Simulation-based optimisation of maintenance systems: Industrial case studies

Investigating the optimum blend of maintenance strategies for a given manufacturing system is a continuing concern amongst maintenance academics and professionals. Recent evidence suggests that little research is conducted on the simulation optimisation of maintenance in industrial systems. This study was designed to make an important contribution to the field of simulation-based optimisation of maintenance by presenting two empirical case studies: a tyre re-treading factory and a petro-chemical plant. It is one of the first to optimise various maintenance strategies simultaneously with their parameters in industrial manufacturing systems while considering production dynamics. Stochastic Discrete Event Simulation models were developed and connected to a Multi-Objective Optimisation engine. Various maintenance strategies were investigated including Corrective Maintenance, Preventive Maintenance, Opportunistic Maintenance and Condition-Based Maintenance. The results of this research suggest that over-looking the optimisation of maintenance on the strategic level may lead to sub-optimal solutions. In addition, it appears that traditional trade-offs between maintenance cost and production throughput are not present in some maintenance systems. This is an interesting observation that requires further investigation and experimentation

Simulation-based optimisation of complex maintenance systems

There is a potential as well as a growing interest amongst researchers to utilise simulation in optimising maintenance systems. The state of the art in simulation-based optimisation of maintenance was established by systematically classifying the published literature and outlining main trends in modelling and optimising maintenance systems. In general, approaches to optimise maintenance varied significantly in the literature. Overall, these studies highlight the need for a framework that unifies the approach to optimising maintenance systems. Framework requirements were established through two main sources of published research. Surveys on maintenance simulation optimisation were examined to document comments on the approaches authors follow while optimising maintenance systems. In addition, advanced and future maintenance strategies were documented to ensure it can be accommodated in the proposed framework. The proposed framework was developed using a standard flowchart tool due to its familiarity and ability to depict decision structures clearly. It provides a systematic methodology that details the steps required to connect the simulation model to an optimisation engine. Not only it provides guidance in terms of formulating the optimal problem for the maintenance system at hand but it also provides support and assistance in defining the optimisation scope and investigating applicable maintenance strategies. Additionally, it considers current issues relating to maintenance systems both in research and in practice such as uncertainty, complexity and multi-objective optimisation. The proposed framework cannot be applied using existing approaches for modelling maintenance. Existing modelling approaches using simulation have a number of limitations: The maintenance system is modelled separately from other inter-related systems such as production and spare parts logistics. In addition, these approaches are used to model one maintenance strategy only. A novel approach for modelling maintenance using Discrete Event Simulation is proposed. The proposed approach enables the modelling of interactions amongst various maintenance strategies and their effects on the assets in non-identical multi-unit systems. Using the proposed framework and modelling approach, simulation-based optimisation was conducted on an academic case and two industrial cases that are varied in terms of sector, size, number of manufacturing processes and level of maintenance documentation. Following the structured framework enabled discussing and selecting the suitable optimisation scope and applicable maintenance strategies as well as formulating a customised optimal problem for each case. The results of the study suggest that over-looking the optimisation of maintenance strategies may lead to sub-optimal solutions. In addition, this research provides insights for non-conflicting objectives in maintenance systems

Performance Evaluation of Remanufacturing Systems

Implementation of new environmental legislation and public awareness has increased the responsibility on manufacturers. These responsibilities have forced manufacturers to begin remanufacturing and recycling of their goods after they are disposed or returned by customers. Ever since the introduction of remanufacturing, it has been applied in many industries and sectors. The remanufacturing process involves many uncertainties like time, quantity, and quality of returned products. Returned products are time sensitive products and their value drops with time. Thus, the returned products need to be remanufactured quickly to generate the maximum revenue. Every year millions of electronic products return to the manufacturer. However, only 10% to 20% of the returned products pass through the remanufacturing process, and the remaining products are disposed in the landfills. Uncertainties like failure rate of the servers, buffer capacity and inappropriate preventive maintenance policy would be highly responsible the delays in remanufacturing. In this thesis, a simulation based experimental methodology is used to determine the optimal preventive maintenance frequency and buffer allocation in a remanufacturing line, which will help to reduce the cycle time and increase the profit of the firm. Moreover, an estimated relationship between preventive maintenance frequency and MTBF (Mean Time Between Failure) is presented to determine the best preventive maintenance frequency for any industry. The solution approach is applied to a computer remanufacturing and a cell phone remanufacturing industry. Analysis of variance and regression analysis are performed to denote the influential factors in the remanufacturing line, and optimization is done by using the regression techniques and ANOVA results

Through life costing in defence electronic systems: an integrated data-driven multi-level approach

Cost estimating is a business process that is critical to the defence sector, where many products have low volumes and long life cycles. The nature of a defence system is often unique (for example, a naval platform) which consists of a number of sub-systems and components. For the design of such a system cost estimating is a critical task, in particular the requirement to predict the cost throughout the systems lifetime. The aim of this paper therefore is to discuss an integrated approach that provides a general framework for through life costing in defence systems via the development of: (1) a generic data library to support designers and cost estimators, (2) data searching and transfer mechanisms to support a top-down and bottom-up hybrid cost modelling approach, (3) capturing reliability data to support product services. The paper is divided into several sections, first, a review of relevant research projects concerning integration and data capture for cost modelling. This is followed by a section, which highlights problems of performing cost estimates for low volume products, and subsequently the proposed solution, methods of cost estimation and example applications. Perhaps most importantly, the methods created in this research are able to enhance decision-making and accelerate the responsiveness of the business bidding process

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 applied to maintenance

The thesis covers studies conducted during 1976-79 under a Science Research Council contract to examine the uses of reliability information in decision-making in maintenance in the process industries. After a discussion of the ideal data system, four practical studies of process plants are described involving both Pareto and distribution analysis. In two of these studies the maintenance policy was changed and the effect on failure modes and frequency observed. Hyper-exponentially distributed failure intervals were found to be common and were explained after observation of maintenance work practices and development of theory as being due to poor workmanship and parts. The fallacy that constant failure rate necessarily implies the optimality of maintenance only at failure is discussed. Two models for the optimisation of inspection intervals are developed; both assume items give detectable warning of impending failure. The first is based upon constant risk of failure between successive inspections 'and Weibull base failure distribution~ Results show that an inspection/on-condition maintenance regime can be cost effective even when the failure rate is falling and may be better than periodiC renewals for an increasing failure situation. The second model is first-order Markov. Transition rate matrices are developed and solved to compare continuous monitoring with inspections/on-condition maintenance an a cost basis. The models incorporate planning delay in starting maintenance after impending failure is detected. The relationships between plant output and maintenance policy as affected by the presence of redundancy and/or storage between stages are examined, mainly through the literature but with some original theoretical proposals. It is concluded that reliability techniques have many applications in the improvement of plant maintenance policy. Techniques abound, but few firms are willing to take the step of faith to set up, even temporarily, the data-collection facilities required to apply them. There are over 350 references, many of which are reviewed in the text, divided into chapter-related sectionso Appendices include a review of Reliability Engineering Theory, based on the author's draft for BS 5760(2) a discussion of the 'bath-tub curves' applicability to maintained systems and the theory connecting hyper-exponentially distributed failures with poor maintenance practices

Survey and preliminary evaluation of barometric altimetry techniques Final report

Survey and evaluation of barometric altimeter technique

Advanced information processing system: The Army fault tolerant architecture conceptual study. Volume 2: Army fault tolerant architecture design and analysis

Described here is the Army Fault Tolerant Architecture (AFTA) hardware architecture and components and the operating system. The architectural and operational theory of the AFTA Fault Tolerant Data Bus is discussed. The test and maintenance strategy developed for use in fielded AFTA installations is presented. An approach to be used in reducing the probability of AFTA failure due to common mode faults is described. Analytical models for AFTA performance, reliability, availability, life cycle cost, weight, power, and volume are developed. An approach is presented for using VHSIC Hardware Description Language (VHDL) to describe and design AFTA's developmental hardware. A plan is described for verifying and validating key AFTA concepts during the Dem/Val phase. Analytical models and partial mission requirements are used to generate AFTA configurations for the TF/TA/NOE and Ground Vehicle missions

Advanced Rotorcraft Transmission (ART) program

Work performed by the McDonnell Douglas Helicopter Company and Lucas Western, Inc. within the U.S. Army/NASA Advanced Rotorcraft Transmission (ART) Program is summarized. The design of a 5000 horsepower transmission for a next generation advanced attack helicopter is described. Government goals for the program were to define technology and detail design the ART to meet, as a minimum, a weight reduction of 25 percent, an internal noise reduction of 10 dB plus a mean-time-between-removal (MTBR) of 5000 hours compared to a state-of-the-art baseline transmission. The split-torque transmission developed using face gears achieved a 40 percent weight reduction, a 9.6 dB noise reduction and a 5270 hour MTBR in meeting or exceeding the above goals. Aircraft mission performance and cost improvements resulting from installation of the ART would include a 17 to 22 percent improvement in loss-exchange ratio during combat, a 22 percent improvement in mean-time-between-failure, a transmission acquisition cost savings of 23 percent of $165K, per unit, and an average transmission direct operating cost savings of 33 percent, or$24K per flight hour. Face gear tests performed successfully at NASA Lewis are summarized. Also, program results of advanced material tooth scoring tests, single tooth bending tests, Charpy impact energy tests, compact tension fracture toughness tests and tensile strength tests are summarized

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