A structural damage identification method using single sensor data based on Gramian angular field （GAF） and convolutional neural networks （CNN） is proposed， which can reduce the number of sensors needed for damage identification， and the cost of the monitoring system and the processing cost of massive data. In this method， the original vibration signal is converted into 2-dimensional images of the Gramian angular summation field （GASF） and the Gramian angular difference field （GADF） based on GAF theory respectively. The image features are extracted and input to the optimized 2-dimensional CNN mode based on LeNet-5 Structure， and the ideal damage identification result is finally obtained. The international association for bridge maintenance and safety bridge health monitoring benchmark structure and a steel frame structure are used to verify the effectiveness of the proposed method. Also the influences of the algorithm for vibration signal transformation into two-dimensional image， convolutional neural network model parameters， sensor location and measurement noise on the damage identification results are analyzed. The results show that the proposed algorithm only needs single sensor data to achieve the purpose of damage identification， and the accuracy of damage identification is 100% in both numerical simulation and model test. The test time of a single sample is about 8.5 ms， which meets the requirements of online damage identification for structural health monitoring， and it is less affected by sensor location and noise level. Secondly， compared with GASF graph， GADF graph has higher convergence efficiency， smaller oscillation amplitude and less influence by local optimal value. Under the condition of the same sample size， it is easier to train and generate the optimal 2-dimensional CNN model.