Multi-fault Diagnosis of Wind Turbine Based on Imprecise Probability

Abstract

In this paper, an imprecise probability multi-fault diagnosis approach was proposed based on imprecise probability, which could simultaneously diagnose multi-fault events of the wind turbine. The approach excavated the dependence relationships between fault events and variables using the greedy search algorithm and constructed a credal network structure to express the relationships in an abstract way. The expanded imprecise Dirichlet model is used to obtain the imprecise conditional probability table at each evidence node in the network, thus to realize the parameter estimation of the credal network It was shown that based on the constructed model, features of different targets can adaptively be extracted from the same signal, and then these features are used to perform multi-fault diagnosis. The test results showed that the proposed approach realizes the correct diagnosis of faults under multiple working conditions.