###### Abstract
\begin{table}
\begin{tabular}{c l l l} \hline \hline  & **Reference** & **Method** & **Objects** \\ \hline \multirow{4}{*}{Supervised} & [5] & One-dimensional convolutional neural network(1-DCNN) & planetary gearbox \\  & [6] & Deep convolution neural network (DCNN) with wavelet image & rolling bearing \\  & [7] & Multiple wavelet regularized deep residual network (MWR-DRN) & planetary gearbox \\  & [8] & Recurrent neural network (RNN) based on gated recurrent units & bearing and pump \\  & [9] & Local connection network (LCN) constructed by normalized sparse autoencoder (NSAE) & gearbox and bearing \\ \multirow{2}{*}{Unsupervised} & [10] & Deep sparse autoencoder (DSAE) & gear pitting fault \\  & [11] & Convolutional deep belief network (CDBN) & rolling bearing \\  & [12] & Enhanced deep convolutional generative adversarial network (DCGAN) & gearbox and bearing \\  & [13] & Wavelet packet transform combined with generative adversarial networks (GAN) & robotic manipulator \\  & [14] & Categorical adversarial autoencoder (CatAAE) & gearbox and bearing \\ \hline \multirow{2}{*}{Semi-supervised} & [15] & Consistency regularization based semi-supervised learning & rolling bearing \\  & [16] & semi-supervised deep sparse autoencoder (SSDSAE) with local and nonlocal information & bearing and industrial rotor \\  & [17] & Graph-based rebalance semi-supervised learning (GRSSL) with visibility graph feature (VGF) & rolling bearing \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the recent fault diagnosis methods based on deep learning. lator. [PERSON] et al. [14] combined categorical GAN and adversarial autoencoder to perform unsupervised clustering of rolling bearings. To make more effective use of both labeled and unlabeled data, semi-supervised learning methods have attracted the attention of researchers. [PERSON] et al. [15] proposed a semi-supervised approach based on consistency regularization principle that makes the model less sensitive to the extra perturbation imposed on the inputs. [PERSON] et al. [16] embedded the labeled and unlabeled data into local and nonlocal regularization terms to realize a semi-supervised deep sparse autoencoder. [PERSON] et al. [17] proposed a graph-based rebalance semi-supervised learning method, and they mainly focus on variable conditions and imbalance unlabeled data. This paper proposes a new semi-supervised learning method focusing on the problem of insufficient labeled data, namely Bidirectional Wasserstein Generative Adversarial Network with Gradient Penalty (BiWGAN-GP). Compare to the existing work of semi-supervised fault diagnosis methods, the proposed method makes full use of the representation learning ability of GAN. It not only can extract effective features from the unlabeled data but also can generate realistic signals for data augmentation, which makes it suitable for fault diagnosis tasks with limited labeled data. The main contributions of this paper can be summarized as follows:  
1. By adding an encoder on the standard GAN architecture, for the first time, bidirectional GAN is introduced in the machinery fault diagnosis field, which provides a new way for automatic feature extraction from unlabeled data. 2. Wasserstein distance with gradient penalty is used in the unsupervised training procedure, which improves the model's stability and usability. Experimental results show the effectiveness and necessity of this improvement. 3. With limited labeled data to fine-tune the model's parameters, the proposed method can achieve a satisfactory diagnosis accuracy, which can reduce the cost of labeling data. The rest of this article is organized as follows. Section 2 gives the theoretical background about GAN and bidirectional GAN, then the specific methods for improving the GAN are described. Section 3 present the detailed system framework and the training procedure of the proposed method. Experiments are conducted in Section 4 to verify the effectiveness of the proposed method for data generation and fault diagnosis.