Effective fault diagnosis of rotating machinery plays a pretty important role in the enhanced reliability and improved safety of industrial informatics applications. Although traditional intelligent fault diagnosis techniques, such as support vector machine, extreme learning machine, and convolutional neural network, might achieve satisfactory accuracy, a very high price is caused by marking all samples manually. In this article, a novel fault diagnosis method of the rotating machinery is proposed by integrating semisupervised generative adversarial nets with wavelet transform (WT-SSGANs). The proposed WT-SSGANs' method involves two parts. In the first part, WT is adopted to transform 1-D raw vibration signals into 2-D time-frequency images. In the second part, the 2-D time-frequency images are inputted into the built SSGANs' model to realize fault diagnosis with little labeled samples. The advantage of the built model is that the unlabeled samples might be made full use of through an adversarial learning mechanism. Finally, two case studies are implemented to verify the proposed method. The results indicate that it can achieve higher accuracy and use less labeled samples than the other existing methods in the literature. In addition, its performance in stability is pretty good as well. Competitive and promising results are still achieved when working conditions are changed.