Since college/university students are one of the most active netizen groups, higher education institutions become one of "storm eyes" of online public opinions. Suddenly-happened group events often cause uproar online. If they are not controlled immediately, they may damage the image of higher education institutions, have an impact on normal education activities, or even disrupt normal social orders. Therefore, strengthening the research of higher education institution-related online public opinions, attaching importance to the analysis and guide of campus online public opinions, and building a synergetic cooperation mechanism for higher education institution-related online public opinions will have a positive effect on the cyberculture in higher education institutions or even social influence. Combining theoretical research with systematic research and empirical research, this paper firstly introduces several concepts, including public opinions, online public opinions, and higher education institution-related online public opinions. Then the formation and cyclical evolution of higher education institution-related online public opinions are analyzed. Subsequently, the reasons higher education institution-related online public opinions shape up are analyzed from psychological, publicity, and pedagogical perspectives. Finally, the strategies for guiding higher education institution-related online public opinions are puts forward, i.e. (1) to build a synergetic cooperation mechanism for online public opinions; and (2) to integrate media literacy education into ideological and political education.

The traditional fatigue life prediction methods based on the S-N curve recognize the parameters in life prediction models can be determined by experiment, simulation and fitting. That is to say, in a given loading environment, various parameters are treated as different constants, and then substitute these parameters into the model, the fatigue life can be obtained. This kind of methods treats the parameters in the model as constants, which belong to deterministic life prediction method. Deterministic life prediction method is suitable for fatigue life prediction and evaluation where the test data is sufficient. However, in practice, because of the restrictions on development cycles and there may be a shortage of funds, combined with technical difficulties, it has great difficulty to carry out a large number of trials. At the same time, due to different test specimens, along with test operations and data reading depend on the accuracy of test equipment and subjective judgment of test personnel, which increases the uncertainty of the fatigue life. S-N curve is the basic parameter of the material or structure life prediction and it is gained mainly through a large number of fatigue tests and fitting analyses. Due to the influence of uncertainty factors, which results in dispersion of fatigue life under constant load in different degrees, so as to the uncertainty of S-N curve. Therefore, in order to keep consistent with the actual situation of the life prediction, taking the uncertainty of S-N curve into account is needed. This article summarizes the uncertainty factors that affect fatigue life of welded joint, the polynomial chaos theory is introduced into the fatigue life prediction, a welded joint cumulative damage model considering the uncertainty S-N curve and probabilistic fatigue life prediction method is built combined with nonlinear cumulative damage model.

Subjective factors and nonlinear characteristics, inherent in the importance identification for a fault tree in the reliability and risk analysis, make it necessary for fuzzy or possibilistic approaches to accommodate the quantificational assessment of epistemic uncertainty in a practical problem when data and information are very limited. After investigating the intuitive interpretations, possibilistic information semantics, measure-theoretic terms and entropy-like models, a new axiomatic index of importance measure for fault trees is proposed based upon possibilistic information entropy, which adopts the possibilistic assumption in place of the probabilistic one. An example of the fault tree is provided along with the concordance analysis and other discussions. The more conservative numerical results of importance rankings that involve more choices could be viewed as “soft” fault identification under a certain expected value. Finally, possible extension to the evidence space and further research directions are discussed.

Due to the discreteness of system state, it is difficult to obtain the system state possibility distribution, thus the possibility theory based system reliability analysis stays at an elementary stage. To deal with the system state possibility distribution and more accurately analyze the system possibilistic reliability, state corresponding most possible residual lifetime (MPRL) is introduced to express the internal functional relationship between system state and residual lifetime. The MPRL is defined as the system most possible remaining lifetime under this state, which connects the system state possibility distribution with system lifetime possibility distribution. The variable at investigating moment is introduced to link the system state and corresponding most possible residual lifetime, then the possibilistic reliability function of multi-state system is redefined. The system possibilistic reliability analysis can be realized with system lifetime possibility distribution instead of state possibility distribution.

Within the practical problems in industrial engineering, the failure effect sometimes can be omitted or delayed if it has less effect on the system. In detail, the prominent features of the system can be described as follows: 1) if a repair time is sufficiently short (less than some threshold value) that does not affect the system operation, i.e. the pessimistic effect of system failure could be ignored. The system can be considered as operating during this repair time. It is called the system with repair time omission (failure effect omitted). 2) if a repair time is longer than the given threshold value and the failure effect is finally suffered. Then the system can be considered to remain operating from the initial stage of the repair till the end of the repair threshold. It is called the system with delayed failure effect. Based on the above two characteristics, model for the related repairable system is introduced in this paper. Two scenarios are discussed where the threshold value is regarded as a constant and non-negative random variable, respectively. Reliability indices such as instantaneous possibilistic availability are formulated for the system with failure effect omitted or delayed.

Possibilistic reliability theory is the study of reliability based on the principles of possibility theory. In this paper, the possibilistic reliability function of multi-state system is redefined and analyzed utilizing the possibility distribution of system lifetime. In order to express the internal relationship between system state and residual lifetime, state corresponding most possible residual lifetime (MPRL) is defined as the system's most possible remaining lifetime under the state. Thus, addressing the system reliable level as keeping a specified state at a specified moment, the system possibilistic reliability can be obtained through the functional relationship between system state and the corresponding MPRL. As a result, a numerical example is developed to illustrate the theory.

As the end point of the power system, a distribution system directly connects with the consumers. The reliability of power supply is directly related with consumers. However, uncertainty of the original parameters of components may be caused by lacking of statistical information and the statistic error. It will inevitably produce large errors if we directly use these parameters for evaluation. The algorithm of interval numbers is used in this paper and the original reliability parameters are treated as interval numbers. Thereby the uncertainty of the parameters can be considered in the whole process of the reliability evaluation. At the same time the reliability interval indicator can be obtained.

Nondeterministic variables of certain distributions are employed to represent uncertainties, which are usually treated as the stochastic factors to reliability models. However, model parameters may not be precisely represented due to some factors in engineering practices, such as lack of sufficient data, data with fuzziness and unknown or non-constant reproduction conditions. To address these issues, fuzzy random variables are implemented and two developments are made in this paper. The first development is that the Saddlepoint Approximation (SAP)-simulation is extended to conduct reliability analysis accounting for the time-dependent degradation process and fuzzy random variables, and we attempt to give a method to select a proper saddlepoint. The second development is that two system reliability analysis methods are proposed for different scenarios of reliability modeling processes. It could be suitable for the system consisting of structural components with gradual failure, whose reliability can be obtained by the method in the improved SPA-simulation, also for system consisting of components with sudden failure, whose reliability can be acquired from site field or experiments. An illustrated example is followed to testify the proposed methods.

Within the practical problems in industrial engineering, the failure effect sometimes can be omitted or delayed if it has less effect on the system. In detail, the prominent features of the system can be described as follows: 1) if a repair time is sufficiently short (less than some threshold value) that does not affect the system operation, i.e. the pessimistic effect of system failure could be ignored. The system can be considered as operating during this repair time. It is called the system with repair time omission (failure effect omitted). 2) if a repair time is longer than the given threshold value and the failure effect is finally suffered. Then the system can be considered to remain operating from the initial stage of the repair till the end of the repair threshold. It is called the system with delayed failure effect. Based on the above two characteristics, model for the related repairable system is introduced in this paper Two scenarios are discussed where the threshold value is regarded as a constant and non-negative random variable, respectively. Reliability indices such as instantaneous possibilistic availability are formulated for the system with failure effect omitted or delayed.

The conventional reliability analysis of mechanical vibration component only considers the randomness of vibration but rarely for the fuzziness that may exist. It is therefore difficult to be consistent with the engineering practices. Based on the mechanical vibration theory, a novel fuzzy reliability approach by integrating the fuzzy comprehensive evaluation and fuzzy set theory is proposed in this paper. The fuzzy comprehensive evaluation is used to optimize the fuzzy factors of the reliability analysis of vibration component. With the aim of comparing the performance of the proposed approach with the conventional approach, two engineering examples are presented. The results demonstrate that the proposed approach is better than the conventional approach for its capability of covering fuzzy factors in the engineering problems.

Reliability sensitivity analysis is used to find the rate of change in the probability of failure (or reliability) due to the changes in distribution parameters such as the means and standard deviations. Most of the existing reliability sensitivity analysis methods assume that all the probabilities and distribution parameters are precisely known. That is, every statistical parameter involved is perfectly determined. However, there are two types of uncertainties, epistemic and aleatory uncertainties that may not be perfectly determined in engineering practices. In this paper, both epistemic and aleatory uncertainties are considered in reliability sensitivity analysis and modeled using P-boxes. The proposed method is based on Monte Carlo simulation (MCS), weighted regression, interval algorithm and first order reliability method (FORM). We linearize original non-linear limit-state function by MCS rather than by expansion as a first order Taylor series at most probable point (MPP) because the MPP search is an iterative optimization process. Finally, we introduce an optimization model for sensitivity analysis under both aleatory and epistemic uncertainties. Four numerical examples are presented to demonstrate the proposed method.

When there is limited objective reliability data, a new method of developing the possibility distribution by subjective assignment strategy is put forward, which makes use of the concept of interval-valued possibilistic mean value of a L-R fuzzy number and is then illustrated by the lifetime data from flexural fatigue reliability modeling and data analysis. An application of four groups' gear-tooth flexural fatigue lifetime data of the 42CrMo steel specimens under various stress levels and sample sizes is presented to illustrate the inferring procedure of possibility distributions. The verification results of 6 and 8 samples between actual lifetime distributions and experimental ones, in comparison with the function termed mid-rank and three-parameter Weibull (3-PWD), show that the proposed approach is available and valid.

In this paper, the fuzzy assessment information is derived for the eight failure modes and relative importance weights of three risk factors by utilizing the interval number matrix multiplying method. Fuzzy Risk Priority Numbers (FRPNs) are defined as Fuzzy Weighted Geometric Mean (FWGM) of the fuzzy ratings for the three risk factors for prioritization of failure modes, and it can be calculated using α-level sets and benchmark adjustment devised search algorithm rather than liner programming (LP). To facilitate the ranking of failure modes, the FRPNs are derived by virtue of a new centroid defuzzification approach based on α-level sets. The fuzzy FMEA approach mentioned above is then illustrated with an application to the Solar Array Drive Assembly. As is illustrated by the numerical example, the proposed FMEA can well capture FMEA team members' diversity opinions and prioritize failure modes under different types of uncertainties.

Classical probability theory has been widely used in reliability analysis; however, it is hard to handle when the system is lack of adequate and sufficient data. Nowadays, alternative approaches such as possibility theory and fuzzy set theory have also been proposed to analyze vagueness and epistemic uncertainty regarding reliability aspects of complex and large systems. The model presented in this paper is based upon possibility theory and multistate assumption. Convex sublattice is addressed on congruence relation regarding the complete lattice of structure functions. The relations between the equivalence classes on the congruence relation and the set of all structure functions are established. Furthermore, important reliability bounds can be derived under the notion of convex sublattice. Finally, a numerical example is given to illustrate the results.

Classical probability theory has been widely used in reliability analysis; however, it is hard to handle when the system is lack of adequate and sufficient data. Nowadays, alternative approaches such as possibility theory and fuzzy set theory have also been proposed to analyze vagueness and epistemic uncertainty regarding reliability aspects of complex and large systems. The model presented in this paper is based upon possibility theory and multistate assumption. Convex sublattice is addressed on congruence relation regarding the complete lattice of structure functions. The relations between the equivalence classes on the congruence relation and the set of all structure functions are established. Furthermore, important reliability bounds can be derived under the notion of convex sublattice. Finally, a numerical example is given to illustrate the results.

A systematic reliability analysis of n-unit warm standby repairable system with k-repair facility is presented in this paper. Traditional approaches are extended under the following assumptions: (1) the working lifetime, the standby lifetime, and the repair time of failed units are represented as exponential distribution; and (2) the repair of failed units are as good as new after repair. In this paper, a general reliability analysis of an n-unit warm standby repairable system with k-repair facility is presented. Based on previous analysis, the steady-state reliability and the average availability of the system are formulated using the Markov process theory and Laplace transform.

Based on some practical problems in industrial engineering, a species of two-unit parallel repairable system model is introduced in this paper. The prominent feature of the model is described as follows: if a repair time is sufficiently short (less than some threshold value) that does not affect the system operation, i.e. the effect could be ignored, the system can be considered as being operating during this repair time. Two scenarios are discussed where the threshold value is regarded as a constant and non-negative random variable, respectively. Reliability indices such as instantaneous availability are formulated.