Reliability computation for an uncertain PVC window production system using a modified bayesian estimation

Nowadays, Industries have been receiving much attention in Failure modelling and reliability assessment of repairable systems due to the fact that it plays a crucial role in risk and safety management of process. The primary purpose of this article is to present a methodology for discussing uncertainty in the reliability assessment if the production system. In fact, we discuss the fuzzy E-Bayesian estimation of reliability for PVC window production system. This approach is used to create the fuzzy E-Bayesian estimations of system reliability by introducing and applying a theorem called “Resolution Identity” for fuzzy sets. To be more specific, the model parameters are assumed to be fuzzy random variables. For this purpose, the original problem is transformed into a nonlinear programming problem which is divided into four sub-problems to simplify the computations. Finally, the results obtained for the sub-problems can be used to determine the membership functions of the fuzzy E-Bayesian estimation of system reliability. To clarify the proposed model, a practical example for PVC window production system is conducted.

Possibilistic Approach for Travel Time Reliability Evaluation

Travel time estimation & reliability evaluation of any means of transportation in every type of travel mode- land, rail, sea and air has been of immense interest of the researchers; primarily due to growing economic concern in the field of logistics & passenger movement. In situations like quantitative data inaccessibility or data imprecision, fuzzy set based possibilistic approach is recognized as a practical choice in obtaining the reliability estimates. This paper proposes and advocates possibilistic approach for travel time reliability computation of any type transportation vehicle under fuzzy type of data. The proposed approach is a novel way of computing the travel time & obtaining the related reliability value. Initially, the paper proposes the general methodology for travel time reliability evaluation. Individual travel time components of a transportation vehicle are considered as fuzzy; as a result, travel time is modelled as a fuzzy variable. Travel time reliability of a transportation vehicle has been defined with the help of possibilistic measures. The proposed procedure is then demonstrated with an application to marine vessel carrying the bulk. After illustration of the proposed methodology, sensitivity analysis is carried out. The paper ends with the comments on comparative features of the three cases.

Snp_blup_rel: software for calculating individual animal SNP-BLUP model reliabilities

The snp_blup_rel program computes model reliabilities for genomic breeding values. The program assumes a single trait SNP-BLUP model where the breeding value can include a residual polygenic (RPG) effect. The reliability calculation requires elements of the inverse of the mixed model equations (MME). The calculation has three steps: 1) MME calculation, 2) MME coefficient matrix inversion, and 3) reliability computation. When needed, the inverted matrix can be saved after step 2. Step 3 can be used separately to new genotypes which do not contribute information to Step 2. When an RPG effect is included, an approximate method based on Monte Carlo sampling is applied. This reduces the MME matrix size and allows including many genotyped individuals. The program is written in Fortran 90/95, and uses LAPACK subroutines which enable multi-threaded parallel computing. The program is efficient in terms of computing time and memory requirements, and can be used to analyze even large genomic data. Thus, the program can be used in calculating model reliabilities for large national genomic evaluations.

Data Quality and Reliability Metrics for Event-Related Potentials (ERPs): The Utility of Subject-Level Reliability

Event-related potentials (ERPs) represent direct measures of neural activity that are leveraged to understand cognitive, affective, sensory, and motor processes. Every ERP researcher encounters the obstacle of determining whether measurements are precise enough for an intended purpose. In this primer we review three types of measurements metrics: data quality, group-level reliability, and subject-level reliability. Data quality estimates characterize the precision of ERP scores but provide no information about whether scores are precise enough for an intended comparison. Group-level reliability characterizes the ratio of between-person differences to the precision of those scores, and provides a single reliability estimate for an entire group of participants that risks masking low reliability for some individuals. Subject-level reliability considers the precision of an ERP score for a person relative to between-person differences for a group, and an estimate is yielded for each individual. We apply each metric to published error-related negativity (ERN) and reward positivity (RewP) data and demonstrate how failing to consider data quality and reliability can undermine statistical inferences. We conclude with general comments on how these estimates may be integrated into the literature to improve measure quality and methodological transparency. Subject-level reliability computation is implemented within the ERP Reliability Analysis (ERA) Toolbox.

Classical and Bayesian estimation of stress-strength reliability of a component having multiple states

PurposeThis article presents the multi-state stress-strength reliability computation of a component having three states namely, working, deteriorating and failed state.Design/methodology/approachThe probabilistic approach is used to obtain the reliability expression by considering the difference between the values of stress and strength of a component, say, for example, the stress (load) and strength of a power generating unit is in terms of megawatt. The range of values taken by the difference variable determines the various states of the component. The method of maximum likelihood and Bayesian estimation is used to obtain the estimators of the parameters and system reliability.FindingsThe maximum likelihood and Bayesian estimates of the reliability approach the actual reliability for increasing sample size.Originality/valueObtained a new expression for the multi-state stress-strength reliability of a component and the findings are positively supported by presenting the general trend of estimated values of reliability approaching the actual value of reliability.