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• A novel model based on GAT for multi-fault detection and identification of sensors in nuclear power plant. • The attention score matrix (α matrix) of GAT is used to fully learn the correlation between sensors and is used for reconstruction. • A strategy based on fault decoupling is proposed for multi-fault identification of sensors. The accuracy of sensor's measurements is critical for the normal operation of nuclear power plants. However, the harsh operating environment of nuclear power plants increases the probability of sensor failure. Therefore, it is necessary to detect and identify the fault of sensors in time. This paper proposes a fault detection model based on graph attention network, which is called ALPHA-GAT, to solve multiple fault detection and identification of sensors in the primary coolant loop of a nuclear power plant. ALPHA-GAT first uses GAT to obtain the correlation matrices of sensors, and the correlation matrices are fed into an adversarial autoencoder for reconstruction. The node features of the GAT are parallelly fed into a long short-term memory network to predict the ground truth of sensors. Subsequently, the anomaly score which is used for fault detection and identification, is calculated based on reconstruction error and prediction error. A strategy based on fault decoupling to identify multi-sensor faults has also been proposed, which replaces the data of fault sensor with the prediction of the model. This strategy effectively alleviates the negative impact of fault data on subsequent diagnosis. Finally, the data from a simulation model of a nuclear power plant is used to verify the efficiency of the proposed model. The results of experiments indicate that ALPHA-GAT effectively improves the accuracy and timeliness of multi-sensor fault detection and identification. [ABSTRACT FROM AUTHOR]