To identify the running fault of motors quickly and effectively from the temperature data collected by thermal imagers, this study combines dropout, nonlinear wavelet transform coefficient enhancement (NLWTCE), and convolutional neural network (CNN) algorithm to identify the motor image. Firstly, the image dataset of the motor is established according to the data collected by the thermal imager and the data image is enhanced by nonlinear wavelet transform (NLWT). Then an improved CNN (ICNN) model is built to identify the image with the extracted features as the final recognition features. Finally, compared with the normal motor images, the faulty motor images are effectively and accurately identified. The experimental results show that the ICNN model not only has a high recognition accuracy but also further simplifies the complex extraction of image features. The validity and reasonableness of the method are verified, and the method is suitable for engineering application.