Recently, frequency transformation (FT) has been increasingly incorporated into deep
learning models to significantly enhance state-of-the-art accuracy and efficiency in time …

As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform
(WT) has shown its great potential in rotary machine fault diagnosis, characterized by …

Convolutional neural network (CNN) is widely used in fault diagnosis of mechanical systems
due to its powerful feature extraction and classification capabilities. However, the CNN is a …

Deep learning (DL) is progressively popular as a viable alternative to traditional signal
processing (SP) based methods for fault diagnosis. However, the lack of explainability …

Time series forecasting is essential for a wide range of real-world applications. Recent
studies have shown the superiority of Transformer in dealing with such problems, especially …

Intelligent fault diagnosis has been increasingly improved with the evolution of deep
learning (DL) approaches. Recently, the emerging graph neural networks (GNNs) have also …

Artificial neural network (ANN) has achieved great success in mechanical fault diagnosis
and has been widely used. However, traditional ANN is still opaque in terms of …

Deep learning (DL) has present great diagnostic results in fault diagnosis field. However, the
poor interpretability and noise robustness of DL-based methods are still the main factors …

In mechanical anomaly detection, algorithms with higher accuracy, such as those based on
artificial neural networks, are frequently constructed as black boxes, resulting in opaque …

Extensive research has been conducted on fault diagnosis of planetary gearboxes using
vibration signals and deep learning (DL) approaches. However, DL-based methods are …