scholarly journals A Fuzzy Reliability Model of Blades to Avoid Resonance and Its Convergence Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Meng Zhang ◽  
Shan Lu ◽  
Bo Li
Keyword(s):  
Fuzzy Random Variable ◽  
Random Variable ◽  
Reliability Model ◽  
Fuzzy Reliability ◽  
Good Convergence ◽  
Proposed Model ◽  
Inherent Frequency ◽  
Cut Set ◽  
Fuzzy Degree ◽  
Fuzzy Random

The inherent frequencies of blades, especially with friction structure of shroud, are related to many uncertain factors, which include not only random factors but also fuzzy or other uncertain factors. In this paper, the fuzzy reliability model of blades to avoid resonance is investigated. By regarding the inherent frequency as a fuzzy random variable with trapezoidal or flat-normal membership function, a fuzzy reliability model of blades to avoid resonance is proposed based on the fuzzy cut-set theory, and the corresponding numerical solution is given. Further, a theorem is proposed and proved to indicate the deficiency of the previous cut-set distributions on convergence, and, then, a new cut-set distribution is given to cover this shortage and ensure that the model is of good convergence. Finally, the proposed model is applied to evaluate the reliability of one certain blade. Some simulations are carried out to compare the new cut-set distribution with the previous ones and study the influence of fuzzy degree on the reliability.

The Compatibility Assessment between Voltage Sags and Equipment Tolerance Based on Fuzzy-Random Method

2012 ◽  
Vol 588-589 ◽  
pp. 458-462
Author(s):  
Zhi Jian Yuan ◽  
Yan Li
Keyword(s):  
Engineering Application ◽  
Fuzzy Random Variable ◽  
Random Variable ◽  
Assessment Model ◽  
Voltage Sags ◽  
Equipment Failure ◽  
Failure Event ◽  
Cut Set ◽  
The Impact ◽  
Fuzzy Random

The impact of voltage sags on equipment is usually described by equipment failure probability.It is generally difficult to assess and predict the probability because of the uncertainty of both the nature of voltage sags and the VTL (VTL) of equipment. By defining the equipment failure event caused by voltage sags as a fuzzy-random event, a fuzzy-random assessment model incorporating those uncertainty is developed. The model is able to convert the probability problem of a fuzzy-random variable to that of a common random variable by using λ-cut set. It is thus valuable in theoretical analysis and engineering application. The validity of the developed model is verified by Monte Carlo stochastic simulation using personal computers (PCs)as test equipment.

A FUZZY RELIABILITY ANALYSIS BASED ON THE TRANSFORMATION BETWEEN DISCRETE FUZZY VARIABLES AND DISCRETE RANDOM VARIABLES

2006 ◽  
Vol 13 (01) ◽  
pp. 25-35 ◽  
Author(s):  
YUGE DONG ◽  
AINAN WANG
Keyword(s):  
Reliability Analysis ◽  
Fuzzy Variable ◽  
Fuzzy Random Variable ◽  
Random Variable ◽  
Fuzzy Information ◽  
Discrete Random Variable ◽  
Fuzzy Reliability ◽  
Fuzzy Variables ◽  
Discrete Random Variables ◽  
Fuzzy Random

When fuzzy information is taken into consideration in design, it is difficult to analyze the reliability of machine parts because we usually must deal with random information and fuzzy information simultaneously. Therefore, in order to make it easy to analyze fuzzy reliability, this paper proposes the transformation between discrete fuzzy random variable and discrete random variable based on a fuzzy reliability analysis when one of the stress and strength is a discrete fuzzy variable and the other is a discrete random variable. The transformation idea put forwards in this paper can be extended to continuous case, and can also be used in the fuzzy reliability analysis of repairable system.

Multi-Attribute Decision Making in Contractor Selection Under Hybrid Uncertainty

2011 ◽  
Vol 15 (4) ◽  
pp. 465-472 ◽  
Author(s):  
Arbaiy Nureize ◽  
◽  
Junzo Watada ◽  
Keyword(s):  
Construction Industry ◽  
Fuzzy Random Variable ◽  
Random Variable ◽  
Random Regression ◽  
Contractor Selection ◽  
Random Regression Model ◽  
Proposed Model ◽  
Multi Attribute Decision Making ◽  
Hybrid Uncertainty ◽  
Fuzzy Random

The successful of a construction industry project depends on contractor evaluation and selection. Further, human judgment and unknown evaluation risk make evaluation and selection increasingly complex. Such situations show that a contractor selection is influenced by multiple attributes that often have the hybrid uncertainty of fuzziness and probability. The objective of this study is therefore to propose a fuzzy random variable based multi-attribute decision scheme that enables us to solve such problems within the bounds of hybrid uncertainty by using a fuzzy random regression model. The proposed model is explained in examples and its usefulness is clarified. This decision model is facilitated in its use by evaluating alternatives and enables us to indicate the optimum choice in the presence of hybrid uncertainty.

A New Cut-Set Distribution in Fuzzy Reliability Models of Mechanical Structures

2014 ◽  
Vol 933 ◽  
pp. 239-244 ◽  
Author(s):  
Meng Zhang ◽  
Shan Lu
Keyword(s):  
Normal Distribution ◽  
Resonance Problem ◽  
Reliability Model ◽  
Truncated Normal Distribution ◽  
Fuzzy Reliability ◽  
Linear Distribution ◽  
Good Convergence ◽  
Reliability Models ◽  
Cut Set ◽  
Truncated Normal

The mechanical fuzzy reliability models based on cut-set method have been applied to many fields in engineering. The reliability analysis results are affected by the cut-set distribution. In this paper, a basic mechanical fuzzy reliability model using cut-set method was established. Based on this model, the deficiency of the three previous cut-set distributions, i.e. uniform distribution, linear distribution and truncated normal distribution, was analyzed, and a new cut-set distribution named modified truncated normal distribution was proposed. The analysis results showed that when the cut-set is long enough, the reliabilities of the fuzzy models with the three previous cut-set distributions tend to a fixed value which has nothing to do with the specific mechanical structures, which makes the models have no good convergence and unreasonable. While, the fuzzy reliability model with the new cut-set distribution proposed in this paper can converge well to the classical random reliability model. Moreover, the reliabilities of a stress-strength interference problem and a resonance problem were assessed as two examples to verify the theoretical analysis results, which indicate that with this new cut-set distribution, the fuzzy reliability models have better convergence and wider applications.

scholarly journals On Distribution Characteristics of a Fuzzy Random Variable

2018 ◽  
Vol 47 (2) ◽  
pp. 53-67 ◽  
Author(s):  
Jalal Chachi
Keyword(s):  
Probability Theory ◽  
Distribution Function ◽  
Cumulative Distribution Function ◽  
Random Variables ◽  
Fuzzy Random Variable ◽  
Random Variable ◽  
Cumulative Distribution ◽  
Distribution Characteristics ◽  
Fuzzy Random Variables ◽  
Fuzzy Random

In this paper, rst a new notion of fuzzy random variables is introduced. Then, usingclassical techniques in Probability Theory, some aspects and results associated to a randomvariable (including expectation, variance, covariance, correlation coecient, etc.) will beextended to this new environment. Furthermore, within this framework, we can use thetools of general Probability Theory to dene fuzzy cumulative distribution function of afuzzy random variable.

scholarly journals On the Generalization Performance of a Regression Model with Imprecise Elements

2017 ◽  
Vol 25 (05) ◽  
pp. 723-740 ◽  
Author(s):  
Maria Brigida Ferraro
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Prediction Error ◽  
Random Element ◽  
Fuzzy Random Variable ◽  
Random Variable ◽  
Generalization Performance ◽  
Bootstrap Procedure ◽  
Link Functions ◽  
Fuzzy Random

A linear regression model for imprecise random variables is considered. The imprecision of a random element has been formalized by means of the LR fuzzy random variable, characterized by a center, a left and a right spread. In order to avoid the non-negativity conditions the spreads are transformed by means of two invertible functions. To analyze the generalization performance of that model an appropriate prediction error is introduced, and it is estimated by means of a bootstrap procedure. Furthermore, since the choice of response transformations could affect the inferential procedures, a computational proposal is introduced for choosing from a family of parametric link functions, the Box-Cox family, the transformation parameters that minimize the prediction error of the model.

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