ANALYSIS AND SIMULATION OF RELIABILITY IMPROVEMENT IN GEAR PUMP MACHINE USING LIFE CYCLE COST METHOD IN XYZ COMPANY

Abstract. Gear pump machine 51-98P01 is a machine that is available at XYZ company, which functions as a transportation machine for raw materials from one production process. Because of its function, if there is a downtime occurs on the machine, production process will stop operating because the raw material cannot be flowed. Those problems caused a large amount of expenses that company should pay because late of production. Downtime occurs due to several factors, such as failure to components on the machine, age that has exceeded the optimal age limit, and the number of maintenance crew that is not optimal. To solve the problems, the Life Cycle Cost (LCC) method is used to determine the optimum age of the machine and the optimal number of maintenance crew using the Life Cycle Cost (LCC) method. In addition, a calculation of the proposed maintenance time interval is also carried out to achieve a certain reliability value using a simulation of reliability improvement to see the effect of reliability on the total LCC. Based on data processing using the LCC method, it is known that LCC in 2018 is Rp1,333,195,316 while the optimum LCC is Rp.690,180,267 with optimal machine life of six years and the number of crew maintenance is one person. Simulation of reliability improvement that carried out on the gear pump machine components shows a decrease of  total LCC of the machine.Keywords: Downtime, Failure Cost Life Cycle Cost, Maintenance Crew, Reliabilit

Developing an optimum maintenance policy by life cycle cost analysis - a case study

This paper focuses on developing maintenance policies for critical assets to improve the production performance based on life cycle cost (LCC) analysis. A general approach is adopted for conducting the LCC analysis. The investigation is based on a case study to demonstrate how an optimum maintenance policy is determined. The relevant LCC structure in the case study is defined for the decision process which involves determination of the optimum life, repair limit and selection of materials, and trade-off between repair and replacement. The LCC analysis is based on statistical data modelling which facilitates decision-making on the optimal replacement of an asset and its remaining life. Based on the optimization and remaining life criterion, the optimal maintenance policy can be made. The results obtained from this case study include selection of the best lining material for use, determination of the optimal time for refractory lining replacement, the hot repair sequence required for maintaining the optimum condition and the repair limit for doing cold repairs before replacement, for one type of electric arc furnaces used in the steel industry

Financial view and profitability evaluation on multistate weighted k-out-of-n:F system reliability

A financial view is proposed for reliability evaluation of multi-state weighted k-out-of-n:F systems. Failure cost as the cost which is imposed on the components by failures is used to denote the importance weight of each component. The deterioration process of components over time is modelled by Markov chain. System failure behaviour is formulated by Universal Generating Function (UGF). Furthermore, the present value of system failure is calculated by considering time value of money. As a result, the system reliability is demonstrated as cost which is more sensible for managers. A numerical example is presented to illustrate the proposed approach. After that, a way is suggested to transform the system cost present value into system reliability value. MATLAB programming is developed to make a sensitivity analysis on example results. Therefore, the impact of maintenance activities is investigated to show how they can reduce system cost through improving the system reliability. Copyright © 2014 Inderscience Enterprises Ltd

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

Spatio-temporal probabilistic methodology to estimate location-specific loss-of-coolant accident frequencies for risk-informed analysis of nuclear power plants

The United States Nuclear Regulatory Commission (NRC) has promoted the use of Probabilistic Risk Assessment (PRA) in nuclear regulatory activities. Since loss-of-coolant accidents (LOCAs) are critical initiating events for many PRA applications, the NRC has taken steps towards the quantification of LOCA frequencies for use in risk-informed applications. This research develops the Spatio-Temporal Probabilistic methodology to explicitly incorporate the underlying physics of failure mechanisms into the location-specific estimation of LOCA frequencies that are required for risk-informed regulatory applications such as risk-informed resolution of generic Safety Issue 191 (GSI-191). The essence of the risk-informed resolution of GSI-191 is that location-specific LOCA frequencies drive the risk. The most recent NRC-sponsored estimations of LOCA frequencies were developed through an expert elicitation approach, provided in NUREG-1829. These estimations provided an implicit incorporation of underlying physics, space, and time. In support of the South Texas Project Nuclear Operating Company (STPNOC) risk-informed pilot project to resolve GSI-191, Fleming and Lydell developed a study which laid the groundwork for the location-specific estimations of LOCA frequencies. This research performs a critical review and a step-by-step quantitative verification of Fleming and Lydell’s methodology and, thus, two key methodological gaps are identified: (a) lack of inclusion of non-piping reactor coolant system components, and (b) lack of explicit incorporation of the underlying physics of failure that lead to the occurrence of a LOCA. To address these gaps, first, this research qualitatively examines the significance of including the contributions of non-piping components into the estimations of LOCA frequencies by conducting industry-academia evidence seeking and screening processes. Then, the Spatio-Temporal Probabilistic methodology is developed that can be used to quantitatively compare non-piping and piping components with respect to LOCA frequencies. The proposed Spatio-Temporal Probabilistic methodology also integrates the following two types of modeling: (1) The Markov modeling technique to depict the renewal processes of components’ repair due to periodic maintenance after degradations; (2) Probabilistic Physics of failure (PPoF) models to explicitly incorporate the failure mechanisms, associated with the location and age of components, into the estimation of LOCA frequencies. PPoF models integrate the underlying mechanisms related to degradation into the Markov modeling technique and, subsequently, into location-specific LOCA frequency estimations. In most of Markov models developed in this area of research, transition rates are developed using solely data-driven approaches and utilizing service data. The main problems with the Markov models with the solely data-driven transition rates are (1) inaccuracy due to insufficient data and (2) the lack of explicit connections with location-specific physics of failure mechanisms associated with transition rates. There is only one existing research that combines the Markov modeling technique with a stress-strength model of erosion corrosion for the piping components of Pressurized Heavy Water Reactors (PHWR); however due to the underlying assumptions of the methodology, this study does not adequately provide explicit incorporation of physical factors associated with locations. The Spatio-Temporal probabilistic methodology is the first research that combines the Markov technique with PPoF models for LOCA frequency estimations and, has four key tasks including: Task #1: Defining Markov States of Degradation Task #2: Modeling and Quantification of the Transition Rates of Degradation o Task # 2.1: Developing and quantifying physics of failure causal models o Task #2.2: Propagating uncertainties in the physics of failure causal models to develop Probabilistic Physics of failure (PPoF) models o Task #2.3: Calculating transition rates of degradation based on the output of Probabilistic Physics of failure (PPoF) models o Task # 2.4: Bayesian integration of the estimated transition rate from PPoF models and the ones from solely data-oriented approaches Task #3: Modeling and Quantification of the Transition Rates of Repair Task #4: Developing the Time-dependent Distributions of State Probabilities The Spatio-Temporal Probabilistic methodology provides the possibility for explicitly including the effects of location-specific causal factors, such as operating conditions (e.g., temperature, pressure, pH), maintenance quality, and material properties (e.g., yield strength and corrosion resistance) on the probability of LOCA occurrence. This methodology is beneficial, not only for estimation of location-specific LOCA frequencies, but also for incorporation of spatio-temporal physics of failure into Probabilistic Risk Assessment (PRA); therefore, it helps advance risk estimation and risk prevention. The explicit incorporation of failure mechanisms helps more accurately estimate the likelihood of LOCA occurrences, dealing with limited historical data. Additionally, the explicit incorporation of the causal factors enables the use of sensitivity analyses, which allow the physical causal factors to be ranked in order of their risk significance. Ranking of causal factors helps optimize maintenance practices by indicating the most resource-efficient methods to reduce risks. To show the feasibility, the spatio-temporal probabilistic methodology is implemented to examine the effects of Stress Corrosion Cracking (SCC) on the rupture probability of steam generator tubes. This case study demonstrates the comparative capabilities of the methodology by showing the variation in rupture probability based on the selection of Stainless Steel and Alloy 690 materials for fabrication of the expansion-transition region of the steam generator tubes. Although the tasks in this case study are explained based on SCC, which is a dominant mechanism associated with LOCA in nuclear power plants, the Spatio-Temporal Probabilistic methodology can be applied for other failure mechanisms (e.g., wear, creep) and for any high-consequence industry that deals with containment of flowing liquids or gases, such as the oil and gas industry