Academic literature on the topic 'Cumulative Logit Model'
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Journal articles on the topic "Cumulative Logit Model"
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Yin, Chang Ming, Xiao Jie Li, and Dan Fu. "Strong Consistency of Maximum Likelihood Estimators in Sequential-Cumulative Logit Model." Applied Mechanics and Materials 742 (March 2015): 445–48. http://dx.doi.org/10.4028/www.scientific.net/amm.742.445.
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In this article, for the sequential-cumulative logit model, we show that maximum likelihood estimates of regression parameter vector is asymptotically existence and strongly consistent under mild conditions
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Yan, Dongmei, and Yang Yang. "A Stochastic User Equilibrium Formulation for the Cumulative Prospect Theory-Based Cross-Nested Logit." Discrete Dynamics in Nature and Society 2021 (June 14, 2021): 1–10. http://dx.doi.org/10.1155/2021/9929015.
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The cumulative prospect theory provides a better description for route choice behavior of the travelers in an uncertain road network environment. In this study, we proposed a multiclass cumulative prospect value- (CPV-) based cross-nested logit (CNL) stochastic user equilibrium (SUE) model. For this model, an equivalent variational inequality (VI) model is provided, and the existence and equivalence of the model solutions are also proved. The method of successive averages (MSA), method of successive weighted averages (MSWA), and self-regulated averaging (SRA) method are designed and compared. In addition, the proposed multiclass CPV-based CNL SUE model is also compared with the multiclass utility value- (UV-) based CNL SUE model. The results show that the path flow assigned by the multiclass CPV-based CNL SUE model is more consistent with the actual situation. The impact of different model parameters on the cumulative prospect value (CPV) is investigated.
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Park, Sun-Sook. "A Study on the Determinants of Partner Violence Change Using Cumulative Logit Model." Correction Welfare Society of Korea 54 (June 30, 2018): 25–52. http://dx.doi.org/10.35422/cwsk.2018.54.2.
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Cappelleri, Joseph C., Stephen S. Bell, and Richard L. Siegel. "Interpretation of a Self-Esteem Subscale for Erectile Dysfunction by Cumulative Logit Model." Drug Information Journal 41, no. 6 (November 2007): 723–32. http://dx.doi.org/10.1177/009286150704100605.
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Zhang, Xuxin, Xuesong Wang, Xiaohan Yang, Chuan Xu, Xiaohui Zhu, and Jiaohua Wei. "Driver drowsiness detection using mixed-effect ordered logit model considering time cumulative effect." Analytic Methods in Accident Research 26 (June 2020): 100114. http://dx.doi.org/10.1016/j.amar.2020.100114.
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Cheng, Chad Shouquan, Guilong Li, Qian Li, and Heather Auld. "A Synoptic Weather Typing Approach to Simulate Daily Rainfall and Extremes in Ontario, Canada: Potential for Climate Change Projections." Journal of Applied Meteorology and Climatology 49, no. 5 (May 1, 2010): 845–66. http://dx.doi.org/10.1175/2010jamc2016.1.
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Abstract An automated synoptic weather typing and stepwise cumulative logit/nonlinear regression analyses were employed to simulate the occurrence and quantity of daily rainfall events. The synoptic weather typing was developed using principal component analysis, an average linkage clustering procedure, and discriminant function analysis to identify the weather types most likely to be associated with daily rainfall events for the four selected river basins in Ontario. Within-weather-type daily rainfall simulation models comprise a two-step process: (i) cumulative logit regression to predict the occurrence of daily rainfall events, and (ii) using probability of the logit regression, a nonlinear regression procedure to simulate daily rainfall quantities. The rainfall simulation models were validated using an independent dataset, and the results showed that the models were successful at replicating the occurrence and quantity of daily rainfall events. For example, the relative operating characteristics score is greater than 0.97 for rainfall events with daily rainfall ≥10 or ≥25 mm, for both model development and validation. For evaluation of daily rainfall quantity simulation models, four correctness classifications of excellent, good, fair, and poor were defined, based on the difference between daily rainfall observations and model simulations. Across four selected river basins, the percentage of excellent and good simulations for model development ranged from 62% to 84% (of 20 individuals, 16 cases ≥ 70%, 7 cases ≥ 80%); the corresponding percentage for model validation ranged from 50% to 76% (of 20 individuals, 15 cases ≥ 60%, 6 cases ≥ 70%).
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Iyit, Neslihan. "Modelling world energy security data from multinomial distribution by generalized linear model under different cumulative link functions." Open Chemistry 16, no. 1 (April 30, 2018): 377–85. http://dx.doi.org/10.1515/chem-2018-0053.
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AbstractEnergy securityis one of the major components of energy sustainability in the world’s energy performance. In this study,energy securityis taken as an ordinal response variable coming from the multinomial distribution with the energy grade levelsA,B,C, andD. Thereafter, the worldenergy securitydata is tried to be statistically modelled by usinggeneralized linear model (GLM)approach for the ordinal response variable under different cumulative link functions. The cumulative link functions comparatively used in this study are cumulative logit, cumulative probit, cumulative complementary log-log, cumulative Cauchit, and cumulative negative log-log. In order to avoid a multicollinearity problem in the data structure, principal component analysis (PCA) technique is integrated with theGLMapproach for the ordinal response variable. In this study, statistically, the importance of determining the best cumulative link function on the accuracy of parameter estimates, confidence intervals, and hypothesis tests in theGLMfor the multinomially distributed response variable is highlighted. In terms of energy evaluation, by usingcumulative logitas the best cumulative link function,energy sources consumptions,electricity productions from nuclear energy,natural gas,oil,coal,and hydroelectric,energy use per capita and energy importsare found to have statistically significant effects onenergy securityin the world’s energy performance.
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Lee, Deogro, and Heuiju Chun. "Analysis of factor of life planners' satisfaction after turnover using the cumulative logit model." Journal of the Korean Data and Information Science Society 24, no. 6 (November 30, 2013): 1369–84. http://dx.doi.org/10.7465/jkdi.2013.24.6.1369.
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Prasetyo, Rindang Bangun, Heri Kuswanto, Nur Iriawan, and Brodjol Sutijo Suprih Ulama. "Binomial Regression Models with a Flexible Generalized Logit Link Function." Symmetry 12, no. 2 (February 2, 2020): 221. http://dx.doi.org/10.3390/sym12020221.
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In binomial regression, a link function is used to join the linear predictor variables and the expectation of the response variable. This paper proposes a flexible link function from a new class of generalized logistic distribution, namely a flexible generalized logit (glogit) link. This approach considers both symmetric and asymmetric models, including the cases of lighter and heavier tails, as compared to standard logistic. The glogit is created from the inverse cumulative distribution function of the exponentiated-exponential logistic (EEL) distribution. Using a Bayesian framework, we conduct a simulation study to investigate the model performance compared to the most commonly used link functions, e.g., logit, probit, and complementary log–log. Furthermore, we compared the proposed model with several other asymmetric models using two previously published datasets. The results show that the proposed model outperforms the existing ones and provides flexibility fitting the experimental dataset. Another attractive aspect of the model are analytically tractable and can be easily implemented under a Bayesian approach.
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Indriany, Sylvia, Ade Sjafruddin, Aine Kusumawati, and Widyarini Weningtyas. "Identification of cumulative prospect theory parameters for mode choice model." MATEC Web of Conferences 270 (2019): 03012. http://dx.doi.org/10.1051/matecconf/201927003012.
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The use of Cumulative Prospect Theory (CPT) in decision making related to transportation risk is still much debated. Mainly because of the travel and socio-economic characteristics of the traveller it possible for different responses to the specified Reference Point (RP) as well as the loss aversion. This difference can be seen from the value of Cumulative Prospect Theory parameters. Therefore, this paper will discuss about the determination of parameters CPT which affect public transportation mode choice model in the course of work trip activity. The reference point as an essential part of this study is determined based on the average travel time of commuter worker from South Tangerang City to Jakarta. Data obtained from stated preference survey, Feeder Busway/Busway and Commuter Line Jabodetabek as mode alternative and travel time attribute as a risk factor. The Binomial Logit model which has transformed utility distribution and probability with CPT and the Least Square Method to be obtained the parameters. Finally, some conclusions can be drawn that the CPT parameters produced by this study, have closed the range of value requirements in the CPT theory. So that the parameter value can be used to model the probability of mode choice with the risk of travel time in the study area.
Dissertations / Theses on the topic "Cumulative Logit Model"
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Alzubaidi, Samirah Hamid. "A case study on cumulative logit models with low frequency and mixed effects." Kansas State University, 2017. http://hdl.handle.net/2097/38252.
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Master of ScienceDepartment of Statistics
Perla E. Reyes Cuellar
Data with ordinal responses may be encountered in many research fields, such as social, medical, agriculture or financial sciences. In this paper, we present a case study on cumulative logit models with low frequency and mixed effects and discuss some strengths and limitations of the current methodology. Two plant pathologists requested our statistical advice to fit a cumulative logit mixed model seeking for the effect of six commercial products on the control of a seed and seedling disease in soybeans in vitro. In their attempt to estimate the model parameters using a generalized linear mixed model approach with PROC GLIMMIX, the model failed to converge. Three alternative approaches to solve the problem were examined: 1) stratifying the data searching for the random effect; 2) assuming the random effect would be small and reducing the model to a fixed model; and 3) combining the original categories of the response variable to a lower number of categories. In addition, we conducted a power analysis to evaluate the required sample size to detect treatment differences. The results of all the proposed solutions were similar. Collapsing categories for a cumulative/proportional odds model has little effect on estimation. The sample size used in the case study is enough to detect a large shift of frequencies between categories, but not for moderated changes. Moreover, we do not have enough information to estimate a random effect. Even when it is present, the results regarding the fixed factors: pathogen, evaluation day, and treatment effects are the same as the obtained by the fixed model alternatives. All six products had a significant effect in slowing the effect of the pathogen, but the effects vary between pathogen species and assessment timing or date.
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Fatoretto, Maíra Blumer. "Modelos para dados categorizados ordinais com efeito aleatório: uma aplicação à análise sensorial." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-16032016-170135/.
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Os modelos para dados categorizados ordinais são extensões dos Modelos Lineares Generalizados e suas suposições e inferências são fundamentadas por esta classe de modelos. Os Modelos de Logitos Cumulativos, em que a função de ligação é constituída de probabilidades acumuladas, são muito utilizados para este tipo de variável, sendo uma de suas simplificações, os Modelos de Chances Proporcionais, em que para todas as covaríaveis no modelo há um crescimento linear nas razões de chances, porém, neste caso, é necessária a verificação da suposição de paralelismo. Outros modelos como o Modelo de Chances Proporcionais Parciais, o Modelo de Categorias Adjacentes e o Modelo Logito de Razão Contínua também podem ser utilizados. Em diversos estudos deste tipo, é necessário a utilização de modelos mistos, seja pelo tipo de um fator ou a dependência entre observações da variável resposta. Objetivou-se, neste trabalho, o estudo de modelos para variável resposta ordinal com a inclusão de um ou mais efeitos aleatórios. Esses modelos são ilustrados com a utilização de dados reais de análise sensorial, cuja variável resposta é constituída de uma escala ordinal e deseja-se saber dentre duas variedades de tomates desidratados (Italiano e Sweet Grape), qual teve melhor aceitação pelos consumidores. Nesse experimento os provadores avaliaram uma única vez cada uma das variedades, sendo as repetições constituídas pelas avaliações dadas por diferentes provadores. Nesse caso, é necessária a inclusão de um efeito aleatório por provador, para que o modelo consiga capturar as diferenças entre esses provadores não treinados. O Modelo de Chances Proporcionais ajustou-se de maneira satisfatória aos dados, podendo-se fazer uso das estimativas de probabilidades e razões de chances para a interpretação dos resultados e concluindo-se que o sabor da variedade Sweet Grape foi o que mais agradou os provadores, independente do sexo.Models for ordinal categorical data are extensions of the Generalized Linear Models and their assumptions and inferences are based on this class of models. The Cumulative Logit Models in wich the link function consists of accumulated probabilities are more used for this type of variable, with one of its simplifications are the Proportional Odds Model, in wich for all covariates in the model there is a linear growth in odds ratios, but in this case, checking the parallelism assumption is required. Other models such as the Partial Proportional Odds Model, the Adjacent-Categories Logits and Continuation-Ratio Logits model can also be used. In several of such studies, the use of mixed models is required, either by type of factor or dependence between the response variable observations. The aim of this work is studying models for ordinal variable response with the inclusion of one or more random effects. These models are illustrated by using real data of sensory analysis, the response variable consists of an ordinal scale and we want to know from two varieties of dried tomatoes, Italian and Sweet Grape, which had better acceptance by consumers. In this experiment, the panelists evaluated each variety once, and the repetitions constituted by the ratings given by different tasters. In this case, the inclusion of a random effect by taster is required so that the model can capture the difference between these untrained tasters. The Proportional Odds Model fitted satisfactorily to the data and it is possible to make use of the estimates of probabilities and odds ratios for the interpretation of results and concluding that the taste of the variety Sweet Grape was the one that most pleased the tasters regardless of sex.
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Beamer, Paloma. "Development of a model to estimate aggregate and cumulative exposure and dose in young children /." May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.
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Haddadian, Rojiar. "Simulation-based estimation in regression models with categorical response variable and mismeasured covariates." 2016. http://hdl.handle.net/1993/31535.
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A common problem in regression analysis is that some covariates are measured with errors. In this dissertation we present simulation-based approach to estimation in two popular regression models with a categorical response variable and classical measurement errors in covariates. The first model is the regression model with a binary response variable. The second one is the proportional odds regression with an ordinal response variable.
In both regression models we consider method of moments estimators for therein unknown parameters that are defined via minimizing respective objective functions. The later functions involve multiple integrals and make obtaining of such estimators unfeasible. To overcome this computational difficulty, we propose Simulation-Based Estimators (SBE). This method does not require parametric assumptions for the distributions of the unobserved covariates and error components. We prove consistency and asymptotic normality of the proposed SBE's under some regularity conditions. We also examine the performance of the SBE's in finite-sample situations through simulation studies and two real data sets: the data set from the AIDS Clinical Trial Group (ACTG175) study for our logistic and probit regression models and one from the Adult Literacy and Life Skills (ALL) Survey for our regression model with the ordinal response variable and mismeasured covariates.October 2016
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Younis, Rizwan. "Development of Wastewater Collection Network Asset Database, Deterioration Models and Management Framework." Thesis, 2010. http://hdl.handle.net/10012/5287.
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The dynamics around managing urban infrastructure are changing dramatically. Today’s infrastructure management challenges – in the wake of shrinking coffers and stricter stakeholders’ requirements – include finding better condition assessment tools and prediction models, and effective and intelligent use of hard-earn data to ensure the sustainability of urban infrastructure systems. Wastewater collection networks – an important and critical component of urban infrastructure – have been neglected, and as a result, municipalities in North America and other parts of the world have accrued significant liabilities and infrastructure deficits. To reduce cost of ownership, to cope with heighten accountability, and to provide reliable and sustainable service, these systems need to be managed in an effective and intelligent manner.
The overall objective of this research is to present a new strategic management framework and related tools to support multi-perspective maintenance, rehabilitation and replacement (M, R&R) planning for wastewater collection networks. The principal objectives of this research include:
(1) Developing a comprehensive wastewater collection network asset database consisting of high quality condition assessment data to support the work presented in this thesis, as well as, the future research in this area.
(2) Proposing a framework and related system to aggregate heterogeneous data from municipal wastewater collection networks to develop better understanding of their historical and future performance.
(3) Developing statistical models to understand the deterioration of wastewater pipelines.
(4) To investigate how strategic management principles and theories can be applied to effectively manage wastewater collection networks, and propose a new management framework and related system.
(5) Demonstrating the application of strategic management framework and economic principles along with the proposed deterioration model to develop long-term financial sustainability plans for wastewater collection networks.
A relational database application, WatBAMS (Waterloo Buried Asset Management System), consisting of high quality data from the City of Niagara Falls wastewater collection system is developed. The wastewater pipelines’ inspections were completed using a relatively new Side Scanner and Evaluation Technology camera that has advantages over the traditional Closed Circuit Television cameras. Appropriate quality assurance and quality control procedures were developed and adopted to capture, store and analyze the condition assessment data. To aggregate heterogeneous data from municipal wastewater collection systems, a data integration framework based on data warehousing approach is proposed. A prototype application, BAMS (Buried Asset Management System), based on XML technologies and specifications shows implementation of the proposed framework. Using wastewater pipelines condition assessment data from the City of Niagara Falls wastewater collection network, the limitations of ordinary and binary logistic regression methodologies for deterioration modeling of wastewater pipelines are demonstrated. Two new empirical models based on ordinal regression modeling technique are proposed. A new multi-perspective – that is, operational/technical, social/political, regulatory, and finance – strategic management framework based on modified balanced-scorecard model is developed. The proposed framework is based on the findings of the first Canadian National Asset Management workshop held in Hamilton, Ontario in 2007. The application of balanced-scorecard model along with additional management tools, such as strategy maps, dashboard reports and business intelligence applications, is presented using data from the City of Niagara Falls. Using economic principles and example management scenarios, application of Monte Carlo simulation technique along with the proposed deterioration model is presented to forecast financial requirements for long-term M, R&R plans for wastewater collection networks.
A myriad of asset management systems and frameworks were found for transportation infrastructure. However, to date few efforts have been concentrated on understanding the performance behaviour of wastewater collection systems, and developing effective and intelligent M, R&R strategies. Incomplete inventories, and scarcity and poor quality of existing datasets on wastewater collection systems were found to be critical and limiting issues in conducting research in this field. It was found that the existing deterioration models either violated model assumptions or assumptions could not be verified due to limited and questionable quality data. The degradation of Reinforced Concrete pipes was found to be affected by age, whereas, for Vitrified Clay pipes, the degradation was not age dependent. The results of financial simulation model show that the City of Niagara Falls can save millions of dollars, in the long-term, by following a pro-active M, R&R strategy.
The work presented in this thesis provides an insight into how an effective and intelligent management system can be developed for wastewater collection networks. The proposed framework and related system will lead to the sustainability of wastewater collection networks and assist municipal public works departments to proactively manage their wastewater collection networks.
Book chapters on the topic "Cumulative Logit Model"
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"Cumulative Logit Model." In Encyclopedia of Quality of Life and Well-Being Research, 1408. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_100852.
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"Logistic Regression Models Using Cumulative Logits." In Analysis of Ordinal Categorical Data, 44–87. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9780470594001.ch3.
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Comfort, Louise K. "The Logic of Resilience." In The Dynamics of Risk, 235–52. Princeton University Press, 2019. http://dx.doi.org/10.23943/princeton/9780691165370.003.0010.
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This concluding chapter presents a conceptual model for adaptation, learning, and resilience in addressing the global problem of seismic risk and outlines a series of next steps for continuing the cumulative inquiry essential to managing seismic risk. To withstand shocks, a system needs to integrate internal components that perform key functions in an altered disaster environment as it interacts simultaneously with external actors to solicit needed resources to achieve the overall system goal of maintaining continuity of operations. Throughout this analysis, the organizations that engage in disaster operations have been identified by two key characteristics: jurisdiction of legal authority and source of funding. Funding and jurisdictional authority are essential to build collaborative action in rapidly changing environments, but these attributes almost always require adaptation from daily, routine tasks that are specified to meet explicit standards of accountability and control. Coherence reveals the extent to which organizations with diverse characteristics of funding and jurisdictional authority are integrated within the operational system, enabling the system to adapt its performance to sudden disruptions of routine procedures and provide an effective response to the external shock of a major earthquake.
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Getz, Donald. "Theory." In Event Impact Assessment. Goodfellow Publishers, 2018. http://dx.doi.org/10.23912/978-1-911635-03-1-4039.
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Outcomes theory incorporates a systems approach to planning, and builds evaluation and impact assessment into the management process. It corresponds with the approach taken in the companion book Event Evaluation and particularly with the Event Compass as a comprehensive approach to planning and evaluation. To put it into IA practice, a logic model or theory of change model is required. The nature of evidence is then considered. It is of critical importance when it comes to measurement and the use of indicators, as the question of “what constitutes acceptable evidence?” will frequently arise in the undertaking and interpretation of impact assessments. The forces-pressures-state-impacts-response model (FPSIR) is then presented. It provides a cyclical framework in which specific types of impact can be addressed by examining general forces and more specific trends that lead to pressures on the environment, economy or society. Specific impacts can then be viewed within a context that examines causes, followed by consideration of how people and systems respond to impacts. The chapter ends with another planning model, Limits of Acceptable Change, which introduces several interrelated concepts that impact assessors need to be familiar with: capacity; tipping points; cumulative impacts; risk and uncertainty; precautionary principle. Why planning models? Evaluation and impact assessment are seldom if ever conducted without reference to plans, strategies or policies. The results have to be used in practice, plus some contribution to theory is always possible. When goals are specified and indicators determined in advance, evaluators and impact assessors know what they are looking for.
Conference papers on the topic "Cumulative Logit Model"
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Yizhen Hai, Kwok-Leung Tsui, and Ming J. Zuo. "Gear crack level classification based on multinomial logit model and cumulative link model." In 2012 Prognostics and System Health Management Conference (PHM). IEEE, 2012. http://dx.doi.org/10.1109/phm.2012.6228904.
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Ou, Mingdong, Nan Li, Shenghuo Zhu, and Rong Jin. "Multinomial Logit Bandit with Linear Utility Functions." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/361.
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Multinomial logit bandit is a sequential subset selection problem which arises in many applications. In each round, the player selects a K-cardinality subset from N candidate items, and receives a reward which is governed by a multinomial logit (MNL) choice model considering both item utility and substitution property among items. The player's objective is to dynamically learn the parameters of MNL model and maximize cumulative reward over a finite horizon T. This problem faces the exploration-exploitation dilemma, and the involved combinatorial nature makes it non-trivial. In recent years, there have developed some algorithms by exploiting specific characteristics of the MNL model, but all of them estimate the parameters of MNL model separately and incur a regret bound which is not preferred for large candidate set size N. In this paper, we consider the linear utility MNL choice model whose item utilities are represented as linear functions of d-dimension item features, and propose an algorithm, titled LUMB, to exploit the underlying structure. It is proven that the proposed algorithm achieves regret which is free of candidate set size. Experiments show the superiority of the proposed algorithm.
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Alzbutas, Robertas. "Risk-Informed Decisions Optimization in Inspection and Maintenance." In 10th International Conference on Nuclear Engineering. ASMEDC, 2002. http://dx.doi.org/10.1115/icone10-22415.
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The Risk-Informed Approach (RIA) used to support decisions related to inspection and maintenance program is considered. The use of risk-informed methods can help focus the adequate in-service inspections and control on the more important locations of complex dynamic systems. The focus is set on the highest risk measured as conditional core damage frequency, which is produced by the frequencies of degradation and final failure at different locations combined with the conditional failure consequence probability. The probabilities of different degradation states per year and consequences are estimated quantitatively. The investigation of inspection and maintenance process is presented as the combination of deterministic and probabilistic analysis based on general risk-informed model, which includes the inspection and maintenance program features. Such RIA allows an optimization of inspection program while maintaining probabilistic and fundamental deterministic safety requirements. The failure statistics analysis is used as well as the evaluation of reliability of inspections. The assumptions regarding the effectiveness of the inspection methods are based on a classification of the accessibility of the welds during the inspection and on the different techniques used for inspection. The probability of defect detection is assumed to depend on the parameters either through logarithmic or logit transformation. As example the modeling of the pipe systems inspection process is analyzed. The means to reduce a number of inspection sites and the cumulative radiation exposure to the NPP inspection personnel with a reduction of overall risk is presented together with used and developed software. The developed software can perform and administrate all the risk evaluations and ensure the possibilities to compare different options and perform sensitivity analysis. The approaches to define an acceptable level of risk are discussed. These approaches with appropriate software in partial case is used for the construction and research of the models related to inspections and maintenance planning of Ignalina Nuclear Power Plant (RBMK-1500) piping components. The discussed example is related to risk analysis and inspection program improvements for selected pipe systems. The new risk-informed inspection and maintenance program for selected pipe systems are compared with various alternatives. The usage of risk evaluations to optimize the selection of inspection locations, the inspection interval, and the changes in risk and cost due suggested modifications are demonstrated. The proposed integrated modeling methodic and general model of inspection process can be used as a base for other risk-informed models of inspection process control and risk monitors of complex dynamic systems.
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Hong, Jie, Xuewen Miao, Lei Han, and Yanhong Ma. "Prognostics Model for Predicting Aero-Engine Bearing Grade-Life." In ASME Turbo Expo 2009: Power for Land, Sea, and Air. ASMEDC, 2009. http://dx.doi.org/10.1115/gt2009-59641.
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Development of practical and verifiable prognostic approaches for service life of gas turbine engine bearings will play a critical role in improving the reliability and safety of aircraft engines. Upgrading current military aeroengine overhaul metrics based strictly on engine flight hours and total accumulated cycles with higher fidelity prognostic models will provide an opportunity to prevent failures caused by accelerated degradation due to operation in unusually harsh conditions, and will help avoid unnecessary maintenance caused by routine check on engines that operate under nominal operating conditions. Grade-life (GL) is used to describe the bearing’s service life, and a comprehensive engine bearing prognostic model comprised of a physics based mathematical model and a prognostic element is presented in this paper. The mathematical model utilizes information from the Sensed Data module to calculate the cumulative damage sustained by the bearing since it was first installed and the Prediction Grade-life (PGL) is captured. The prognostic estimate model is an empirical lifetime model, in which Empirical Grade-life (EGL) is assessed based on vibration signals intelligently. The final Grade-life of bearings is determined by fusion of analytic Grade-life (PGL) and Grade-life assessment value (EGL) based on Fuzzy Logic Inference, which reduces the uncertainty’s affection towards prediction results of the analytic model. Finally, bearing test stand run-to-failure data is used to verify the approach.
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Lipowsky, Holger, Stephan Staudacher, Michael Bauer, and Klaus-Juergen Schmidt. "Application of Bayesian Forecasting to Change Detection and Prognosis of Gas Turbine Performance." In ASME Turbo Expo 2009: Power for Land, Sea, and Air. ASMEDC, 2009. http://dx.doi.org/10.1115/gt2009-59447.
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This paper presents a novel technique for automatic change detection of the performance of gas turbines. In addition to change detection the proposed technique has the ability to perform a prognosis of measurement values. The proposed technique is deemed to be new in the field of gas turbine monitoring and forms the basic building block of a patent pending filed by the authors [1]. The technique used is called Bayesian Forecasting and is applied to Dynamic Linear Models (DLMs). The idea of Bayesian Forecasting is based on Bayes’ Theorem, which enables the calculation of conditional probabilities. In combination with DLMs (which break down the chronological sequence of the observed parameter into mathematical components like value, gradient, etc.) Bayesian Forecasting can be used to calculate probability density functions prior to the next observation, so called forecast distributions. The change detection is carried out by comparing the current model with an alternative model which mean value is shifted by a prescribed offset. If the forecast distribution of the alternative model better fits the actual observation, a potential change is detected. To determine whether the respective observation is a single outlier or the first observation of a significant change, a special logic is developed. Studies have shown that a confident change detection is possible for a change height of only 1.5 times the standard deviation of the observed signal. In terms of prognostic abilities the proposed technique not only estimates the point of time of a potential limit exceedance of respective parameters, but also calculates confidence bounds as well as probability density and cumulative distribution functions for the prognosis.