Optimal Maintenance Time Research Articles

Condition based opportunistic preventive maintenance policy for utility systems with both economic and structural dependencies [formula omitted] application to a gas supply network

The main challenge in the maintenance planing lies in the realistic modeling of the maintenance policy. However, the complexity of the industrial systems makes difficult the achievement of this prospect. To face the realistic constraints, the present paper provides an approach for the maintenance planning of utility systems on the basis of opportunistic maintenance policy and Bayesian theory. The reliability assessment, which can be updated according to the evolution of the environmental conditions using the Bayesian theory, is integrated in the maintenance model, in order to evaluate the optimal preventive maintenance times and to update them when new degradation data become available. The opportunistic replacement is used to take benefit from the non-operating state of some components, caused by the maintenance action on other components, in order to replace them opportunistically. The effectiveness of the proposed approach is illustrated through a numerical application on operating gas supply system.

OPTIMIZATION OF TIME AND COST OF BUILDING COMPONENT MAINTENANCE: A CASE-STUDY IN A FIVE STAR HOTEL

Building component maintenance management is one of the most important issues in management of building lifecycle costs. Key to successful maintenance management is the ability to survey present condition and predict the future condition for each component and facilities present in a building. The paper presents a process to optimize maintenance cost and time of caste iron pipe component in wastewater plumbing system of hotel building in Iran as a case study using the condition index (CI) method. The research uses a systematic assessment field as followed by the USACERL condition index method. To achieve this objective, cast iron pipe within wastewater plumbing system is surveyed using the economical tools for implementation of optimal maintenance time and cost based on highest saving to investment ratio. The research presents a financial analysis using the existing data. The data was collected through financial and technical sheet. Data is analyzed using the economical tools and financial analysis is simulated with simulation software. The purpose of this paper is to develop a financial analysis simulation for component maintenance assessment during its service life based on highest interest rate. The analysis results in to identify optimum maintenance time basing on the limited cost. This method helps to facilities managers and engineers to make better decisions for reducing maintenance costs and increasing the component’s service life.

Opportunistic Maintenance Model for Wind Turbine Based on Reliability Constraint

Preventive maintenance of wind turbine can effectively improve the reliability of wind turbine. This paper firstly set single component reliability of wind turbine as constraint condition, single component minimum unit time maintenance cost as objective function to obtain optimal maintenance cycle and maintenance times. On this basis, repairing component which meet certain conditions, then build opportunistic maintenance model of wind turbine. Lastly, set the minimum total cost under opportunistic maintenance as objective function, the wind turbine availability as constraint condition, then solve this model to obtain optimal threshold of opportunistic maintenance and minimum total maintenance cost using genetic algorithm. The example analysis shows that the model can effectively save the total maintenance cost. This study has some reference significance for the maintenance department to make the maintenance plan.

Increase KRL Utilization by Reducing Final Maintenance Time at Balai Yasa KRL Depok

The increase of population in Indonesia especially in Jabodetabe from year to year has increased significantly, as well as the number of private vehicles which from year to year has increased. By not compensating for the increase in road length, then there will be congestion in the next few years. PT. KCJ is here to deal with the problem. By using rail mode transportation, it will reduce the number of vehicles operating on the highway, thereby reducing congestion. To meet the number of travel needs and offset the increase in population in Greater Jakarta, the availability of facilities at PT. KCJ must comply with applicable provisions. By optimizing the time of maintenance of the facilities, it will obtain the availability of adequate facilities. In this analysis, we use network planning method to determine the optimal maintenance time of suggestion. In optimizing the maintenance time of the facilities there are several things that influence, among others: the needs of employees, the competence of personnel maintenance personnel, and reduction of existing backlog. At the end of the research will be known the most optimal time to perform the final treatment, so the availability of facilities will be fulfilled with due regard to the reliability of the means.

Maintenance Policy in Public-Transport Involving Government Subsidy

A public transport with government subsidy is considered to encourage the sustainability of the transportation. The transportations revenue is determined by the maximum of the uptimes of the vehicle. In this paper, we study a one-dimensional maintenance policy for new vehicle which is characterized by age parameter. We consider that the degradation of the vehicle is affected by the age of the vehicle, and modelled by using a one-dimensional approach. The owner performs both preventive and corrective maintenance actions, and the preventive maintenance action will reduce the vehicle failure rate and hence it will decrease the corrective maintenance cost during the life time of the vehicle. The decision problem for the owner is to find the optimal preventive maintenance time of the vehicle of each subsidy option offered by maximizing the expected profit for each subsidy.

Combined Redundancy Allocation and Maintenance Planning Using a Two-Stage Stochastic Programming Model for Multiple Component Systems

A new modeling approach is presented to optimally and simultaneously design the configuration of a multicomponent system and determine a maintenance plan with uncertain future stress exposure. Traditionally, analytical models for system design and maintenance planning are applied sequentially, but this new model provides an integrated approach to make decisions considering the lifecycle cost of the system. Specifically considering the influence of uncertain future usage stresses on component and system reliability, the integrated redundancy allocation and maintenance planning problem is formulated as a two-stage stochastic programming model with recourse. In this model, the system is exposed to uncertain usage scenarios with their associated probabilities of occurrence or likelihood. The decision variables for the first stage are the selection of component types and the number of components to be used in the system, and these variables are modeled before the uncertainty is revealed. The second-stage variables, involving a recourse function, are the preventive maintenance plan, which defines optimal maintenance times for planned replacement of components under distinct usage scenarios. Numerical examples and sensitivity analysis on series–parallel systems demonstrate applications of the proposed model and provide further insights. The comparisons of the proposed integrated approach to traditional sequential method show advantages of the proposed model in cost saving.

Risk-Based Maintenance Planning for Deteriorating Pressure Vessels With Multiple Defects

Pressure vessels are subject to deterioration processes, such as corrosion and fatigue, which can lead to failure. Inspections and repairs are performed to mitigate this risk. Large industrial facilities (e.g., oil and gas refineries) often have regularly scheduled shutdown periods during which many components, including the pressure vessels, are disassembled, inspected, and repaired if necessary. This paper presents a decision analysis framework for the risk-based maintenance (RBM) planning of corroding pressure vessels. After a vessel has been inspected, this framework determines the optimal maintenance time of each defect, where the optimal time is the one that minimizes the total expected cost over the lifecycle of the vessel. The framework allows for multiple defects and two failure modes (leak and burst), and accounts for the dependent failure events. A stochastic gamma process is used to model the future deterioration growth to determine the probability of vessel failure. The novel growth model presents a simple method to predict both the depth and length of each corrosion defect to enable burst analysis. The decision analysis framework can aid decision makers in deciding when a repair or replacement should be performed. This method can be used to immediately inform the decision maker of the optimal decision postinspection. A numerical example of a corroding pressure vessel illustrates the method.

Maintenance optimization for power distribution systems subjected to hurricane hazard, timber decay and climate change

Electric power systems are vulnerable to extensive damage due to hurricanes with most of the damage concentrated on overhead distribution systems. There is evidence that climate change will affect future hurricane patterns. Additionally, wood poles, which are most commonly used in distribution systems, are susceptible to decay. The scarcity of resources and increasing demand for higher reliability warrant the use of optimization techniques for wood pole maintenance planning. This paper presents a framework for optimal maintenance of wood poles subjected to non-stationary hurricane hazard and decay. Maintenance cost, service life, and system performance are considered separately and simultaneously in the optimization. Periodic chemical treatment and repair of decayed poles using fiber-reinforced polymer are considered. The distribution system of a virtual city assumed to be in Florida is used to demonstrate the framework. The results of the single-objective optimization indicate that the objective that maximizes service life resulted in higher optimal maintenance time. However, delaying maintenance will lead to a larger probability of pole failure, higher corrective maintenance cost, and lower system performance. The result of the multi-objective optimization is closer to the result of the cost-based optimization because the cost function is more sensitive to the variation of maintenance time.

Optimum Maintenance Strategy of a Repairable System Under Long-Term Free Preventive Maintenance Warranty with Predicted Maintenance

In this paper, the optimum user’s maintenance strategy of a repairable system for free preventive maintenance (PM) warranty policy is proposed. This study considers predicted maintenance due to the system failure is likely occurring and requires repair during periodic maintenance time. Periodic maintenance can be classified as one of three types — imperfect PM, perfect PM and predicted maintenance. The probability that periodic maintenance is perfect PM or predicted maintenance depends on the number of imperfect maintenance operations conducted since the previous renewal cycle. The sellers offer free perfect and imperfect PM warranty. An optimal periodic maintenance time is determined by minimizing the total cost. Some special cases, implemented with machine learning and human learning, are given to demonstrate the feasibility of the proposed strategy. A numerical example is given.

Investigation on physical and chemical parameters to predict long-term aging of asphalt binder

The objective of this paper is to investigate and determine the critical physical and chemical parameters corresponded with long-term aging of asphalt binder in both laboratory and field. Firstly, the basic properties, rheological properties, component analysis and functional group analysis were conducted to determine the critical physical and chemical parameters of long-term PAV aged binder in laboratory. Secondly, the asphalt binders in field with different service life are extracted and comparatively analysed with laboratory aged binder. Lastly, the critical physical and chemical parameters of aged asphalt were determined and discussed. The results indicate that viscosity ratio can be used as indicator to predict the aging of asphalt binder in both laboratory and field. Furthermore, the carbonyl area index can be used as a chemical indicator to effectively reflect the aging level of asphalt binder. The results of this paper will help to gain a better understanding of the long-term aging behavior of asphalt binder both in laboratory and in field pavement and provide methods to determine the optimal preventive maintenance time for aged asphalt pavement in service.

Geometric Process-Based Maintenance and Optimization Strategy for the Energy Storage Batteries

Renewable energy is critical for improving energy structure and reducing environment pollution. But its strong fluctuation and randomness have a serious effect on the stability of the microgrid without the coordination of the energy storage batteries. The main factors that influence the development of the energy storage system are the lack of valid operation and maintenance management as well as the cost control. By analyzing the typical characteristics of the energy storage batteries in their life cycle, the geometric process-based model including the deteriorating system and the improving system is firstly built for describing the operation process, the preventive maintenance process, and the corrective maintenance process. In addition, this paper proposes an optimized management strategy, which aims to minimize the long-run average cost of the energy storage batteries by defining the time interval of the detection and preventive maintenance process as well as the optimal corrective maintenance times, subjected to the state of health and the reliability conditions. The simulation is taken under the built model by applying the proposed energy storage batteries’ optimized management strategy, which verifies the effectiveness and applicability of the management strategy, denoting its obvious practicality on the current application.

An Optimization Model for Condition-Based Maintenance Decision Considering Total Risk of Grid Operation

In the background of condition-based maintenance (CBM), the equipment maintenance levels in a certain state have different choices, which has different impacts on the improvement of the equipment state and maintenance costs, which in turn affects the time schedule of equipment maintenance. Therefore, in this paper the equipment maintenance time and maintenance level are viewed as decision variables to make maintenance schedules. This paper presents a risk-based optimization model for system maintenance scheduling problem, considering the state of equipment, correlations of the equipment and the operation of grid. An optimization model minimizing total risk of grid operation by balancing system maintenance risk and failure risk is established, so that both optimal maintenance level and optimal maintenance time could be determined simultaneously. An example is taken to verify the validity of the model.

An optimal age-based group maintenance policy for multi-unit degrading systems

In order to share maintenance set-up costs and reduce system breakdown, the group maintenance policies are widely used for complex multi-unit systems. In this paper, an optimal age-based group maintenance policy is proposed for a multi-unit series system whose components are subject to different gradual degradation phenomena. When the degradation level of a component reaches a given critical size, it is replaced by a new one and the other components undergo a preventive maintenance (PM) action; otherwise, a planned group PM is performed for the whole system at operational age T>0. The problem is to determine an optimal group maintenance time T⁎ such that the system’s average long-run maintenance cost per unit time is minimized. The explicit expression of the objective function is derived and sufficient conditions for existence and uniqueness of the optimal solution are obtained. Finally, the proposed maintenance policy is applied to a group of wind turbine bearings and the results are compared with the case without planned maintenance (i.e., reactive response) and with an individual age-based maintenance policy.

Performance deterioration modeling and optimal preventive maintenance strategy under scheduled servicing subject to mission time

Servicing is applied periodically in practice with the aim of restoring the system state and prolonging the lifetime. It is generally seen as an imperfect maintenance action which has a chief influence on the maintenance strategy. In order to model the maintenance effect of servicing, this study analyzes the deterioration characteristics of system under scheduled servicing. And then the deterioration model is established from the failure mechanism by compound Poisson process. On the basis of the system damage value and failure mechanism, the failure rate refresh factor is proposed to describe the maintenance effect of servicing. A maintenance strategy is developed which combines the benefits of scheduled servicing and preventive maintenance. Then the optimization model is given to determine the optimal servicing period and preventive maintenance time, with an objective to minimize the system expected life-cycle cost per unit time and a constraint on system survival probability for the duration of mission time. Subject to mission time, it can control the ability of accomplishing the mission at any time so as to ensure the high dependability. An example of water pump rotor relating to scheduled servicing is introduced to illustrate the failure rate refresh factor and the proposed maintenance strategy. Compared with traditional methods, the numerical results show that the failure rate refresh factor can describe the maintenance effect of servicing more intuitively and objectively. It also demonstrates that this maintenance strategy can prolong the lifetime, reduce the total lifetime maintenance cost and guarantee the dependability of system.

Data-Mining – A Valuable Managerial Tool for Improving Power Plants Efficiency

Abstract Energy and environment are top priorities for the EU’s Europe 2020 Strategy. Both fields imply complex approaches and consistent investment. The paper presents an alternative to large investments to improve the efficiencies of existing (outdated) power installations: namely the use of data-mining techniques for analysing existing operational data. Data-mining is based upon exhaustive analysis of operational records, inferring high-value information by simply processing records with advanced mathematical / statistical tools. Results can be: assessment of the consistency of measurements, identification of new hardware needed for improving the quality of data, deducing the most efficient level for operation (internal benchmarking), correlation of consumptions with power/ heat production, of technical parameters with environmental impact, scheduling the optimal maintenance time, fuel stock optimization, simulating scenarios for equipment operation, anticipating periods of maximal stress of equipment, identification of medium and long term trends, planning and decision support for new investment, etc. The paper presents a data mining process carried out at the TERMICA - Suceava power plant. The analysis calls for a multidisciplinary approach, a complex team (experts in power&heat production, mechanics, environmental protection, economists, and last but not least IT experts) and can be carried out with lower expenses than an investment in new equipment. Involvement of top management of the company is essential, being the driving force and motivation source for the data-mining team. The approach presented is self learning as once established, the data-mining analytical, modelling and simulation procedures and associated parameter databases can adjust themselves by absorbing and processing new relevant information and can be used on a long term basis for monitoring the performance of the installation, certifying the soundness of managerial measures taken and suggesting further adjustments

Risk-Based Predictive Maintenance for Safety-Critical Systems by Using Probabilistic Inference

Risk-based maintenance (RBM) aims to improve maintenance planning and decision making by reducing the probability and consequences of failure of equipment. A new predictive maintenance strategy that integrates dynamic evolution model and risk assessment is proposed which can be used to calculate the optimal maintenance time with minimal cost and safety constraints. The dynamic evolution model provides qualified risks by using probabilistic inference with bucket elimination and gives the prospective degradation trend of a complex system. Based on the degradation trend, an optimal maintenance time can be determined by minimizing the expected maintenance cost per time unit. The effectiveness of the proposed method is validated and demonstrated by a collision accident of high-speed trains with obstacles in the presence of safety and cost constrains.

Opportunistic predictive maintenance for complex multi-component systems based on DBN-HAZOP model

Predictive maintenance (PdM) focuses on failure prediction in order to prevent failure in advance and offer sufficient information to improve inherent safety and maintenance planning. A novel opportunistic predictive maintenance-decision (OPM) method integrating of machinery prognostic and opportunistic maintenance model is proposed in this paper to indicate the optimal maintenance time with minimal cost and safety constrains. DBN-HAZOP model quantifies hazard and operability analysis by dynamic Bayesian networks to provide prospective degradation trends of each component and the overall system for maintenance decision making. It is developed by integrating the prior knowledge of the interactions and dependencies among components and also the external environment, while the online condition monitoring data which is further to update the parameters of the model. Based on the future degradation trends given by DBN-HAZOP model, a local optimal proactive maintenance practice can be determined for each component by minimizing the expected maintenance cost per time unit. Understanding that for a complex system, whenever one of the components stops to perform a predictive maintenance action, the whole complex system must be stopped, at this moment, PdM opportunities arise for the other degraded components in the system at a reduced additional cost. Therefore, this paper further proposes an opportunistic PdM strategy for global cost optimization of predictive maintenance for the whole system, which considers failure probabilities, repair costs, down time cost and set-up cost. Case studies are given throughout to show how this approach works, and the sensitivity of the results to some of the driving cost parameters has also been examined.

Optimal stopping for the predictive maintenance of a structure subject to corrosion

This paper presents a numerical method to compute the optimal maintenance time for a complex dynamic system applied to an example of maintenance of a metallic structure subject to corrosion. An arbitrarily early intervention may be uselessly costly, but a late one may lead to a partial/complete failure of the system, which has to be avoided. One must therefore find a balance between these too-simple maintenance policies. To achieve this aim, the system is modelled by a stochastic hybrid process. The maintenance problem thus corresponds to an optimal stopping problem. A numerical method is proposed to solve the optimal stopping problem and optimize the maintenance time for this kind of process.

Optimal maintenance time for imperfect maintenance actions on repairable product

This paper develops a maintenance strategy for repairable products that combines imperfect maintenance actions at pre-scheduled times and minimal repair actions for failures. Under a power law process of failures, an expected total cost is developed that involves the sum of the total cost of imperfect preventive maintenances and the expected total cost of minimal repairs. Moreover, a searching procedure is provided to determine the optimal maintenance schedule within a finite time span of warranty. When the parameters of the power law process are unknown, the accuracy of the estimated maintenance schedule is evaluated based on data through an asymptotic upper bound for the difference of the true expected total cost and its estimate. The proposed method is applied to an example regarding the maintenance of power transformers and the performance of the proposed method is investigated through a numerical study. Numerical results show that the proposed maintenance strategy could save cost whether an imperfect maintenance action or the perfect maintenance action is implemented.

Estimation of base rate by a goal programming approach: a case study from India

Each equipment plays an imperative function and its malfunction leads to heavy cost and plant development. The current study is resolute on base-rate estimation of breakdowns of boilers at sugar plant (capacity – 2,500 tons crushed per day) under different set of parameters. It is relatively difficult to guesstimate the base rate, as one knows hardly the expenses incurred following a breakdown in terms of many associated expenditures. To estimate the base rate, a preemptive goal programming (GP) model is formulated that considers all major influencing factors. With any change in factors, maintenance time also gets pretentious. However, these factors are further sub-levelled for a very precise inference of optimal maintenance time. Additional constraints are obtained by providing priority-based benchmark jobs. Finally, the model is worked out within 3% variation from the benchmark jobs involved.