An integrated optimization framework for multi-cycle environmental stress screening tests and preventive maintenance scheduling

Jet engines are critical assets in aircraft, and their availability is crucial in the modern aircraft industry. Therefore, their maintenance scheduling is one of the major tasks an airline has to make during an engine’s lifetime. A proper engine maintenance schedule can significantly reduce maintenance costs without compromising the aircraft's reliability and safety. Different maintenance scheduling approaches have been used for jet engines, such as corrective, preventive, and predictive maintenance strategies. Regarding the safety demands in aircraft industries, preventive maintenance is a frequent maintenance method for jet engines. However, preventive maintenance schedules are often use fixed maintenance intervals, which is usually suboptimal. This paper focuses on minimizing a jet engine's overall maintenance cost by optimizing its preventive maintenance schedule based on an engine’s comprehensive reliability model. A hierarchical optimization framework including the golden section search and genetic algorithms is applied to find the optimal set of preventive maintenance number and their times and the components to be replaced at those times during the jet engine's overall lifetime. The Monte Carlo simulation is used to estimate the engine’s failure times using their lifetime distributions from the reliability model. The estimated failure times are then used to determine the engine's overall corrective and preventive maintenance costs during its lifetime. Finally, an optimal preventive maintenance schedule is proposed for an RB 211 jet engine using the presented method. In the end, comparing the proposed method's overall maintenance cost with two other maintenance methods demonstrates the proposed schedule's effectiveness. The method presented in this paper is generic, and it can be used for other similar engineering systems.

The maintenance of any system can be categorized in two ways: failure based maintenance (corrective maintenance) and life based maintenance (preventive maintenance). The time interval at which the preventive maintenance could be scheduled is dependent on both the life distribution of the subsystems/components and the total cost involved in the maintenance activity. However, the corrective maintenance cannot be avoided when a random failure of a component occurs. The total cost of the maintenance depends on the number of components replaced during the entire operating period of the system and the respective cost involved in maintenance actions. In this paper the preventive maintenance schedule (t/sub 0/) is calculated taking the failure distribution of a component as Weibull. The variation in (t/sub 0/) with respect to the shape parameter (/spl beta/) and characteristic life (/spl alpha/) of the Weibull distribution was studied by the author for a given preventive maintenance cost and corrective maintenance cost. The ratio of the corrective maintenance cost to the preventive maintenance cost (R/sub cp/) has a significant effect on the maintenance schedule (t/sub 0/). Thus the variation of (t/sub 0/) with (R/sub cp/) is addressed in this paper. The ratio is varied between 10 and 1.5. It is found that the (t/sub 0/) value increases as shape parameter increases when the ratio is 10 whereas the (to) value decreases when the ratio is 1.5. This shows that as we decrease the cost ratio (R/sub cp/) the frequency of preventive maintenance decreases with shape parameter. This in turn implies that the frequency of the preventive maintenance schedule can be kept at minimum by bringing down the cost of corrective maintenance. Sensitivity analysis carried out on the (t/sub 0/) values indicates that the variation in the cost of maintenance is about 5 to 8 percent :in all the cases even when the value of (t/sub 0/) is increased. by 50% over the optimum value. This indicates, while the optimum value of (to) obtained may be of academic interest, one can 'in practice' choose a convenient value near the optimum without appreciably affecting the advantage in terms of cost.

Opportunistic preventive maintenance scheduling for multi-unit reusable rocket engine system based on the variable maintenance task window method

The migration of environmental stress screening (ESS), a military convention, into other markets is discussed. ESS provides an inexpensive way to improve product reliability and identify flaws. A well-designed ESS program, applied to both prototype designs and actual production units, adds little time to the production process, although time will be needed to rework items with defects. The use of ESS in the development of IBM's 4234 printer is examined. The differences between qualification testing and ESS for screening during production are discussed, as well as alternatives to ESS. The controversy in the ESS field over tight tolerances is described. To illustrate the value of ESS, its use at Lockheed-California Company in 1981, to screen switching logic units used in US Navy S-3A antisubmarine aircraft, is examined.

Security constrained Preventive Maintenance (PM) scheduling is addressed as a crucial issue especially coping with new challenges of smart grids. Under the smart grid environment, Demand Response Resources (DRRs) are considered as virtual power plants in energy policy decisions which affect the controllability of power system ranging from short-term to long-term. Here, DRRs are regarded as a virtual resource of capacity reserve acquisition. Furthermore, under new environment, multifarious objectives will be imposed to PM scheduling which can be replicated into a single objective problem via a weighted approach with consideration of arbitrary weighting coefficients. In this paper, a new structure is proposed for determining the efficient weighting factors for reserve cost, operation cost, and generated emissions in PM scheduling associated with DR Programs (DRPs) the so-called PMDRPs. Utilizing Entropy method as one of the Multi Attribute Decision Making (MADM) techniques, the aforementioned factors are determined in a two phase hierarchy. Using the nominated structure, the maintenance scheme, commitment status, energy scheduling, and demand-as well as supply-side reserve scheduling are determined along the scheduling horizon. The numerical studies are carried out on the IEEE-RTS to confirm the capability of the proposed framework.

PurposeThis paper aims to revisit the preventive maintenance scheduling literature. The problem to be solved is the simultaneous scheduling of resource‐constrained preventive maintenance and operations. In particular, the expression that defines the period‐dependent cost function for a preventive maintenance scheduling activity is redefined. A case study is presented from the shipping industry.Design/methodology/approachIn this paper a mathematical theory of differential calculus known as three‐dimensional wave equation is applied. The methodology involves transforming the preventive maintenance cost function that is expressed in terms of several variables into a more precise framework. The motivation for the work is the need to measure the total preventive maintenance scheduling cost more precisely than with the use of the existing linear cost structure.FindingsIn this paper the findings from the analysis carried out found evidence that validates the claim of the feasibility of analyzing preventive maintenance cost using the approach proposed.Research limitations/implicationsThe paper shows that, in practice, maintenance managers strive to reduce the cost of preventive maintenance activities in order to achieve low cost production of goods. This would encourage a high patronage of customers and prevent decisions being made on wrong data. The approach presented here aims at correcting this weakness by revealing a more precise and reliable method of preventive maintenance scheduling cost computation. This is a scientific tool that should be of immense benefit to maintenance planners, particularly those actively engaged in scheduling functions.Originality/valueThe work in this paper is new, since a novel framework is presented in a way that has not been documented earlier.

Two-stage maintenance of a production system with exponentially distributed On- and Off-periods

Nowadays, almost every firm focuses to beat the global competition across the worldwide. In order to deal with such situation, companies are undertaking efforts to improve the productivity of their products but at the minimum possible cost. Asset management is one of the ways to enhance the productivity under cost constraint which may also be seen as the management strategy for different the phases of asset life cycle. Operations and maintenance is one of the important phases of asset life cycle that can be focussed to improve the productivity. This phase may extend the equipment life, improves availability and retains them in healthy positions. But at the same time, frequent maintenance actions may increase the maintenance cost thereby increase the life cycle cost of a product. The maintenance cost only includes the preventive and corrective maintenance cost and which may in-turn depend upon the scheduled maintenance interval. Thus, a trade-off between maintenance actions and operational objectives (i.e. availability, etc.) is required to minimize the maintenance cost. In this paper, the genetic algorithm is applied to optimize the maintenance cost for higher performance (i.e. availability). A case study is taken into consideration for implementing the GA to optimize the objective function. The three different cases are presented, in the first case, subassemblies are repaired during maintenance action(s); in the second case subassemblies are repaired in preventive maintenance action and while replaced in corrective maintenance action; in the last case, the subassemblies are replaced in both kind of maintenance. In order to check the robustness of the solution, the sensitivity analysis is also performs and that validates the strength of the solution methodology.

Economic production lot-sizing for an unreliable machine under imperfect age-based maintenance policy

Imperfect preventive maintenance optimization for flexible flowshop manufacturing cells considering sequence-dependent group scheduling

This paper presents a new strategy for reliability-based Preventive Inspection and Maintenance (PIM) scheduling of coal-fired power plants considering maintenance costs. Preventive Inspection and Maintenance Interval (PIMI) for any series or parallel subsystem is optimised at the reliability level where total (preventive and corrective) life cycle maintenance cost is the minimum. PIM of any series subsystem in a continuous operating power plant is difficult since off-working period is unavailable. Therefore a time management strategy followed by maintenance class formation is introduced for their PIM according to the estimated PIMIs. Here corrective maintenance of any failed subsystem along with PIM of the remaining subsystems in the same maintenance class is suggested. The case studies reveal that the optimum PIMIs of the subsystems vary from 75.8% to 93.2% reliability level. The proposed maintenance strategy is very simple, easy to implement, improves availability and reduces the maintenance costs.

Maintenance is regarded as one of the effective methods to enhance the reliability of nuclear power plant (NPP) system. The existing research works on NPP maintenance optimization mainly focus on the preventive maintenance (PM) scheduling, with the common assumption that activities are performed at scheduled time. Though the straightforward asset management brought by PM scheduling, its inflexibility could result to either unnecessary maintenance activities or undiscovered failures. On the other hand, due to the budget limit it's inappropriate to neglect the shortage of maintenance workers and the missed plans caused by it. In this research, an integrated scheduling of maintenance workers and activities for NPP subsystem is considered. Effective age is used to determine the performed maintenance activities while measuring its effectiveness. To deal with this problem, a mathematical model is established with the objective of minimizing the total cost. Maintenance starting time, workers and activities of each component over the scheduling horizon are regarded as decision variables. The proposed model is applied to an NPP feed water subsystem and solved by specially designed genetic algorithm.

This chapter provides an overview of operations-intensive organizations. The deep insights and applications of the asset maintenance contributing factors during asset maintenance phases such as the initial phase, implementation phase, and assessment phase are explained based on the research study conducted among asset maintenance managers/engineers working in operations-intensive organizations. This approach adds value to asset maintenance concepts by capturing tacit knowledge available to the industry.During the initial phase of asset maintenance, the key elements to be considered in planning and scheduling maintenance work are equipment history, work order with priority, preventive maintenance schedules, weekly work schedules, long-term/shutdown maintenance schedule, and work measurement backlog. A suitable Computerized Maintenance Management Software that integrates all these elements to maintain the maintenance scheduling and monitor work schedules is essential in asset maintenance. The importance of maintenance policy, allocation of budget resources, the role of materials management, and workforce resources are discussed in this chapter.During the asset maintenance implementation phase, the various elements of maintenance tactics such as preventive maintenance, predictive maintenance, total maintenance hours, compliance of maintenance schedules, Original Equipment Manufacturer (OEM) recommendations, Reliability-Centered Maintenance, Total Productive Maintenance, proactive maintenance, and shutdown maintenance are also deliberated in detail in this chapter. Further, the first area of change toward employee motivation should be employee empowerment. Maintenance managers should provide directions and point out the desired results; empowering maintenance supervisors to achieve the desired results within their capacity is elaborated in the employee empowerment topic.The key elements in evaluating maintenance performance such as maintenance cost, equipment history, training man-hours of maintenance staff, internal/industry norms, benchmarking measures and targets, and their role in formulating and selecting maintenance strategy are examined in this chapter.Maintenance practitioners believe that the maintenance challenges are helping in formulating maintenance strategies for the company. Maintenance challenges are the major issues faced by maintenance managers in their day-to-day asset maintenance function. Such issues are grouped into technical issues, contract issues, stakeholder issues, internal issues, and unexpected issues. Each of the asset maintenance contributing factors is supported by Industry Quotes in the Industry Quote Box.

Q: Due to constraints in our biomed budget, I was wondering: Is there anyway to reduce the rotations for sterilizers in our preventive maintenance (PM) program?A: Sure. In fact, several years ago, we reduced the rotation and the items we change on sterilizers. Our facility has 11 operating rooms (OR) and two labor-and-delivery rooms. Our sterilizers get fairly heavy use between 6 a.m. and 11 p.m. But we're definitely not as busy as facilities that run 24 hours a day, 365 days a year. So we reduced the PM schedule for some of them to a semiannual basis. This process has worked very well. We have not had any more failures than we had when we did them quarterly. We saved ourselves time and the materials we would have had in replacing those quarterly parts. Certain sterilizers, like the washer-sterilizers, still undergo quarterly PM due to their fairly heavy use and the fact that we only have two in the OR. If one goes down, the impact to our customer is significant.I am making my suggestions based on the assumption that your facility has a good preventive maintenance program and some sort of a program that enables you to track equipment. By tracking equipment repairs, you can monitor the results if you reduce your facility's PM schedule. Our facility continuously monitors work requests through a program in our database. This program allows use to “write” almost any report one can imagine. For example, one of our performance indicators is repeat work requests. We have a report that compiles all such requests during a given period—three months, in our case. Each technician is given the piece of the report related to the equipment that he or she is responsible for. The technician then checks the work requests for that equipment, looking for anything that can be incorporated into a regular PM schedule and prevent a failure. This allows your facility to “hold all the cards,” so to speak. If someone at your facility periodically monitors your success, you will be able to adjust your PM program accordingly.As for what parts can be reduced, that depends on several factors such as use, steam quality, water quality, and maintenance program effectiveness. Our water here tends to be higher in calcium than most parts can tolerate. So we keep water traps and water solenoids on a semiannual schedule. That said, our steam quality is fairly good, and we push our steam parts as long as possible. When we looked at ways to reduce cost, we looked along the line of steam—not water.Another key factor is staff care with equipment. For example, we once had someone in the instrument room who would slam the doors of the sterilizers so hard that the machines shook. We talked to him on several occasions, but he slammed the door right up until his last day at our facility. If that was not bad enough, after slamming it one time, this now-former employee cranked hard on the handle and then brought it to me to report, “It's broke.” Ha, ha! While he worked at our facility, we stepped up the PM frequency on the doors. We would check the handles for tightness, the arms that wind out to make sure they were not missing their holders, the door seal which would snap if brittle and the door was slammed, and the door cover to make sure it had not been bent.But let me end by returning to the big picture. You can and should alter your PM schedule to maximize your resources. Working with a good database, a good PM program can save you time and money. Remember: Garbage in, garbage out.

A joint preventive maintenance and spare parts provisioning strategy is suggested for a failure prone system. Replacements are carried-out at failure, if spare parts are available, and at spare parts replenishment delivery instants. Spare parts are provisioned according to an (s, Q) control policy. The ordering parameters, and preventive maintenance interval are derived from a mathematical model which aims at maximizing the system's availability under a budget constraint. The model takes into account the system lifetime distribution, the preventive and corrective maintenance costs and durations, as well as the total spare parts inventory management cost. Unlike classical inventory management models, the s-expected total cost is derived using the system lifetime distribution. Because each demand for spare parts is triggered by failure, the probabilities of shortage, and surplus will be evaluated based on the system failure distribution rather than the demand distribution during the lead-time. Numerical results have been obtained for an illustrative example.