F-22 Subsystem Fleet Management Trend Analysis

2009 ◽  
Author(s):  
Stefan M. Glista ◽  
David J. Rushing ◽  
Charles R. Lide ◽  
Patrick C. McPherson
Keyword(s):  
Learning Curve ◽  
Growth Curves ◽  
Reliability Growth ◽  
Reliability Engineering ◽  
Physics Of Failure ◽  
Durability Analysis ◽  
Distribution Shape ◽  
Proposed Model ◽  
Qualification Testing ◽  
Insight Into

Reliability growth curves are modeled using the results of Weibull distribution regression analysis. The Weibull Learning Curve (WLC) model is useful because it provides an early statistical indication of wear out issues. The vibration, thermal, humidity and corrosion environments of the F-22 are severe. It is prudent to be concerned about wearout problems in severe aircraft environments. The power of Weibull is the ability to predict the characteristic life and distribution shape with a small amount of data. The proposed model is compared to the traditional Duane Learning Curve approach. Statistical model validity is evaluated using numeric and graphical methods, including coefficients of determination (r2) and residuals. The WLC model provides insight into interdisciplinary issues, including supplier quality control, qualification testing, physics of failure, and reliability engineering. In addition, the WLC model quantifies the number of items with removal statistics consistent with wearout; therefore, this study helps to quantify successes and shortfalls of durability analysis and testing approaches.

scholarly journals Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Viscosity Data ◽  
Oil Viscosity ◽  
Pvt Data ◽  
Proposed Model ◽  
Wide Range ◽  
Engineering Problems ◽  
Functional Forms ◽  
Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

Vibration-Induced Loosening Performance of Preloaded Threaded Fasteners

2010 ◽  
Author(s):  
Xianjie Yang ◽  
Sayed Nassar
Keyword(s):  
Nonlinear Model ◽  
Excitation Amplitude ◽  
The Self ◽  
Transverse Displacement ◽  
Transverse Loading ◽  
Threaded Fasteners ◽  
Bolt Preload ◽  
Proposed Model ◽  
Number Of Cycles ◽  
Insight Into

In an effort to establish a theoretical outline of a criterion for preventing the vibration-induced loosening of preloaded threaded fasteners, this paper provides an experimental and analytical insight into the effect of the initial bolt preload and the excitation amplitude on the self loosening performance of cap screw fastener. A nonlinear model is used for predicting the clamp load loss caused by the vibration-induced loosening of cap screw fasteners under cyclic transverse loading. Experimental verification was conducted on the twisting torque variation and the effect of the preload level and transverse displacement amplitude. Comparison of the experimental and analytical results on the clamp load loss with the number of cycles verifies that the proposed model accurately predicts self-loosening performance.

Decomposition of Growth Curves into Growth Rate and Acceleration: a Novel Procedure to Monitor Bacterial Growth and the Time-Dependent Effect of Antimicrobials

2021 ◽  
Author(s):  
M. Luisa Navarro-Pérez ◽  
M. Coronada Fernández-Calderón ◽  
Virginia Vadillo-Rodríguez
Keyword(s):  
Growth Rate ◽  
Mathematical Models ◽  
Bacterial Growth ◽  
Time Course ◽  
Antimicrobial Agents ◽  
Numerical Procedure ◽  
Growth Curves ◽  
Predictive Microbiology ◽  
Insight Into ◽  
Over Time

In this paper, a simple numerical procedure is presented to monitor the growth of Streptococcus sanguinis over time in the absence and presence of propolis, a natural antimicrobial. In particular, it is shown that the real-time decomposition of growth curves obtained through optical density measurements into growth rate and acceleration can be a powerful tool to precisely assess a large range of key parameters [ i.e. lag time ( t 0 ), starting growth rate ( γ 0 ), initial acceleration of the growth ( a 0 ), maximum growth rate ( γ max ), maximum acceleration ( a max ) and deceleration ( a min ) of the growth and the total number of cells at the beginning of the saturation phase ( N s )] that can be readily used to fully describe growth over time. Consequently, the procedure presented provides precise data of the time course of the different growth phases and features, which is expected to be relevant, for instance, to thoroughly evaluate the effect of new antimicrobial agents. It further provides insight into predictive microbiology, likely having important implications to assumptions adopted in mathematical models to predict the progress of bacterial growth. Importance: The new and simple numerical procedure presented in this paper to analyze bacterial growth will possibly allow identifying true differences in efficacy among antimicrobial drugs for their applications in human health, food security, and environment, among others. It further provides insight into predictive microbiology, likely helping in the development of proper mathematical models to predict the course of bacterial growth under diverse circumstances.

Application of Physics-of-Failure Method in Reliability Engineering of Electronic Products

2013 ◽  
Vol 313-314 ◽  
pp. 697-701
Author(s):  
Jiang Shao ◽  
Chen Hui Zeng
Keyword(s):  
Reliability Engineering ◽  
Physical Processes ◽  
Developed Country ◽  
Physics Of Failure ◽  
Electronic Products ◽  
Reliability Design ◽  
Application Condition ◽  
Reliability Method ◽  
New Period ◽  
The New Period

Physics-of-Failure (PoF) represents one approach to reliability assessment based on modeling and simulation that relies on understanding the physical processes contributing to the appearance of the critical failures. Firstly the connotation and meaning of PoF method were analyzed here, the inherence relation between PoF and reliability was expatiated, the PoF based reliability method and current reliability method based on probability statistics were compared, their differences and relationships were discussed here. Then the application condition of PoF method in reliability engineering in European and American developed country and China were summarized, the PoF based reliability engineering technologies were introduced systemically from several aspects, such as reliability design and analysis, reliability test and validation, maintain and support. Finally, combining with the developing characteristics of military materiel during the new period, some future investigation directions and application foregrounds were prospected.

RELIABILITY ANALYSIS OF PROJECT AND PRODUCT TYPE SOFTWARE IN OPERATIONAL PHASE INCORPORATING THE EFFECT OF FAULT REMOVAL EFFICIENCY

2007 ◽  
Vol 14 (03) ◽  
pp. 219-240 ◽  
Author(s):  
P. K. KAPUR ◽  
ANSHU GUPTA ◽  
P. C. JHA
Keyword(s):  
Software Reliability ◽  
Product Type ◽  
Data Sets ◽  
Reliability Growth ◽  
Operational Environment ◽  
Testing Environment ◽  
Testing Phase ◽  
Proposed Model ◽  
Software Reliability Growth ◽  
Operational Phase

Since the early 1970's numerous Software Reliability Growth Models (SRGM) have been proposed in the literature to estimate the software reliability measures such as the remaining number of faults, failure rate and reliability growth during the testing phase. These models are applied to the software testing data collected during the testing phase and then are often used to predict the software failures in operational phase. In practice simulating mirror image of the diverse testing environment representative of the operational environment is difficult in practice and hence the simulated testing environment during the testing phase may not be similar to the conditions that exist in the operational phase. During testing phase testing is performed under a controlled environment whereas during the operational phase failure phenomenon depends on the operational environment and usage of software. Therefore an SRGM developed for the testing phase is not suitable for estimating the reliability growth during the operational phase. In this paper, we propose a generalized Software Reliability Growth Model, which can be used to estimate number of faults during the testing phase and can be easily extended to the operation phase. In the testing phase, it is appropriate to estimate the reliability growth with respect to the amount of testing resources spent on testing whereas in the operational phase the amount of effort to be spent on removing a fault reported by a user is fixed by the developer. The number of failures detected and hence the reliability growth during the user phase depends on the usage of software. The proposed model appropriately incorporates these changes. Further we categorize the software into two-categories- (a) project and (b) product type software. Appropriate usage functions are linked to both project and product type software. To describe the fault removal phenomenon, imperfect debugging environment is incorporated into the model building. The paper highlights an interdisciplinary mathematical modeling approach in Software Reliability Engineering and Marketing. The proposed model is validated for both phases using the software failure data sets obtained from different sources. Model describes the failure phenomenon for these data sets fairly.

scholarly journals Classical and Bayesian Inference of a Mixture of Bivariate Exponentiated Exponential Model

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Refah Alotaibi ◽  
Mervat Khalifa ◽  
Ehab M. Almetwally ◽  
Indranil Ghosh ◽  
Rezk. H.
Keyword(s):  
Exponential Model ◽  
Gaussian Copula ◽  
Estimation Methods ◽  
Model Parameters ◽  
Reliability Engineering ◽  
Bivariate Model ◽  
Conditional Moments ◽  
Bayesian Paradigm ◽  
Proposed Model ◽  
Two Parameters

Exponentiated exponential (EE) model has been used effectively in reliability, engineering, biomedical, social sciences, and other applications. In this study, we introduce a new bivariate mixture EE model with two parameters assuming two cases, independent and dependent random variables. We develop a bivariate mixture starting from two EE models assuming two cases, two independent and two dependent EE models. We study some useful statistical properties of this distribution, such as marginals and conditional distributions and product moments and conditional moments. In addition, we study a dependent case, a new mixture of the bivariate model based on EE distribution marginal with two parameters and with a bivariate Gaussian copula. Different methods of estimation for the model parameters are used both under the classical and under the Bayesian paradigm. Some simulation studies are presented to verify the performance of the estimation methods of the proposed model. To illustrate the flexibility of the proposed model, a real dataset is reanalyzed.

Application of Hybrid VIKOR Model in Selection of Maintenance Strategy

2012 ◽  
Vol 5 (2) ◽  
pp. 59-81 ◽  
Author(s):  
M. Ilangkumaran ◽  
S. Kumanan
Keyword(s):  
Textile Industry ◽  
Maintenance Strategy ◽  
Analytic Hierarchy ◽  
Proposed Model ◽  
Order Preference ◽  
Hierarchy Process ◽  
Pair Wise Comparison ◽  
Insight Into ◽  
Selection Of

This paper focuses on the use of Fuzzy Analytic Hierarchy Process (FAHP) and VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian (VIKOR) to select an optimum maintenance strategy for a textile industry. In the proposed methodology, first the weight of each criterion is calculated by using improved AHP with fuzzy set theory to overcome the problems of unbalanced scale of judgments, uncertainty and imprecision in the pair-wise comparison process and then the VIKOR method is applied to compensate the imprecise ranking of the AHP in the selection of maintenance strategy. The real case study is conducted for a textile industry to illustrate the utilization of the proposed model for the maintenance strategy selection problem. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to make sure that the result of the proposed model can be acceptable. A sensitivity analysis is also conducted to show the validity of the proposed model. The paper gives an insight into multi criteria decision-making (MCDM) techniques to select an optimum maintenance strategy for a process industry using a case study.

QR codes as a tool for receiving feedback about guests’ satisfaction at destinations

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nikola Vuksanović ◽  
Dunja Demirović Bajrami ◽  
Marko D. Petrović ◽  
Elena M. Grigorieva
Keyword(s):  
Convergent Validity ◽  
Economic Status ◽  
Qr Code ◽  
Qr Codes ◽  
Content Type ◽  
Proposed Model ◽  
Marketing Tool ◽  
The Impact ◽  
Positive Effect ◽  
Insight Into

Purpose The purpose of this study is to examine the impact of the use of Quick Response (QR) code application among the tourists on their satisfaction at a destination regarding information about restaurants’ offer. Design/methodology/approach A quantitative method was implemented in this study. The field study was conducted in 2019 in the two most visited urban destinations in Serbia. The proposed model was examined using partial least squares, and the model fits, composite reliability and convergent validity were assessed. The direction and significance of the relationships were determined by testing all of the hypotheses. Findings The results showed that there was a positive effect of using QR codes. However, the study showed that a QR code cannot completely affect the overall satisfaction at a destination. The analysis of the control variables (age and economic status) showed that individual, demographic and economic factors must be taken into consideration to predict individuals’ behaviour. Research limitations/implications To enable the generalization of the results, it is advised to conduct research on cross-cultural levels. Future studies related to the topic could be conveyed in other forms of tourism, as well as in other industries, which would provide a better insight into the application of this technology in the future. Originality/value The study enables managers of tourism businesses, especially in hospitality, to better understand the importance of the use of a QR code at a destination as an important marketing tool for getting information, and thus to satisfy guests’ expectations.

scholarly journals A two-stage heat transfer model for the peripheral layers of a grain store

2003 ◽  
Vol 7 (3) ◽  
pp. 147-164
Author(s):  
Alexsandar Antic ◽  
James M. Hill
Keyword(s):  
Heat Transfer ◽  
Present Model ◽  
Heat Transfer Model ◽  
Transfer Model ◽  
Two Stage ◽  
Peripheral Layer ◽  
Proposed Model ◽  
Grain Bulk ◽  
Insight Into ◽  
Good Agreement

An understanding of the flow of heat in grain store structures, in particular, within the peripheral layer, is important from many industrial perspectives. To analyse the heat transfer within such regions a mathematical model known as the two-stage heat transfer model is proposed. This model makes a distinction between the air and grain within the grain bulk, and thus takes into consideration the fact that the rate of heat transfer through the grain is different to that through the interstitial air surrounding the grain. Such a model lends itself to a solution via Laplace transforms and approximate analytical results are obtained for small and large times. In addition, the Stehfest numerical algorithm is used for the inversions and very good agreement is obtained between the two approaches. The present model is compared to a previously developed double-diffusivity heat transfer model by the authors, and good agreement is obtained. At present, no experimental data is available to validate the model as it is very difficult to measure the air and grain temperatures separately, particularly in the peripheral layer. The proposed model provides insight into the potential difference existing between the air and grain temperatures.

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