In engineering practice, small-sample fault diagnosis of mechanical equipment towards
heavy noise interference poses great challenges for the existing Transformer based …

Deep learning-based intelligent diagnostic methods have been widely used in aerospace,
rail transportation, automotive, rail vehicles, and other fields. However, deep neural …

In pursuit of advancing fault diagnosis in electromechanical systems, this research focusses
on vibration analysis through innovative techniques. The study unfolds in a structured …

Cross-domain bearing fault diagnosis models have weaknesses such as large size,
complex calculation and weak anti-noise ability. Hence, a lightweight and robust model via …

In this paper, we propose the gradient-oriented prioritization meta-learning (GOPML)
algorithm, a new approach for few-shot fault diagnosis in industrial systems. The GOPML …

CNC machine is an important equipment for intelligent manufacturing. As a key component
of CNC machines, cutting tools are most easily to be damaged and wasted 12. Unexpected …

The condition monitoring and potential anomaly detection of wind turbines have gained
significant attention because of the benefits of reducing the operating and maintenance …

Transformers are indispensable in the industry sector and society in general, as they play an
important role in power distribution, allowing the delivery of electricity to different loads and …

Faults in the ball bearing are a major cause of failure in rotating machinery where ball
bearings are used. Therefore, there is a growing demand for ball bearing fault diagnosis to …

In response to the problem that nonlinear and non-stationary rolling bearing fault signals are
easily disturbed by noise, which leads to the difficulty of fault feature extraction, to take full …