Maintenance Strategies Design and Assessment Using a Periodic Complexity Approach

People become more dependent on various devices, which do deteriorate over time and their operation becomes more complex. This leads to higher unexpected failure chance, which causes inconvenience, cost, time, and even lives. Therefore, an efficient maintenance strategy that reduces complexity should be established to ensure the system performs economically as designed without interruption. In the current research, a comprehensive novel approach is developed for designing and evaluating maintenance strategies that effectively reduce complexity in a cost efficient way with maximum availability and quality. A proper maintenance strategy application needs a rigorous failure definition. A new complexity based mathematical definition of failure is introduced that is able to model all failure types. A complexity-based metric, complication rate , is introduced to measure functionality degradation and gradual failure. Maintenance reduces the system complexity by system resetting via introducing periodicity. A metric for measuring the amount of periodicity introduced by maintenance strategy is developed. Developing efficient maintenance strategies that improve system performance criteria, requires developing the mathematical relationships between maintenance and quality, availability, and cost. The first relation relating the product quality to maintenance policy is developed using the virtual age concept. The aging intensity function is then deployed to develop the relation between maintenance and availability. The relation between maintenance and cost is formulated by investigating the maintenance effect on each cost element. The final step in maintenance policy design is finding the optimum periodicity level. Two approaches are investigated; weighted sum integrated with AHP and a comfort zones approach. Comfort zones is a new developed physical programming based optimization heuristic that captures designer preferences and limitations without substantial efforts in tweaking or calculating weights. A mining truck case study is presented to explain the application of the developed maintenance design approach and compare its results to the traditional reward renewal theory. It is shown that the developed approach is more capable of designing a maintenance policy that reduces complexity and simultaneously improves some other performance measures. This research explains that considering complexity reduction in maintenance policy design improves system functionality, and it can be achieved by simple industrially applicable approach

Modeling Preventive Maintenance in Complex Systems

This thesis presents an explicit consideration of the impacts of modeling decisions on the resulting maintenance planning. Incomplete data is common in maintenance planning, but is rarely considered explicitly. Robust optimization aims to minimize the impact of uncertainty--here, in contrast, I show how its impact can be explicitly quantified. Doing so allows decision makers to determine whether it is worthwhile to invest in reducing uncertainty about the system or the effect of maintenance. The thesis consists of two parts. Part I uses a case study to show how incomplete data arises and how the data can be used to derive models of a system. A case study based on the US Navy\u27s DDG-51 class of ships illustrates the approach. Analysis of maintenance effort and cost against time suggests that significant effort is expended on numerous small unscheduled maintenance tasks. Some of these corrective tasks are likely the result of deferring maintenance, and, ultimately decreasing the ship reliability. I use a series of graphical tests to identify the underlying failure characteristics of the ship class. The tests suggest that the class follows a renewal process, and can be modeled as a single unit, at least in terms of predicting system lifetime. Part II considers the impact of uncertainty and modeling decisions on preventive maintenance planning. I review the literature on multi-unit maintenance and provide a conceptual discussion of the impact of deferred maintenance on single and multi-unit systems. The single-unit assumption can be used without significant loss of accuracy when modeling preventive maintenance decisions, but leads to underestimating reliability and hence ultimately performance impacts in multi-unit systems. Next, I consider the two main approaches to modeling maintenance impact, Type I and Type II Kijima models and investigate the impact of maintenance level, maintenance interval, and system quality on system lifetime. I quantify the net present value obtained of the system under different maintenance strategies and show how modeling decisions and uncertainty affect how closely the actual system and maintenance policy approach the maximum net present value. Incorrect assumptions about the impact of maintenance on system aging have the most cost, while assumptions about design quality and maintenance level have significant but smaller impact. In these cases, it is generally better to underestimate quality, and to overestimate maintenance level

Reliability and Condition-Based Maintenance Analysis of Deteriorating Systems Subject to Generalized Mixed Shock Model

For successful commercialization of evolving devices (e.g., micro-electro-mechanical systems, and biomedical devices), there must be new research focusing on reliability models and analysis tools that can assist manufacturing and maintenance of these devices. These advanced systems may experience multiple failure processes that compete against each other. Two major failure processes are identified to be deteriorating or degradation processes (e.g., wear, fatigue, erosion, corrosion) and random shocks. When these failure processes are dependent, it is a challenging problem to predict reliability of complex systems. This research aims to develop reliability models by exploring new aspects of dependency between competing risks of degradation-based and shock-based failure considering a generalized mixed shock model, and to develop new and effective condition-based maintenance policies based on the developed reliability models. In this research, different aspects of dependency are explored to accurately estimate the reliability of complex systems. When the degradation rate is accelerated as a result of withstanding a particular shock pattern, we develop reliability models with a changing degradation rate for four different shock patterns. When the hard failure threshold reduces due to changes in degradation, we investigate reliability models considering the dependence of the hard failure threshold on the degradation level for two different scenarios. More generally, when the degradation rate and the hard failure threshold can simultaneously transition multiple times, we propose a rich reliability model for a new generalized mixed shock model that is a combination of extreme shock model, δ-shock model and run shock model. This general assumption reflects complex behaviors associated with modern systems and structures that experience multiple sources of external shocks. Based on the developed reliability models, we introduce new condition-based maintenance strategies by including various maintenance actions (e.g., corrective replacement, preventive replacement, and imperfect repair) to minimize the expected long-run average maintenance cost rate. The decisions for maintenance actions are made based on the health condition of systems that can be observed through periodic inspection. The reliability and maintenance models developed in this research can provide timely and effective tools for decision-makers in manufacturing to economically optimize operational decisions for improving reliability, quality and productivity.Industrial Engineering, Department o

Cost optimization of maintenance scheduling for wind turbines with aging components

A major part of the wind turbine operation cost is resulted from the maintenance of its components. This thesis deals with the theory, algorithms, and applications concerning minimization of the maintenance cost of wind power turbines, using mathematical modelling to find the optimal schedules of preventive maintenance activities for multi-component systems.\ua0 \ua0 The main contributions of this thesis are covered by the four papers appended. The unifying goal of these papers is to produce new optimization models resulting in effective and fast algorithms for preventive maintenance time schedules. The features of the multi-component systems addressed in our project are: aging components, long-term, and short-term planning, planning for a wind power farm, end of the lifetime of the wind farm, maintenance contracts, and condition monitoring data.\ua0 \ua0 For the long-term maintenance planning problem, this thesis contains an optimization framework that recognizes different phases of the wind turbine lifetime. For short-term planning problem, this thesis contains two modeling frameworks, which both focus on the planning of the next preventive maintenance activities. Our virtual experiments show that the developed optimization models adopt realistic assumptions and can be accurately solved in seconds. One of these two frameworks is further extended so that available condition monitoring data can be incorporated for regular updates of the components\u27 hazard functions. In collaboration with the Swedish Wind Power Technology Center at Chalmers and its member companies, we test this method with real-world wind farm data. Our case studies demonstrate that this framework may result in remarkable savings due to the smart scheduling of preventive maintenance activities by monitoring the ages of the components as well as operation data of the wind turbines. \ua0 \ua0 We believe that in the future, the proposed optimization model for short-term planning based on the component age and condition monitoring data can be used as a key module in a maintenance scheduling app

Optimum maintenance strategy for deteriorating bridge structures based on lifetime functions

The highway networks of most European and North American countries are completed or close to completion. However, many of their bridges are aging, and in the United States alone a very significant part of the about 600,000 existing bridges is considered to be deficient and must be replaced, repaired or upgraded in the short term. The funds available for the maintenance of existing highway bridges are extremely limited when compared with the huge investment necessary, and must, therefore, be spent wisely. In this paper, a model based on lifetime functions for predicting the evolution in time of the reliability of deteriorating bridges under maintenance is presented. This model uses the probability of satisfactory system performance during a specified time interval as a measure of reliability and treats each bridge structure as a system composed of several components. In this manner, it is possible to predict the structural performance of deteriorating structures in a probabilistic framework. In addition, the optimum maintenance strategy is identified using as objective the minimization of the present value of the life-cycle maintenance cost. An existing bridge is analyzed using lifetime functions and its optimum maintenance strategy is found.U.S. National Science Foundation - CMS-9912525; CMS-0217290.Colorado Department of TransportationDutch Ministry of Transportation, Public Works, and Water Management

Optimal Periodic Inspection of a Stochastically Degrading System

This thesis develops and analyzes a procedure to determine the optimal inspection interval that maximizes the limiting average availability of a stochastically degrading component operating in a randomly evolving environment. The component is inspected periodically, and if the total observed cumulative degradation exceeds a fixed threshold value, the component is instantly replaced with a new, statistically identical component. Degradation is due to a combination of continuous wear caused by the component\u27s random operating environment, as well as damage due to randomly occurring shocks of random magnitude. In order to compute an optimal inspection interval and corresponding limiting average availability, a nonlinear program is formulated and solved using a direct search algorithm in conjunction with numerical Laplace transform inversion. Techniques are developed to significantly decrease the time required to compute the approximate optimal solutions. The mathematical programming formulation and solution techniques are illustrated through a series of increasingly complex example problems

Extending the life of nuclear power plants : technical and institutional issues

Managing nuclear power plant aging is one of the important technical issues which needs to be addressed by utilities in order to extend the operating life of some of the early LWR power stations. Plant managers must understand complex aging phenomena, identify aging effects, anticipate failure, and mitigate the aging process. Typically, the age-related design limits of crucial components are not known, and this information usually does not appear to be easily available from the equipment vendor. Electrical cables, insulation and instrumentation are most susceptible to age-related degradation. material degradation due to corrosion is the main costly problem affecting a small but important portion of piping and major equipment. Upgrading the plant, replacing aging equipment, and implementing good maintenance, surveillance and spare parts inventory control programs are actions a utility can take to extend the life of operating nuclear plants. Considerable institutional uncertainties are associated with nuclear plant life extension. These spring mainly from the absence of clearly defined policies by the U.S. Nuclear Regulatory Commission stating the technical and procedural requirements for plant life extension. From precedents established to-date, it is reasonable to expect plant operation to be permitted for most plants for a total of 40 years after start of commerical operation. As a large share of the net discounted benefits of extended life operation may be derived from the first decade of additional life, the basis for utility investments for life extension is thus assured.Northeast Utilities Service Compan

Development Of Maintenance Policy Selection (MPS) Model To Select The Optimal Maintenance Policy For A System In Palm Oil Mill

The maintenance is change due to the development of technology. Under the changes, different maintenance policies have been developed, from corrective maintenance until predictive maintenance. Penyelenggaraan telah berubah dengan perkembangan teknologi. Di bawah perubahan ini , pelbagai jenis dasar penyelenggaraan telah dibangunka

The Psychology of Dynamic Product Maintenance

The processes that underlie consumer decisions to invest in the maintenance of a durable good over time are examined. The work centers on a hypotheses that consumers make decisions about whether to repair or replace a good that has suffered a decrease in performance through a process that assesses the value of repair actions relative to two points of reference: the normal rate at which the performance of goods declines as they age (age-indexing), and how the timing and cost of the repair compares to parallel norms for repair expenditures (expenditure indexing). We show how these heuristics can be represented by a cognitive algebra that models maintenance decisions as a series of myopic utility-maximization problems. This process yields outcomes that can approximate those that would emerge from an optimal dynamic maintenance policy in some cases, but significantly depart from optimality in others. The algebra is then used to generate a series of predictions about how maintenance decisions may depart from normative benchmarks that are tested in a dynamic computer-pet ownership simulation. Actual maintenance behavior is characterized by a number of biases that are consistent with theoretical predictions, including a seemingly contradictory tendency to undermaintain and prematurely replace goods of superior value when they were acquired, yet be overly reluctant to part with and over-maintain inferior goods. A discussion of the implications of the work for understanding real-world biases in product care and maintenance behavior is offered