Although deep learning-based intelligent fault diagnosis methods have garnered significant research interest, their impressive performance relies on the assumption that the training and test data share the same distribution. This constraint limits their effectiveness in scenarios where these distributions differ. Unsupervised domain adaptation techniques for fault diagnosis address this issue; however, most existing approaches focus solely on data from a single source domain, which overlooks the potential for leveraging multiple source domains to enhance diagnostic accuracy. In this paper, we propose a multi-source domain adaptation fault diagnosis method using enhanced multi-kernel maximum mean discrepancy to effectively extract features from multiple source domains for accurate target domain fault diagnosis. Extensive experiments on two fault diagnosis datasets demonstrate the superior performance of the proposed method.

Class-incremental fault diagnosis requires a model to adapt to new fault classes while retaining previous knowledge. However, limited research exists for imbalanced and long-tailed data. Extracting discriminative features from few-shot fault data is challenging, and adding new fault classes often demands costly model retraining. Moreover, incremental training of existing methods risks catastrophic forgetting, and severe class imbalance can bias the model's decisions toward normal classes. To tackle these issues, we introduce a supervised contrastive knowledge distillation for class incremental fault diagnosis (SCLIFD) framework proposing supervised contrastive knowledge distillation for improved representation learning capability and less forgetting, a novel prioritized exemplar selection method for sample replay to alleviate catastrophic forgetting, and the random forest classifier to address the class imbalance. Extensive experimentation on simulated and real-world industrial datasets across various imbalance ratios demonstrates the superiority of SCLIFD over existing approaches.

The gearbox in a wind turbine is an important component, whereas it often operates in harsh environments, resulting in a relatively high failure rate. Fault diagnosis of wind turbine gearboxes by means of vibration signals is a feasible solution, but in practice, the vibration signals collected by sensors are often accompanied by noise, which affects the accuracy of fault diagnosis. In this paper, a noise-robust convolutional neural network (NRCNN) is utilized for fault diagnosis of wind turbine gearboxes with noisy vibration signals. The structure of multilayer convolutional layers and fully connected layers provides the NRCNN with excellent feature extraction capability, while the introduction of the dropout layer enables the NRCNN to have better generalization capability. Comprehensive experiments on two gearbox vibration datasets demonstrate that the NRCNN could accurately perform the diagnosis of vibration signals with noise.

Intelligent fault diagnosis is essential to guarantee the safe operation of industrial processes. And an important issue is how to develop a method to tackle the dilemma where we can only collect limited fault samples. In this paper, we propose a two-stage method based on supervised contrastive learning for imbalanced fault diagnosis tasks. We utilize the supervised contrastive learning technique as it has shown a powerful representation learning ability in previous works. The computational experiments on the Tennessee Eastman dataset show that our proposed two-stage method can achieve improved performance when compared to existing methods.

Collaborative industrial robot is becoming more and more important in the manufacturing industry. Thanks for a variety of high-precision sensors integrated by the Cyber-Physical System, the collaborative industrial robot can model the working environment and know its own state in real time. Because of this, CPS enables the robot to plan a feasible path in a virtual simulation environment. In this paper, a two-dimensional working space and a three-dimensional working space is constructed and set as a virtual environment model constructed by CPS. Q-learning algorithm is used to plan a path in the working space. A feasible path is found by adjusting the number of iteration times of the algorithm. Further more, the learning rate α of the Q-learning algorithm is also adjusted and the results demonstrate that the increase of α will accelerate the convergence speed of the algorithm within the set range.