Profit Analysis of a Warm Standby Non-Identical Units System with Single Server Subject to Priority

The present paper deals with the profit analysis of a warm standby non-identical (one is main unit another is duplicate unit) units system with single server. The model consists of two non-identical units –one is operative and the other kept as warm standby and one unit is sufficient to make the system in operative mode. The main unit may fail directly from normal mode and the warm standby unit can fail owing to remain unused for a longer period of time. There is a single server, who gives priority to repair of the main unit over the repair of the duplicate unit. The time is taken to repair activity by the server follows negative exponential distribution whereas the distributions of unit are taken as arbitrary with different probability density functions. The expressions of various efficiency measures are analyzed in steady state using semi-Markov process and regenerative point technique. Also, taking the arbitrary values for the parameters (i.e. ?, µ, ? and ?) to delineate the behavior of some important performance measures to check the efficacy of the system model under such situations shown in the graphs

Stochastic Modeling of a Concrete Mixture Plant with Preventive Maintenance

In this paper, a stochastic model for concrete mixture plant with Preventive Maintenance (PM) is analyzed in detail by using a supplementary variable technique. In a concrete mixture plant eight subsystems are arranged in a series. The system goes under PM after a maximum operation time and work as new after PM. The time to failure of each subsystem follows a negative exponential distribution while PM and repair time distributions are taken as arbitrary. A sufficient repair facility is provided to the system for conducting PM and repair of the system. Repair, maintenance and switch devices are perfect. All random variables are statistically independent. Various measures of system effectiveness such as reliability, mean time to system failure (MTSF), are derived using a supplementary variable technique. The numerical results for reliability and availability are obtained for particular values of various parameters and costs

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

A survey of the machine interference problem

This paper surveys the research published on the machine interference problem since the 1985 review by Stecke & Aronson. After introducing the basic model, we discuss the literature along several dimensions. We then note how research has evolved since the 1985 review, including a trend towards the modelling of stochastic (rather than deterministic) systems and the corresponding use of more advanced queuing methods for analysis. We conclude with some suggestions for areas holding particular promise for future studies.Natural Sciences and Engineering Research Council (NSERC) Discovery Grant 238294-200

Modeling reliability considerations in the design and analysis of cellular manufacturing systems.

Reliability plays a vital role in the overall performance of cellular manufacturing systems (CMSs). Machine failures significantly impact the fulfillment of due dates and other performance criteria, despite the option of part rerouting to alternative workstations. These facts suggest a need for the consideration of machine reliability during the operation allocation process. Attempting to improve a system\u27s reliability invariably results in higher costs. It follows that the ideal strategy for achieving optimum balance lies in an approach that integrates both cost and reliability information. A mixed integer multi-objective mathematical programming model that incorporates machine reliability and cost considerations is developed for the design of CMSs. The model selects processing route for each part type which maximizes the overall system reliability of machines along the route, while minimizing the overall costs. The proposed approach provides flexible routing, ensuring high CMS performance by minimizing the impact of machine failure through the provision of alternative process routes. To account for the constant and increasing failure pattern of manufacturing machines, the CMS design model considers both the exponential and Weibull distribution approaches. A performance evaluation criterion in terms of system availability for the part-process plan assignment based on the exponential distribution is also developed. Applicability of the model is demonstrated by solving example problems by following the ∈-constraint approach. Optimization techniques for solving such models for large practical-size problems require a substantial amount of time and memory space; therefore, a heuristic, based on the basic steps to simulated annealing and solution generation procedure of genetic algorithm is developed. The heuristic is evaluated by comparing the solutions generated by the heuristic with the LP relaxation solution for the large problems and optimal solution for the smaller-sized problems. The results reveal that the heuristic performs well in various problem instances for reliability and cost combinations. The sensitivity of the model outputs to key factors has also been investigated. A reliability-based, preventive maintenance (PM) planning model is also incorporated, allowing CMS to restrict deterioration of machines due to usage and age and improve system reliability. A procedure for the integration of PM planning into the CMS design model is included for overall reliability and cost improvement of the CMS. Example problems are solved to illustrate the model\u27s applicability.* *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation).Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .D37. Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 4032. Thesis (Ph.D.)--University of Windsor (Canada), 2006

Critical Infrastructures: Enhancing Preparedness & Resilience for the Security of Citizens and Services Supply Continuity: Proceedings of the 52nd ESReDA Seminar Hosted by the Lithuanian Energy Institute & Vytautas Magnus University

Critical Infrastructures Preparedness and Resilience is a major societal security issue in modern society. Critical Infrastructures (CIs) provide vital services to modern societies. Some CIs’ disruptions may endanger the security of the citizen, the safety of the strategic assets and even the governance continuity. The European Safety, Reliability and Data Association (ESReDA) as one of the most active EU networks in the field has initiated a project group on the “Critical Infrastructure/Modelling, Simulation and Analysis – Data”. The main focus of the project group is to report on the state of progress in MS&A of the CIs preparedness & resilience with a specific focus on the corresponding data availability and relevance. In order to report on the most recent developments in the field of the CIs preparedness & resilience MS&A and the availability of the relevant data, ESReDA held its 52nd Seminar on the following thematic: “Critical Infrastructures: Enhancing Preparedness & Resilience for the security of citizens and services supply continuity”. The 52nd ESReDA Seminar was a very successful event, which attracted about 50 participants from industry, authorities, operators, research centres, academia and consultancy companies.JRC.G.10-Knowledge for Nuclear Security and Safet

Modernizing a Preventive Maintenance Strategy for Facility and Infrastructure Maintenance

Preventive maintenance (PM) is defined as regularly scheduled maintenance actions based on average failure rates. A properly implemented PM strategy can provide many benefits to an organization in terms of extending equipment life, optimizing resource expenditures, and balancing work schedules. Periodic evaluation of a PM strategy can help identify ways to improve efficiencies and maximize effectiveness. This research effort was accomplished by performing a case study of the United States Air Force’s infrastructure and facility PM program known as the Recurring Work Program (RWP). The methodology consisted of two phases. The first phase, intended to develop an understanding of the gap between the current program and what it needs to become, consisted of two segments: data collection and a strengths, weaknesses, opportunities, and threats (SWOT) analysis. Data was collected during 25 interviews with a wide variety of Air Force members highly experienced with the RWP. Using the interview data, the SWOT analysis compared the state of the current program to relevant maintenance management theory and best practices from industry; this analysis resulted in the identification of one strength, six weaknesses, eight opportunities, and seven threats to the RWP. The second phase of the methodology consisted of developing a model to bridge the gap between the current RWP and what it needs to become. It resulted in eight Focus Areas (FAs) that were based on the findings from the SWOT analysis; each FA represents a unique theme of practical recommendations for improving the program. As a result of this research, maintenance managers have a practical tool to help evaluate and modernize their facilities and infrastructure PM strategy. Additionally, the Air Force has a model for modernizing its RWP

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

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

RELIABILITY CENTERED MAINTENANCE (RCM) FOR ASSET MANAGEMENT IN ELECTRIC POWER DISTRIBUTION SYSTEM

The purpose of Maintenance is to extend equipment life time or at least the mean time to the next failure. Asset Maintenance, which is part of asset management, incurs expenditure but could result in very costly consequences if not performed or performed too little. It may not even be economical to perform it too frequently. The decision therefore, to eliminate or minimize the risk of equipment failure must not be based on trial and error as it was done in the past. In this thesis, an enhanced Reliability-Centered Maintenance (RCM) methodology that is based on a quantitative relationship between preventive maintenance (PM) performed at system component level and the overall system reliability was applied to identify the distribution components that are critical to system reliability. Maintenance model relating probability of failure to maintenance activity was developed for maintainable distribution components. The Markov maintenance Model developed was then used to predict the remaining life of transformer insulation for a selected distribution system. This Model incorporates various levels of insulation deterioration and minor maintenance state. If current state of insulation ageing is assumed from diagnostic testing and inspection, the Model is capable of computing the average time before insulation failure occurs. The results obtained from both Model simulation and the computer program of the mathematical formulation of the expected remaining life verified the mathematical analysis of the developed model in this thesis. The conclusion from this study shows that it is beneficial to base asset management decisions on a model that is verified with processed, analysed and tested outage data such as the model developed in this thesis