Machinery fault diagnosis is pretty vital in modern manufacturing industry since an early detection can avoid some dangerous situations. Among various diagnosis methods, data-driven approaches are gaining popularity with the widespread development of data analysis techniques. In this research, an effective deep learning method known as stacked autoencoders (SAEs) is proposed to solve gearbox fault diagnosis. The proposed method can directly extract salient features from frequency-domain signals and eliminate the exhausted use of handcrafted features. Furthermore, to reduce the overfitting problem in training process and improve the performance for small training set, dropout technique and ReLU activation function are introduced into SAEs. Two gearbox datasets are employed to conform the effectiveness of the proposed method; the result indicates that the proposed method can not only achieve significant improvement but also is superior to the raw SAEs and some other traditional methods.

Mechanical fault datasets are always highly imbalanced with abundant common mechanical fault samples but a paucity of samples from rare fault conditions. To overcome this weakness, the simulation of rare fault signals is proposed in this paper. Specifically, frequency spectra are employed as model signals, then Wasserstein generative adversarial network (WGAN) is implemented to generate simulated signals based on a labeled dataset. Finally, the real and artificial signals are combined to train stacked autoencoders (SAE) to detect mechanical health conditions. To validate the effectiveness of the proposed WGAN-SAE method, two specially designed experiments are carried out and some traditional methods are adopted for comparison. The diagnosis results show that the proposed method can deal with imbalanced fault classification problem much more effectively. The improved performance is mainly due to the artificial fault signals generated from the WGAN to balance the dataset, where the signals that are lacking in training dataset are effectively augmented. Furthermore, the learned features in each layer of the generator network are also analyzed via visualization, which may help us understand the working process of the WGAN.

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As a useful data augmentation technique, generative adversarial networks have been successfully applied in fault diagnosis field. But traditional generative adversarial networks can only generate one category fault signals in one time, which is time-consuming and costly. To overcome this weakness, we develop a novel fault diagnosis method which combines conditional generative adversarial networks and stacked autoencoders, and both of them are built by stacking one-dimensional full connection layers. First, conditional generative adversarial networks is used to generate artificial samples based on the frequency samples, and category labels are adopted as the conditional information to simultaneously generate different category signals. Meanwhile, spectrum normalization is added to the discriminator of conditional generative adversarial networks to enhance the model training. Then, the augmented training samples are transferred to stacked autoencoders for feature extraction and fault classification. Finally, two datasets of bearing and gearbox are employed to investigate the effectiveness of the proposed conditional generative adversarial network–stacked autoencoder method.

The speed of rotating parts is often affected by different working conditions in mechanical equipment, which results in more complications in the feature mapping relationship. However, existing methods that address the large fluctuation problem in rotational speed have been formulated merely to improve test accuracy and do not consider the effects of irregular fluctuation frequency on the fault samples located at the class boundary. Thus, to distinguish the health conditions under frequent or irregular fluctuation speeds, this paper explores an enhanced sparse filtering (SF) algorithm based on maximum classifier discrepancy to diagnose the fault conditions caused by speed fluctuation. It considers the superiority of the task-specific decision boundary and adversarial training for the fault diagnosis network. Unlike traditional SF methods, the proposed framework introduces the Wasserstein distance to reduce the domain discrepancy between the source domain and the target domain and then uses the probability output discrepancy of the classifier to locate the fuzzy fault samples on the class boundary. This paper conducts theoretical analysis and experimental comparison and verifies the performance advantages of the framework through bearing and gear experiments under large speed fluctuation conditions. The proposed model also shows an excellent performance even when the speed fluctuates frequently.