target percentage to be achived in every machine. The achivement of this set goal is based on Technical Availability (TA). TA is the percentage of machines to be ready to use every month. This calculation is adjusted to the amount of downtime and leadtime of the machine. Eurosicma E75 DS (4)/A has the highest number of downtime from January 2017 until October 2017 and the machine only reaches the target once time. The research was conducted using Reliability Centered Maintenance (RCM) method to determine the maintenance method according to the characteristics of machine and Relibility Centered Spares (RCS) method to determine the number of sparepart requirement. Based on the result of data processing using RCM, obtained 11 Scheduled on Condtion Task, four Scheduled Restoration Task, two Scheduled Discard Task and six Run to Failure. The total cost of the proposed maintenance is Rp 237,063,659,829.68 while based on data processing using RCS method, the acquisition of sparepart preparation requirements for each component included in non repairable category.

Integration of maintenance systems

Excellence in maintenance is imperative in highly competitive market because it resulted into minimum maintenance cost, high equipment effectiveness, maximum reliability of the system, high quality of the products, low delivery time, high flexibility, safety etc. Any maintenance system such as Total Productive Maintenance (TPM) or Reliability Centered Maintenance (RCM) or Condition Based Maintenance (CBM) alone cannot achieve the excellence in maintenance but its integration may do. In this paper, an integration of TPM, RCM and CBM is proposed with a maintenance policy to take advantage of their respective strengths. A continuously monitored system subject to degradation due to the imperfect maintenance, where a hybrid hazard rate based on the concept of age reduction factor and hazard rate increase factor to predict the evolution of the system reliability in different maintenance cycles has been assumed.A quantitative decision making model for an integrated maintenance system is derived in order to assess the performance of the proposed maintenance policy. Numerical examples of calculation of optimal preventive maintenance age x and preventive maintenance number N* for the given cost ratio of corrective replacement and predictive preventive maintenance are given.

A Real-Time Maintenance Policy for Multi-Stage Manufacturing Systems Considering Imperfect Maintenance Effects

Machines are subject to deterioration due to usage and aging. Once one machine fails, maintenance action has to be imposed in order to resume its operation. In practice, maintenance actions are not always able to restore the machine as new. Multi-level maintenance is a more realistic scenario. It is important to select a proper maintenance level upon failure since it directly relates to the duration of a maintenance action and the deterioration status after maintenance and therefore determines the overall system performance. In this paper, a real-time maintenance policy is proposed to select an optimal maintenance level aiming at reducing maintenance-related costs. The maintenance cost includes resource cost and production loss due to machine stoppage. The maintenance cost rate, which is the cost per unit time after the maintenance, is used to select the optimal maintenance level. The maintenance effect is modeled with a virtual-age approach. With the available data collected by distributed sensors, a data-driven modeling of serial production lines is established for analyzing production dynamics. The opportunity window of machine stoppage in the stochastic scenario is derived, which is further used to estimate the production loss caused by the machine stoppage due to maintenance. A numerical experiment is conducted to validate the proposed maintenance policy.

Opportunistic Maintenance Policy for a Multi-Component System Subject to Random Failures

In this paper, an opportunistic maintenance policy (OMP) for a multi-component system is studied. The objective is to minimize the maintenance cost while guaranteeing a minimum level of reliability for the system and for each of its components. Each component is subject to random failures and at most one spare part of it should be kept in stock or ordering at any time. The lifetime of this system will be divided into several periods. At the beginning of each period, the set of actions (among many others) must be determined in order to achieve the objective mentioned above. The policy OMP is characterized by two parameters; the first one is the scheduled time for spare ordering and the second one is the period of realization of the maintenance action (if any). These parameters will be derived from the joint optimization of maintenance cost and the inventory cost for each component. Finally, a numerical example to explain the proposed maintenance policy and the optimization procedure is provided.

A preventive maintenance of circular consecutive-k-out-of-n: F systems

PurposeThe purpose of this paper is to develop a preventive maintenance policy with continuous monitoring for a circular consecutive-k-out-of-n: F systems. A preventive maintenance policy is developed based on the system critical condition which is related to the number of working components in the minimal cut sets of the system. If there is at least one minimal cut set which consists of only one working component, the system is maintained preventively (PM) after a certain time interval and the failed components are replaced with the new ones to prevent the system failures. If the system fails prior to the preventive maintenance, the system is correctively maintained (CM) immediately by replacing the failed components.Design/methodology/approachThe mathematical function of the expected cost rate for the proposed maintenance policy is derived. The costs of PM, CM, and replacement per component are considered. The optimal maintenance parameter, which is the PM interval, is obtained by enumeration, and the numerical studies are shown with various system and cost parameters. The performance of the proposed policy is evaluated by comparing its expected cost rate to those of the no-PM and age-PM policies. The percentage of cost increase from the no-PM and age-PM policies to the proposed PM policy is calculated and this value can represents how important the continuous monitoring in this policy.FindingsThe proposed policy outperforms other policies. When the cost of CM is high and the cost of PM is low, the proposed PM policy is more suitable.Research limitations/implicationsThe system consists of identical components and the component failure times follow an exponential distribution. Continuous monitoring is considered, which means that the component states can be known at any time. Three cost parameters, cost of PM, CM, and replacement per component, are considered.Originality/valueThis paper shows a maintenance problem for circular consecutive-k-out-of-n: F systems. Many studies on this system type focused on the reliability estimation or system design problem. Previous study with this policy (Endharta and Yun, 2015) has been developed for linear systems, although the study used a simulation approach to estimate the expected cost rate. Also, Endharta et al. (2016) considered a similar method for the different types of system, which is linear consecutive-k-out-of-n: F system.

Jointly Production and Correlated Maintenance Optimization for Parallel Leased Machines

This paper deals with a preventive maintenance strategy optimization correlated to production for a manufacturing system made by several parallel machines under lease contract. In order to minimize the total cost of production and maintenance by reducing the production system interruptions due to maintenance activities, a correlated group preventive maintenance policy is developed using the gravity center approach (GCA). The aim of this study is to determine an economical production plan and an optimal group preventive maintenance interval Tn at which all machines are maintained simultaneously. An analytical correlation between failure rate of machines and production level is considered and the impact of the preventive maintenance policy on the production plan is studied. Finally, the proposed maintenance policy GPM is compared with an individual simple strategy approach IPM in order to illustrate its efficiency.

Optimal CBM policy with two sampling intervals

PurposeThe purpose of this paper is to propose a novel condition-based maintenance (CBM) policy with two sampling intervals for a system subject to stochastic deterioration described by the Cox’s proportional hazards model (PHM).Design/methodology/approachIn this paper, the new or renewed system is monitored using a longer sampling interval. When the estimated hazard function of the system exceeds a warning limit, the observations are taken more frequently, i.e., the sampling interval changes to a shorter one. Preventive maintenance is performed when either the hazard function exceeds a maintenance threshold or the system age exceeds a pre-determined age. A more expensive corrective maintenance is performed upon system failure. The proposed model is formulated in the semi-Markov decision process (SMDP) framework.FindingsThe optimal maintenance policy is found and a computational algorithm based on policy iteration for SMDP is developed to obtain the control thresholds as well as the sampling intervals minimizing the long-run expected average cost per unit time.Research limitations/implicationsA numerical example is presented to illustrate the whole procedure. The newly proposed maintenance policy with two sampling intervals outperforms previously developed maintenance policies using PHM. The paper compares the proposed model with a single sampling interval CBM model and well-known age-based model. Formulas for the conditional reliability function and the mean residual life are also derived for the proposed model. Sensitivity analysis has been performed to study the effect of the changes in the Weibull parameters on the average cost.Practical implicationsThe results show that considerable cost savings can be obtained by implementing the maintenance policy developed in this paper.Originality/valueUnlike the previous CBM policies widely discussed in the literature which use sequential or periodic monitoring, the authors propose a new sampling strategy based on two sampling intervals. From the economic point of view, when the sampling is costly, it is advantageous to monitor the system less frequently when it is in a healthy state and more frequently when it deteriorates and enters the unhealthy state.