Transformer Fault Analysis Based on Bayesian Networks and Importance Measures

Complex environment stresses bring many uncertainties to transformer fault. The Bayesian network(BN) can represent prior knowledge in the form of probability which makes it an effective tool to deal with the uncertain problems. This paper established a BN model for the transformer fault diagnosis with practical operation dataset and expert knowledge. Then importance measures are introduced to indentify the key attributes which affect the results of transformer diagnosis most. Moreover, a strategy was proposed to reduce the number of attribute in transformer fault detection and the resource cost was saved. At last, a diagnosis case of practical transformer was implemented to verify the effectiveness of this method.     

Reliability Analysis of Systems with Common Cause Failure Based on Stress-Strength Interference Model

Many mechanical systems have the characteristics of multiple failure modes and complex failure mechanisms. On the basis of stress-strength interference (SSI) model, this paper takes the mechanical system with common cause failure (CCF) as the research object. The relationship between the stress distribution and the strength distribution is studied, and the failures of components are independent of each other under the deterministic stress. Then, the concept of conditional reliability is introduced to build the system reliability models under the action of one-stress and multi-stress for both series and parallel systems. Finally, the corresponding properties of the proposed methods are discussed to show their advantages.