Imbalance fault diagnosis under long-tailed distribution: Challenges, solutions and prospects
Intelligent fault diagnosis based on deep learning has yielded remarkable progress for its
strong feature representation capability in recent years. Nevertheless, in engineering …
strong feature representation capability in recent years. Nevertheless, in engineering …
A holistic semi-supervised method for imbalanced fault diagnosis of rotational machinery with out-of-distribution samples
Fault diagnosis plays a critical role in ensuring the reliability and safety of industrial systems.
Despite the success of semi-supervised learning in fault diagnosis, challenges persist in …
Despite the success of semi-supervised learning in fault diagnosis, challenges persist in …
A unified out-of-distribution detection framework for trustworthy prognostics and health management in renewable energy systems
With the advances in artificial intelligence, there is a growing expectation of more automatic
and intelligent prognostics and health management (PHM) systems for the real-time …
and intelligent prognostics and health management (PHM) systems for the real-time …
Intelligent condition monitoring of industrial plants: An overview of methodologies and uncertainty management strategies
Condition monitoring plays a significant role in the safety and reliability of modern industrial
systems. Artificial intelligence (AI) approaches are gaining attention from academia and …
systems. Artificial intelligence (AI) approaches are gaining attention from academia and …
Attention-embedded quadratic network (qttention) for effective and interpretable bearing fault diagnosis
Bearing fault diagnosis is of great importance to decrease the damage risk of rotating
machines and further improve economic profits. Recently, machine learning, represented by …
machines and further improve economic profits. Recently, machine learning, represented by …
Reserving embedding space for new fault types: A new continual learning method for bearing fault diagnosis
In complex operating environments, rotating equipment may continually generate new fault
categories, affecting the safety of equipment operation, and the number of collected fault …
categories, affecting the safety of equipment operation, and the number of collected fault …
Self-supervised feature extraction via time–frequency contrast for intelligent fault diagnosis of rotating machinery
Y Liu, W Wen, Y Bai, Q Meng - Measurement, 2023 - Elsevier
Data-driven intelligent fault diagnosis requires a large amount of data. However, collecting
sufficient labeled data from the field is generally difficult because mechanical devices are …
sufficient labeled data from the field is generally difficult because mechanical devices are …
Granularity knowledge-sharing supervised contrastive learning framework for long-tailed fault diagnosis of rotating machinery
S Chang, L Wang, M Shi, J Zhang, L Yang - Knowledge-Based Systems, 2024 - Elsevier
The long-tailed distribution of monitoring data poses challenges for deep learning-based
fault diagnosis (FD). Recent efforts utilizing supervised contrastive learning (SCL) and …
fault diagnosis (FD). Recent efforts utilizing supervised contrastive learning (SCL) and …
An adaptive fault diagnosis framework under class-imbalanced conditions based on contrastive augmented deep reinforcement learning
In practical scenarios, it is difficult to acquire fault data from rotating machinery, resulting in
class-imbalanced problems in the fault diagnosis field. Training a fault diagnosis model …
class-imbalanced problems in the fault diagnosis field. Training a fault diagnosis model …