A vast amount of operating data, control data and log data are generated in the monitoring process of a gas turbine. This massive amount of data includes not only spatial distance features, but also related features. Currently, the analysis of data-related features based on data-driven diagnosis methods is challenging. In response to this limitation, a gas turbine fault diagnosis method based on network community detection is proposed in this paper. First, a gas turbine topology network model is established based on complex network theory. In the network model, gas turbine sampled-data are defined as the nodes; the similarity degree between the sampled-data is defined as the edge of the network; and the reciprocal function is selected as the similarity criterion function. Second, to reflect the community characteristics of the network, the topology network is converted to a training network by a threshold criterion that is designed based on the variation of the average path length and the average clustering coefficient. This method can establish a training network without prior knowledge of the number of clusters. Third, a fault detection algorithm is proposed based on community modularity, and fault reasoning is presented by calculating the probability based on the change in the community modularity which demonstrates the occurrence possibility of multiple faults. The effectiveness of the algorithm is verified by a three-shaft gas turbine fault simulation data-set. The results indicate that the proposed algorithm can be used to identify known faults, unknown faults and the graphical representation of a network can reflect the spatial distance and relationship among sampled-data.
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