Showing papers on "Preventive maintenance published in 2009"
Book•
01 Jan 2009
TL;DR: In this article, the authors present a historical review of field performance of embedded steel corrosion and other determinization processes in concrete, concrete, and concrete construction, including concrete quality condition assessment and preventive maintenance.
Abstract: Historical Review Field Performance Corrosion of Embedded Steel Other Deteriorating Processes Codes and Practices Probability of Steel Corrosion Additional Protective Strategies and Measures Concrete Quality Control Achieved Construction Quality Condition Assessment and Preventive Maintenance Practical Applications Life Cycle Costs Life Cycle Assessment Recommended Job Specifications
190 citations
TL;DR: The stochastic gamma process model of deterioration proposed in this article is more versatile than the random rate model commonly used in structural reliability literature and highlights their profound implications on age-based and condition-based preventive maintenance policies.
Abstract: In the life-cycle management of infrastructure systems, the decisions regarding the time and frequency of inspection, maintenance and replacement are confounded by sampling and temporal uncertainties associated with deterioration of the structural resistance. To account for these uncertainties, probabilistic models of deterioration have been developed under two broad categories, namely the random variable model and the stochastic process model. This paper presents a conceptual exposition of these two models and highlights their profound implications on age-based and condition-based preventive maintenance policies. The stochastic gamma process model of deterioration proposed here is more versatile than the random rate model commonly used in structural reliability literature.
163 citations
TL;DR: A preventive maintenance plan approach is proposed for a multi-component series system subjected to random failures, where the cost rate is minimized under general lifetime distribution.
151 citations
TL;DR: The proposed tool acquires the forecasted failure probability of individual components from the prognostics module, and their reliability expectations after maintenance, and the risk is minimized utilizing genetic algorithms for the whole system rather than individual components.
Abstract: Condition based maintenance (CBM) aims to balance two extreme sides (i.e., corrective maintenance (CM), and preventive maintenance (PM)) by observing and forecasting the real time health of machines. Recent developments in CBM revealed promising technologies for advanced fault detection, and forecasting. Traditional maintenance scheduling in CBM is based on the threshold setting on forecasted failure probability, or remaining useful life (RUL) for individual components. However, this approach may not give the best result for the system, because individual components are inter-related, and mutually dependent. It is not uncommon in systems that turning off a machine due to failure or maintenance causes other machinery or components to be turned off. Designing a comprehensive tool that optimizes availability & cost of the whole system incorporating prognostics information is crucial to fully benefit from CBM. The goal of this paper is to emphasize this need by demonstrating scenarios in CM, PM, and CBM; and to present a solution that optimizes system availability, and cost with system-maintenance constraints using genetic algorithms. The proposed tool acquires the forecasted failure probability of individual components from the prognostics module, and their reliability expectations after maintenance. The tradeoff between maintenance & failure is quantified in risk as the objective function to be minimized. The risk is minimized utilizing genetic algorithms for the whole system rather than individual components. The results of the proposed tool are compared with PM, CM, and CBM in which prognostics information of components are analyzed individually.
147 citations
TL;DR: To optimize the maintenance policy of the system, an approach combining statistical process control (SPC) and condition-based maintenance (CBM) is proposed, and a mathematical model for the maintained system cost is derived.
137 citations
TL;DR: This paper deals with the joint production and maintenance scheduling problem according to a new bi-objective approach that allows the decision maker to find compromise solutions between the production objectives and maintenance ones.
Abstract: This paper deals with the joint production and maintenance scheduling problem according to a new bi-objective approach. This method allows the decision maker to find compromise solutions between the production objectives and maintenance ones. Reliability models are used to take into account the maintenance aspect of the problem. The aim is to simultaneously optimize two criteria: the minimization of the makespan for the production part and the minimization of the system unavailability for the maintenance side. Two decisions are taken at the same time: finding the best assignment of n jobs to m machines in order to minimize the makespan and deciding when to carry out the preventive maintenance actions in order to minimize the system unavailability. The maintenance actions numbers as well as the maintenance intervals are not fixed in advance. Two evolutionary genetic algorithms are compared to find an approximation of the Pareto-optimal front in the parallel machine case. On a large number of test instances (more than 5000), the obtained results are promising.
136 citations
04 Dec 2009
TL;DR: The thermal diagnosis system proposed by this paper can be used at the various power facilities to improve inspection efficiency as illustrated in the experiment results.
Abstract: An automatic diagnosis system is proposed by this paper for a more and more important issue, preventive maintenance. Every year, various workplace accidents happen due to undesirable maintenance. No matter how stringent the rules governing the maintenance of electrical equipment may be, it is always a challenge for the power industry due to the large number of electrical equipment and the shortage of manpower. In this paper, an automatic diagnosis system for testing electrical equipment for defects is proposed. Based on nondestructive inspection, infrared thermography is used to automate the diagnosis process. Thermal image processing based on statistical methods and morphological image processing technique are used to identify hotspots and the reference temperature. Qualitative and quantitative analyses are carried out on the gathered information and inspection results are presented after being processed by the diagnosis. The thermal diagnosis system proposed by this paper can be used at the various power facilities to improve inspection efficiency as illustrated in the experiment results.
122 citations
TL;DR: An opportunistic PM scheduling algorithm for the multi-unit series system based on dynamic programming with the integration of the imperfect effect into maintenance actions is proposed.
115 citations
09 Oct 2009
TL;DR: In this paper, an opportunistic maintenance optimization model for offshore wind power system is presented, which takes advantage of wind forecasts and corrective maintenance activities in order to perform preventive maintenance tasks at low costs.
Abstract: A sound maintenance planning is of crucial importance for wind power farms, and especially for offshore locations. There is a large potential in cost savings by maintenance optimization to make the projects more cost-efficient. This paper presents an opportunistic maintenance optimization model for offshore wind power system. The model takes advantage of wind forecasts and corrective maintenance activities in order to perform preventive maintenance tasks at low costs. The approach is illustrated with an example to demonstrate the value of the optimization. In this example 43% of the cost to perform preventive maintenance could be saved using the proposed method.
112 citations
TL;DR: This study integrates maintenance and production programs with the economic production quantity (EPQ) model for an imperfect process involving a deteriorating production system with increasing hazard rate: imperfect repair and rework upon failure (out of control state).
102 citations
TL;DR: An adjusted marginal analysis is developed and it is shown that it performs considerably better in numerical experiments than the straightforward extension of the METRIC method for spare part inventory optimisation.
TL;DR: An efficient algorithm is developed to derive the optimal PM policy and a closed-form solution is obtained for the case where the lifetime distribution of the equipment is Weibull and the expected total cost is compared with the performance of other policies under various maintenance schemes through numerical examples.
TL;DR: A maintenance planning approach, called value-driven maintenance planning (VDMP), is presented, to emphasise the fact that the objectives of the plant are the reference points for specifying functional requirements for the equipment locations and equipment.
TL;DR: In this paper, the authors proposed two techniques that are easy to understand and code, yet simplistically adaptable to any other machine-scheduling problems, such as job shop scheduling with sequencedependent setup times and preventive maintenance policies.
Abstract: Although many researchers have proposed different techniques to integrate production scheduling and preventive maintenance, these techniques have some drawbacks. For example, some of them are so intricate that one cannot easily implement them, or some strongly exploit specific features of the original studied problem that one cannot apply them to other problems. We hereby propose two techniques that are easy to understand and code, yet simplistically adaptable to any other machine-scheduling problems. This paper investigates job shop scheduling with sequence-dependent setup times and preventive maintenance policies. The optimization criterion is to minimize makespan. Four metaheuristics based on simulated annealing and genetic algorithms as well as adaptations of two metaheuristics in the literature are employed to solve the problem. The performances of the proposed algorithms are evaluated by comparing their solutions through two benchmarks based on Taillard’s instances.
TL;DR: The paper develops integrated production, inventory and maintenance models for a deteriorating production system in which the production facility may not only shift from an 'in- control' state to an 'out-of-control' state but also may break down at any random point in time during a production run.
Abstract: The paper develops integrated production, inventory and maintenance models for a deteriorating production system in which the production facility may not only shift from an ‘in-control’ state to an ‘out-of-control’ state but also may break down at any random point in time during a production run. In case of machine breakdown, production of the interrupted lot is aborted and a new production lot is started when the on-hand inventory is depleted after corrective repair. The process is inspected during each production run to examine the state of the production process. If it is found in the ‘in-control’ state then either (a) no action is taken except at the time of last inspection where preventive maintenance is done (inspection policy-I) or (b) preventive maintenance is performed (inspection policy-II). If, however, the process is found to be in the ‘out-of-control’ state at any inspection then restoration is done. The proposed models are formulated under general shift, breakdown and repair time distributions. As it is, in general, difficult to find the optimal production policy under inspection policy-I, a suboptimal production policy is derived. Numerical examples are taken to determine numerically the optimal/suboptimal production policies of the proposed models, to examine the sensitivity of important model parameters and to compare the performance of inspection and no inspection policies.
TL;DR: In this article, an analytical, option-based cost model for scheduling joint production and preventive maintenance when demand is uncertain is presented, which can balance the tradeoff between reduced risk from uncertainty that options afford and the increased price premium paid to invest preventive maintenance resources.
TL;DR: This paper expound a new method to integrate the effect of CM while planning for the PM policy and uses the proportional hazard function as a modeling tool for that purpose.
TL;DR: The imperfect PM model proposed in this study incorporates improvement factors vis-a-vis the hazard-rate function, and effective age as well as determining the optimal PM & replacement schedule that minimize the expected cost per unit of time, over an infinite horizon.
Abstract: This paper presents a generalized periodic imperfect preventive maintenance (PM) model for a system with age-dependent failure type. The imperfect PM model proposed in this study incorporates improvement factors vis-a-vis the hazard-rate function, and effective age. As failures occur, the system experiences one of the two types of failure: type-I failure (minor), and type-II failure (catastrophic). Type-I failures are rectified with minimal repair. In a PM period, the system is preventively maintained following the occurrence of a type-II failure, or at age T, whichever takes place first. At the N th PM, the system is replaced. An approach that generalizes the existing studies on the periodic PM policy is proposed. Taking age-dependent failure type into consideration, the objective consists of determining the optimal PM & replacement schedule that minimize the expected cost per unit of time, over an infinite horizon.
TL;DR: The results indicate that these policies rarely differ, and that when they do, the difference in long-run mission reliability is minimal, which suggests that future work should concentrate on extending results for the single-mission problem.
Abstract: ?Selective maintenance? models determine the optimal subset of desirable maintenance actions to perform when maintenance resources are constrained. We analyse a corrective selective maintenance model that identifies which components to replace in the finitely long periods of time between missions performed by a series-parallel system. We formulate this multi-mission problem as a stochastic dynamic program, and compare the resulting optimal infinite-horizon policy to both the optimal single-mission, and two-mission policies by executing a large numerical experiment. Our results indicate that these policies rarely differ, and that when they do, the difference in long-run mission reliability is minimal, which suggests that future work should concentrate on extending results for the single-mission problem.
TL;DR: A model for the economic design of a variable-parameter Shewhart control chart used to monitor the mean in a process, where, apart from quality shifts, failures may also occur, allows the determination of the scheme parameters that minimize the total expected quality and maintenance cost of the procedure.
TL;DR: Optimal preventive maintenance schedules are obtained for non-decreasing failure rates and numerical examples for power law models are given.
TL;DR: It is demonstrated that a well-designed payment scheme can induce the contractor to adopt the maintenance schedule that maximizes the manufacturer’s profit.
TL;DR: A numerical investigation into the sensitivity of optimum schedules to different aspects of an age-reduction model is described, including the situation when parts of a system are non-maintainable—i.e., unaffected by PM.
01 Jan 2009
TL;DR: Ahuja et al. as discussed by the authors developed and implemented strategic Total Productive Maintenance (TPM) programs to improve the performance of maintenance activities in manufacturing organizations, which are referred to as components of World Class Manufacturing.
Abstract: Manufacturing organizations worldwide are facing many challenges to achieve successful operation in today’s competitive environment. Modern manufacturing requires that, to be successful, organizations must be supported by both effective and efficient maintenance practices and procedures. The global marketplace has necessitated many organizations to implement proactive lean manufacturing programs and organizational structures to enhance their competitiveness (Bonavia and Marin, 2006). Over the past two decades, manufacturing organizations have used different approaches to improve maintenance effectiveness. One approach to improving the performance of maintenance activities is to develop and implement strategic TPM programs (Ahuja and Khamba, 2007). Among various manufacturing programs, Total Quality Management (TQM), Just-in-Time (JIT), Total Productive Maintenance (TPM) and Total Employee Involvement (TEI) programs have often been referred to as components of “World Class Manufacturing” (Cua et al. 2001).
TL;DR: In this article, the use of state diagrams in maintenance modeling is discussed, and it is shown that some properties make the model inconsistent with reality especially in cases where a maintenance policy with nonperiodic inspections is modeled.
Abstract: This paper discusses the use of state diagrams in maintenance modeling. These diagrams frequently illustrate deterioration, inspections and maintenance. Mathematically, the state diagram can be represented by a Markov process. The paper discusses the properties of such a Markov process. They are compared with the maintenance situation in the real world. It is shown that some properties make the model inconsistent with reality especially in cases where a maintenance policy with nonperiodic inspections is modeled. A numerical example is provided that shows that these model properties result in modeling errors. The presented results make it clear that the common practice of using Markov processes based on state diagrams must be judged critically when they are used for modeling certain maintenance strategies.
TL;DR: The proposed approach provides manufacturers with guidelines on how to offer customers two-dimensional warranty programs with proper time and usage limits and investigates the robustness of the derived optimal warranty policy.
Abstract: In dealing with the effects of product deterioration in the context of reliability analysis, it may not be satisfactory to consider only the effects of time or age because usage is often another essential factor that accounts for deterioration. A two-dimensional warranty with consideration of both time and usage for deteriorating products would be more advantageous for manufacturers. In this paper, a two-dimensional warranty model in which the customer is expected to perform appropriate preventive maintenance is analyzed and the warranty policy that maximizes the manufacturers' profits is determined. The proposed approach provides manufacturers with guidelines on how to offer customers two-dimensional warranty programs with proper time and usage limits. A numerical example shows the effectiveness of the proposed approach. Sensitivity analyses are conducted to investigate the robustness of the derived optimal warranty policy.
TL;DR: A backward allocation algorithm is proposed and applied to scheduling the preventive maintenance of an engine module installed in T-59 advanced jet trainers in the Republic of Korea Air Force.
Metz1
TL;DR: In this paper, a joint quality control and preventive maintenance policy for a randomly failing production system producing conforming and non-conforming units is developed and combined with simulation in order to determine simultaneously the optimal rate, and the optimal size h* which minimize the expected total cost per time unit including the average costs related to maintenance, quality and inventory.
Abstract: In this paper, we develop a joint quality control and preventive maintenance policy for a randomly failing production system producing conforming and non-conforming units. The considered system consists of one machine designed to fulfil a constant demand. According to the proportion l of non-conforming units observed on each lot and compared to a threshold value lm, one decides to undertake or not maintenance actions on the system. In order to palliate perturbations caused by the stopping of the machine to undergo preventive maintenance or an overhaul, a buffer stock h is built up to ensure the continuous supply of the subsequent production line. A mathematical model is developed and combined with simulation in order to determine simultaneously the optimal rate, and the optimal size h* which minimize the expected total cost per time unit including the average costs related to maintenance, quality and inventory.
TL;DR: In this paper, the mean time to system failure (MTSF), steady state availability and the profit function of a two-unit cold standby repairable redundant system involving common-cause failures and preventive maintenance were discussed.
Abstract: Problem statement: Many authors have studied the cost analysis of a two-unit cold standby redundant system with two types of unit failure, but no attention was paid to the reliability of a system involving common-cause failures and preventive maintenance. Question was raised whether the preventive maintenance would be effective on the reliability and performance of the system. Determine the efficacy of preventive maintenance on the reliability and performance of the system. Approach: In this study, the Mean Time to System Failure (MTSF), steady state availability and the profit function of a two-unit cold standby repairable redundant system involving common-cause failures and preventive maintenance were discussed. We analyzed the system by using Kolmogorov's forward equations method. Some particular cases have also been discussed graphically. Results: The results indicated that the system with preventive maintenance is better than the system without preventive maintenance. Conclusion: These results indicated that the better maintenance of parts of the system originated better reliability and performance of the system.
TL;DR: In this article, the optimal time for preventive maintenance and spare part provision by a stochastic optimization algorithm based on a load-dependent reliability model is calculated for different machine components.