In this article, fault diagnosis is of great significance for system health maintenance. For real applications, diagnosis accuracy suffers from unbalanced data patterns, where normal data are usually abundant than anomaly ones, leading to tremendous diagnosis obstacles. Therefore, it is challenging to use only normal data for fault diagnosis under this imbalanced condition. In addition, a single fault diagnosis model can only conduct one fault diagnosis task in most of cases. Accordingly, a one-class generative adversarial detection (OCGAD) framework based on semisupervised learning is proposed to learn one-class latent knowledge for dealing with multiple semisupervised fault diagnosis tasks, i.e., fault detection using only normal knowledge learning, novelty detection from unknown conditional data, and fault classification with unlabeled data. A bi-directional generative adversarial network (Bi-GAN) is first trained with only normal data. A one-class support vector machine is then established using features exacted by Bi-GAN from signals acquired from an attitude sensor for multifunctional fault detection. The presented OCGAD model is validated using an industrial robot with experiments of three fault detection tasks. The results demonstrate that the present model has good performance for dealing with multiple semisupervised diagnosis problems.

The gearbox will directly affect the safety and reliability of the wind turbine, whose failure leads to low processing accuracy and certain economic losses. To address this issue, a deep enhanced fusion network (DEFN) is proposed for the fault diagnosis of the wind turbine gearbox with the experimental vibration data. In the proposed DEFN, three sparse autoencoders are firstly applied to extract deep features of three-axial vibration signals respectively. Secondly, a feature enhancement mapping is developed to minimize the intra-class distance of the deep features in the three-axial vibration respectively. Finally, the fused three-axis features are put into an echo state network for fault classification. The results of the experiment carried out in a wind turbine show that the proposed DEFN has a good fault diagnosis accuracy comparing with other peer models.

As vital renewable energy devices, wind turbines suffer from gearbox failures due to harsh speed increasing operations. Therefore, gearbox fault diagnosis is crucial for wind turbine maintenance with reducing economic costs. However, obtaining faulty data is rather challenging, especially at the early fault stage. For this reason, a sparse isolation encoding forest (SIEF) is proposed aiming at both anomaly detection and novel fault discrimination for wind turbine gearboxes. In the present SIEF method, a sparse autoencoder (SAE) is first trained with only normal data to obtain an optimized and robust weight structure. Newly acquired data corresponding to faulty or healthy conditions are sent to this encoder for feature extraction by encoding to its low-dimensional space. All the data in low-dimensional space are fed to an isolation forest (IF) for anomaly detection and novel fault discrimination. In the addressed SIEF approach, only normal data are required to train the model for fault detection and further discrimination. It is consistent with the actual operations of the wind turbines, with much less dependence on the fault data for model training. The proposed method was evaluated by fault diagnosis tests on wind turbine gearboxes. Results show good performances of the proposal compared with peers.

In real-world industrial scenarios, fault diagnosis often relies on a significant volume of normal data to detect faults with a few coming samples. The limited sample nature of newly introduced faults can pose challenges for deep learning-based fault diagnosis models. To address this issue, a novel framework named incrementally generative adversarial diagnostics was developed by using few-shot enabled one-class learning (FSEOCL) for effective anomaly detection and fault classification of the new-coming few samples. In the addressed method, a bi-directional generative adversarial network is first trained using only normal data to acquire an encoder for latent representation from time-series data. A few samples of each fault condition in latent space can be then identified and classified correctly through FSEOCL. The effectiveness of the proposed framework is demonstrated through fault diagnosis experiments conducted on both a benchmark bearing and an industrial robot. The results underscore the adaptability of this framework to industrial fault diagnosis scenarios, highlighting its ability to achieve accurate anomaly detection and fault classification compared to state-of-the-art peer methods.

Generative adversarial networks (GANs) have shown their potential for data generation. However, this type of generative model often suffers from oscillating training processes and mode collapse, among other issues. To mitigate these, this work proposes a generalization of both mean square error (mse) GAN and Wasserstein GAN (WGAN) with gradient penalty, referred to as VGAN. Within the framework of conditional WGAN with gradient penalty, VGAN resorts to the Vapnik V-matrix-based criterion that generalizes mse. Also, a novel early stopping-like strategy is proposed that keeps track during training of the most suitable model. A comprehensive set of experiments on a fault-diagnosis task for an industrial robot where the generative model is used as a data augmentation tool for dealing with imbalance datasets is presented. The statistical analysis of the results shows that the proposed model outperforms nine other models, including vanilla GAN, conditional WGAN with and without conventional regularization, and synthetic minority oversampling technique, a classic data augmentation technique.