Rotating machineries are core components of mechanical equipment， and faults in such machineries can cause significant losses； thus， the real-time monitoring and diagnosis of rotating machinery are highly necessary. Therefore， we study the monitoring and diagnosis of rotating machinery and propose an infrared intelligent diagnosis method for rotating machinery based on deep learning. In this study， we develop a rotating machinery fault simulation test-bed， with three preset motor states： normal， overloaded， and short-circuited and three rotor system states： normal， imbalanced， and misaligned. The surface temperature of the rotating machinery is recorded using an infrared thermal imager， followed by infrared imaging and enhancement. A target detection algorithm is used to identify and locate the rotating machinery parts in the image， and an infrared image of the part is reconstructed according to the detection results. Finally， an image classification algorithm is used to classify the two types of components， thus achieving intelligent fault diagnosis. The experimental results reveal that the accuracy of the intelligent fault diagnosis for rotating machinery is 90.06 %， and a good intelligent diagnosis effect can be realized. In addition， after the expansion of component and fault types， the method and process may be used as a reference for rotating machinery fault diagnosis and even mechanical fault diagnosis.