RELIABILITY ANALYSIS OF CRANE BRAKE PARAMETERS

The high failure rate of crane brake results from improper choice of brakingtorque. The mathematical model of reliability for the crane brake parameters is introduced. Based onlarge amount of actual data the parameter reliabilities of 5 approx 50 t general overheadtravelling cranes are calculated, the probabilities that the braking torque is less than the statictorque acting on the axle when the load moves down are obtained. Moreover, the ways to improve thereliability of brake parameters are discussed, the most reasonable values of braking safetycoefficient are given.

Combination of structural reliability and interval analysis

In engineering applications, probabilistic reliability theory appears to be presently the most important method, however, in many cases precise probabilistic reliability theory cannot be considered as adequate and credible model of the real state of actual affairs. In this paper, we developed a hybrid of probabilistic and non-probabilistic reliability theory, which describes the structural uncertain parameters as interval variables when statistical data are found insufficient. By using the interval analysis, a new method for calculating the interval of the structural reliability as well as the reliability index is introduced in this paper, and the traditional probabilistic theory is incorporated with the interval analysis. Moreover, the new method preserves the useful part of the traditional probabilistic reliability theory, but removes the restriction of its strict requirement on data acquisition. Example is presented to demonstrate the feasibility and validity of the proposed theory.

An inventory model under development cost-dependent imperfect production and reliability-dependent demand

This paper considers a model that deals with an imperfect production process where both perfect and imperfect quality items are produced.Here,demand depends on selling price and reliability of the product.Each manufacturing company expects to produce perfect quality items.But due to the long-run process,several kinds of problem such as labor,machinery,and technology arise.As a result,the manufacturing system becomes out-of-control state and consequently produces both perfect and imperfect quality items.Perfect items are ready to sell but imperfect items are reworked at a cost to become perfect.Reworking cost,reliability of the product and reliability parameter of the manufacturing system can be improved by introducing the development cost and also by improving the quality of the raw material of the production system.Under such circumstances,a profit function has been developed to find the optimum values of reliability parameter of the manufacturing system,reliability of the product and duration of production such that a manufacturer gets a maximum profit.Finally,the model has been illustrated with some numerical examples exploring the sensitivity analysis with respect to some parameters.

Hybrid Reliability Parameter Selection Method Based on Text Mining, Frequent Pattern Growth Algorithm and Fuzzy Bayesian Network

Reliability parameter selection is very important in the period of equipment project design and demonstration. In this paper, the problem in selecting the reliability parameters and their number is proposed. In order to solve this problem, the thought of text mining is used to extract the feature and curtail feature sets from text data firstly, and frequent pattern tree(FPT) of the text data is constructed to reason frequent item-set between the key factors by frequent patter growth(FPG) algorithm. Then on the basis of fuzzy Bayesian network(FBN)and sample distribution, this paper fuzzifies the key attributes, which forms associated relationship in frequent item-sets and their main parameters, eliminates the subjective influence factors and obtains condition mutual information and maximum weight directed tree among all the attribute variables. Furthermore, the hybrid model is established by reason fuzzy prior probability and contingent probability and concluding parameter learning method. Finally, the example indicates the model is believable and effective.