In an effort to achieve an optimal availability time of induction motors via fault probabilities reduction and improved prediction or diagnostic tools responsiveness, a conditional probabilistic approach was used. So, a Bayesian network (BN) has been developed in this paper. The objective will be to prioritize predictive and corrective maintenance actions based on the definition of the most probable fault elements and to see how they serve as a foundation for the decision framework. We have explored the causes of faults for an induction motor. The influence of different power ranges and the criticality of the electric induction motor are also discussed. With regard to the problem of induction motor faults monitoring and diagnostics, each technique developed in the literature concerns one or two faults. The model developed, through its unique structure, is valid for all faults and all situations. Application of the proposed approach to some machines shows promising results on the practical side. The model developed uses factual information (causes and effects) that is easy to identify, since it is best known to the operator. After that comes an investigation into the causal links and the definition of the a priori probabilities. The presented application of Bayesian networks is the first of its kind to predict faults of induction motors. Following the results of the inference obtained, prioritizations of the actions can be carried out.

The main aim of this paper is to analyse the effect of Common Agricultural Policy (CAP) subsidies on technical efficiency of Polish dairy farms. We have distinguished several types of subsidies and provided an analysis to find out which types are most likely to engender systematic differences in technical efficiency. A balanced panel of microeconomic data on Polish dairy farms over an eight-year period (between 2004 and 2011), taken from the Farm Accountancy Data Network (FADN), is used. The translog production function is estimated by employing the Bayesian approach. The empirical results show that the elasticity of production with respect to livestock is the highest, whereas with respect to feed is the lowest. The mean technical efficiency in the covered period is 83%. The research reveals the negative effect of subsidies on technical efficiency.

A Bayesian stochastic volatility model with a leverage effect, normal errors and jump component with the double exponential distribution of a jump value is proposed. The ready to use Gibbs sampler is presented, which enables one to conduct statistical inference. In the empirical study, the SVLEDEJ model is applied to model logarithmic growth rates of one month forward gas prices. The results reveal an important role of both jump and stochastic volatility components.