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The general approach to percussion-based monitoring of bolt preload is to train a classifier model to map the preloads and acoustic characteristics of percussion signals. However, the traditional percussion-based approach can only classify the preloads of bolts into certain ranges, and cannot accurately predict the exact bolt preload. This paper proposes an approach to estimate bolt preload using a convolutional neural network (CNN). The frequency contents of the percussion signals are analysed with the Fast Fourier Transform (FFT), and the magnitudes of signals in different frequencies ranges are reconstructed into a matrix, which can be treated as an image. Each image is labelled with the corresponding bolt preload. Then the labelled images are used to train the CNN model, and the trained model is used to detect the actual preload of a selected bolt. The proposed model was experimentally verified using a bolted steel plate. The results show that the proposed model can accurately predict the preload values outside the range of training samples with an accuracy over 95%. Meanwhile, compared with the predicted effect of the traditional regression models, the predicted validity of the proposed model is further verified. [ABSTRACT FROM AUTHOR]