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Condition-based maintenance is an effective way to reduce operation and maintenance cost of wind turbine. Highly complex and non-stationary operational conditions of wind turbine pose a challenge to conventional condition monitoring technique. Thus, a systematic multi-parameter health condition evaluation framework that considers the dynamic operational conditions is proposed. After characteristic parameter selection and Gaussian mixture model based multi-regime modeling, evidential reasoning is developed to evaluate the health condition of wind turbine. The proposed approach shows good health condition evaluation performance not only on the parameter level but also on the component and system level. Case studies indicate the effectiveness and potential applications of the proposed method for the wind turbine health condition evaluation. V C 2013 AIP Publishing LLC.

Aiming at the problem that it is difficult to select optimum maintenance strategy for power plant equipment, a method based on criticality evaluation and failure mode characteristic analysis is put forward. In the method, the uncertainty and incompletion of criticality evaluation factors are completely considered, qualitative and quantitative evidences are integrated and their acquisition and transformation method is put forward based on constructing criticality evaluation multiple-attribute decision tree. Then a decision tree criticality evaluation model is established, a corresponding evidential reasoning algorithm is deduced, and the equipment in power plant is ranked by criticality. Integrating the results of criticality evaluation and failure mode and effect analysis (FMEA), the decision model of selecting optimum maintenance strategy of power plant equipment is established and applied in a fossil-fired power station. It is shown by the instance that this method is feasible and effective, can select optimum maintenance strategy for power plant equipment.Keywords -Power plant equipment, evidential reasoning, failure mode and effect analysis, maintenance strategy

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Aiming at the problem that the equipment in power plant are complex and difficult to predict their conditions accurately, an artificial neural network for condition prediction on equipment in power plant based on principal component analysis is proposed on the basis of characteristic condition parameter extraction. By fully using the operating parameters, condition monitoring parameters and operation statistic parameters, the conditions of equipment are predicted. It is shown by the instance that the model has higher efficiency and precision than those of the traditional BP neural network. The predicted results can be used as a support next in making scientific maintenance decision. Keywords -equipment in power plant, condition prediction, principal component analysis, artificial neural network, maintenance decision