The co-frequency vibration fault is one of the common faults in the operation of rotating equipment, and realizing the real-time diagnosis of the co-frequency vibration fault is of great significance for monitoring the health state and carrying out vibration suppression of the equipment. In engineering scenarios, co-frequency vibration faults are highlighted by rotational frequency and difficult to identify, and existing intelligent methods require more hardware conditions and exclusive time-consuming. Therefore, lightweight-Convolutional Neural Networks (LW-CNN) algorithm is proposed in this paper to achieve real-time fault diagnosis purpose. For the sliding window data augmentation method, the important parameters are discussed and verified by simulated and experimental signals. Based on LW-CNN and data augmentation, the real-time intelligent diagnosis of co-frequency is realized. Moreover, a real-time detection method of fault diagnosis algorithm is proposed for the process from data acquisition to fault diagnosis. It is verified by experiments that the LW-CNN and sliding window method are used with high accuracy and real-time performance.