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Accurately diagnosing power transformer faults is critical to improving the operational reliability of power systems. Although some researchers have made great efforts to improve the accuracy of transformer fault diagnosis, accurate diagnosis of multiple faults is still a difficult problem. In order to improve the accuracy of transformer multiple faults diagnosis, a multiple fault diagnosis method based on interval fuzzy probability is proposed. Different from the previous methods which provide single-value probability, this method use probability interval to represent the occurrence degree of various possible faults, which can objectively predict the potential faults that occurring in a transformer and provide a more reasonable explanation for the diagnosis results. In the proposed method, the interval fuzzy set is used to describe the evaluation of state variables and the interval fuzzy probability model based on interval weighted average is applied to integrate the fault information. The representative matrix of fault types based on fuzzy preference relationship is established to estimate the relative importance of each gas in the dissolved gases. The proposed method can provide the probability of probable faults in transformer, help engineers quickly determine the type and location of faults, and improve the accuracy of diagnosis and maintenance efficiency of transformer. The effectiveness of the method is verified with case studies.

Accurately assessing the health condition of the power transformer is critical to improving the operational reliability of power systems. Although great efforts to improve the accuracy of transformer fault diagnosis, accurate detection of multiple latent faults is still a difficult problem. Therefore, a multifactorial condition assessment method based on fuzzy sets and factor space is proposed. Different from the previous diagnosis methods that only provide a single-valued result, the proposed method can display the influence degree of various variables on the condition assessment result, which indirectly indicates the cause and location of faults that occur in a transformer. In the proposed method, the type-2 fuzzy set is used to describe the evaluation of various test values and the multifactorial condition assessment model based on factor space is applied to integrate the condition information. A modified analytic hierarchy process method is developed to estimate the relative importance of attributes. This method can distinctly indicate multiple probable latent faults of the transformer and help engineers quickly determine the cause and location of faults, which is helpful to improve the accuracy of diagnosis and the efficiency of transformer maintenance. The effectiveness of this method is verified with case studies.

In order to reduce the number of defective products caused by the unreasonable baking time during the tobacco production process, this paper proposes a method for establishing a multi-model reasoning tobacco baking quality prediction model. Conduct data mining and analysis on the data of various indicators of the original tobacco, and screen out the data that have an impact on the quality of tobacco baking. In order to reduce the complexity of the model and eliminate the influence between different dimensions, the data are carried out and standardized processing. Next, the normalized data is explored for the multi-input and multi-output mapping relationship. Finally, a mapping matrix is given for the multi-input and multi-output mapping relationship so as to establish a tobacco baking quality prediction model. The test results show that the predicted value of this model is basically the actual value, and the prediction accuracy rate is more than 90%. It has a high prediction accuracy rate. The cured tobacco leaves are basically the same as the actual cured yellow expected value. This model provides a practical guide method for tobacco baking, which has certain practical value in actual tobacco baking.

Teaching performance evaluation plays an important role in the management of education institutions. In this process, the relevant factors may be complicated and various, it is difficult to dealt with the factors once for all, and a decomposed smaller part sometimes could be easily handled. Based on this consideration, a hierarchical multifactorial evaluation model based on type-2 fuzzy sets is formulated and applied to evaluate teaching performance. In the constructed model, in order to obtain a more rational evaluation result, the factor weights are replaced by triangular fuzzy numbers, and the defuzzification results are represented by the triangular incentre points. To estimate the factor and sub-factor weights in the multifactorial evaluation, a new method of fuzzy analytic hierarchy process (AHP) based on triangular incentre point is developed, and the effectiveness of the hierarchical multifactorial evaluation model is verified with an specific instance.